For electro-discharge machining, only in the optimum state could the highest material removal rate be realized. In practical machining process, the timely elevation of the tool electrode is needed to eliminate chippin...For electro-discharge machining, only in the optimum state could the highest material removal rate be realized. In practical machining process, the timely elevation of the tool electrode is needed to eliminate chipping, which ordinarily occupies quite a lot of time. Therefore, besides the control of the machining parameters, the control of the optimum discharge gap and the conversion of different machining states is also needed. In this paper, the adaptive fuzzy control system of servomechanism for EDM combined with ultrasonic vibration is studied, the servomechanism of which is composed of the stepping motor comprising variable steps and the inductive synchronizer. The fuzzy control technology is used to realize the control of the frequency and the step of the servomechanism. The adaptive fuzzy controller has three inputs and two outputs, which can well meet the actual control requirements. The constitution of the fuzzy control regulation for the step frequency is the key to the design of the whole fuzzy control system of the servomechanism. The step frequency is mainly determined by the position error and the change rate of the position error. When the value of the position error is high or medium, the controlled parameters are selected to eliminate the error; when the position error is lower, the controlled parameters are selected to avoid the over-orientation and thus keep the stability of the system. According to these, a fuzzy control table is established in advanced, which is used to express the relations between the fuzzy input parameters and the fuzzy output parameters. The input parameters and the output parameters are all expressed by the level-values in fuzzy field. Therefore, the output parameters used for control can be obtained for the fuzzy control table according to the detected actual input parameters, by which the EDM combined with ultrasonic vibration is improved and the machining efficiency is increased. In addition, a stimulation program is designed by means of Microsoft Visual Basic展开更多
The advantage of fuzzy controllers in working with inaccurate and nonlinear inputs is that there is no need for an accurate mathematical model and fast convergence and minimal fluctuations in the maximum power point d...The advantage of fuzzy controllers in working with inaccurate and nonlinear inputs is that there is no need for an accurate mathematical model and fast convergence and minimal fluctuations in the maximum power point detector.The capability of online fuzzy tracking systems is maximum power,resistance to radiation and temperature changes,and no need for external sensors to measure radiation intensity and temperature.However,the most important issue is the constant changes in the amount of sunlight that cause the maximum power point to be constantly changing.The controller used in the maximum power point tracking(MPPT)circuit must be able to adapt to the new radiation conditions.Therefore,in this paper,to more accurately track the maximumpower point of the solar system and receive more electrical power at its output,an adaptive fuzzy control was proposed,the parameters of which are optimized by the whale algorithm.The studies have repeated under different irradiation conditions and the proposed controller performance has been compared with perturb and observe algorithm(P&O)method,which is a practical and high-performance method.To evaluate the performance of the proposed algorithm,the particle swarm algorithm optimized the adaptive fuzzy controller.The simulation results show that the adaptive fuzzy control system performs better than the P&O tracking system.Higher accuracy and consequently more production power at the output of the solar panel is one of the salient features of the proposed control method,which distinguishes it from other methods.On the other hand,the adaptive fuzzy controller optimized by the whale algorithm has been able to perform relatively better than the controller designed by the particle swarm algorithm,which confirms the higher accuracy of the proposed algorithm.展开更多
Multi-joint manipulator systems are subject to nonlinear influences such as frictional characteristics,random disturbances and load variations.To account for uncertain disturbances in the operation of manipulators,we ...Multi-joint manipulator systems are subject to nonlinear influences such as frictional characteristics,random disturbances and load variations.To account for uncertain disturbances in the operation of manipulators,we propose an adaptive manipulator control method based on a multi-joint fuzzy system,in which the upper bound information of the fuzzy system is constant and the state variables of the manipulator control system are measurable.The control algorithm of the system is a MIMO(multi-input-multi-output)fuzzy system that can approximate system error by using a robust adaptive control law to eliminate the shadow caused by approximation error.It can ensure the stability of complex manipulator control systems and reduce the number of fuzzy rules required.Comparison of experimental and simulation data shows that the controller designed using this algorithm has highly-precise trajectory-tracking control and can control robotic systems with complex characteristics of non-linearity,coupling and uncertainty.Therefore,the proposed algorithm has good practical application prospects and promotes the development of complex control systems.展开更多
The paper investigates the practical prescribed-time fuzzy tracking control problem for a category of nonlinear system subject to time-varying actuator faults.The presence of unknown nonlinear dynamics and actuator fa...The paper investigates the practical prescribed-time fuzzy tracking control problem for a category of nonlinear system subject to time-varying actuator faults.The presence of unknown nonlinear dynamics and actuator faults makes achieving tracking control within a prescribed-time challenging.To tackle this issue,we propose a novel practical prescribed-time fuzzy tracking control strategy,which is independent of the initial state of the system and does not rely on precise modeling of the system and actuators.We apply the approximation capabilities of fuzzy logic systems to handle the unknown nonlinear functions and unidentified actuator faults in the system.The piecewise controller and adaptive law constructed based on piecewise prescribed time-varying function and backstepping technique method establish the theoretical framework of practical prescribed-time tracking control,and extend the range of prescribed-time tracking control to infinity.Regardless of the initial conditions,the proposed control strategy can guarantee that all signals remain uniformly bounded within the practical prescribed time in the presence of unknown nonlinear item and time-varying actuator faults.Simulation example is presented to demonstrate the effectiveness of the proposed control strategy.展开更多
In this paper, we propose an adaptive fuzzy dynamic surface control(DSC) scheme for single-link flexible-joint robotic systems with input saturation. A smooth function is utilized with the mean-value theorem to deal w...In this paper, we propose an adaptive fuzzy dynamic surface control(DSC) scheme for single-link flexible-joint robotic systems with input saturation. A smooth function is utilized with the mean-value theorem to deal with the difficulties associated with input saturation. An adaptive DSC design with an auxiliary first-order filter is used to solve the "explosion of complexity"problem. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood around zero. The main advantage of the proposed method is that only one adaptation parameter needs to be updated,which reduces the computational burden significantly. Simulation results demonstrate the feasibility of the proposed scheme and the comparison results show that the improved DSC method can reduce the computational burden by almost two thirds in comparison with the standard DSC method.展开更多
This paper presents an improved observer-based indirect adaptive fuzzy control scheme for multiinput-multioutput (MIMO) nonlinear time-delay systems.The control scheme synthesizes adaptive fuzzy control with adaptive ...This paper presents an improved observer-based indirect adaptive fuzzy control scheme for multiinput-multioutput (MIMO) nonlinear time-delay systems.The control scheme synthesizes adaptive fuzzy control with adaptive fuzzy identification.An observer is designed to observe the system state,and an identifier is developed to identify the unknown parts of the system.The update laws for parameters utilize two types of errors in the adaptive time-delay fuzzy logic systems,the observation error and the identification error.Performance analysis proves the superiority of the update laws in terms of faster and improved tracking and parameter convergence.Simulation results of two-link manipulator demonstrate the effectiveness of the improved control scheme.展开更多
In this paper,a survey of adaptive fuzzy for uncertain nonlinear systems is presented.The first part introduces adaptive fuzzy control emergence and some typical control methods for uncertain nonlinear systems with ma...In this paper,a survey of adaptive fuzzy for uncertain nonlinear systems is presented.The first part introduces adaptive fuzzy control emergence and some typical control methods for uncertain nonlinear systems with matching conditions(single-input singleoutput systems,multi-input multi-output systems).The last part presents the adaptive fuzzy state feedback and output-feedback control methods for uncertain nonlinear systems with non-matching conditions based on the backstepping technique,including strictfeedback systems,pure-feedback systems and non-strict-feedback systems.展开更多
This paper studies the robust adaptive fuzzy cooperative tracking control problem for a class of uncertain non-linear multi-agent systems with multiple time delays and dead-zone non-linearities.First,based on the impl...This paper studies the robust adaptive fuzzy cooperative tracking control problem for a class of uncertain non-linear multi-agent systems with multiple time delays and dead-zone non-linearities.First,based on the implicit function theorem,the non-affine form of the multi-agent system can be converted into the corresponding affine form.Then,using the local state information of neighbouring agents,a novel adaptive fuzzy cooperative tracking controller with the corresponding parameter-updated laws is designed based on undirected communication topologies.Furthermore,it is shown that all the closed-loop signals are bounded,and all follower nodes asymptotically tracking to the leader can be achieved in the presence of time-delayed perturbations and unknown dead-zone inputs.Finally,simulation results are given to demonstrate the effectiveness of the proposed adaptive control scheme.展开更多
In this paper,an adaptive fuzzy state feedback control method is proposed for the single-link robotic manipulator system.The considered system contains unknown nonlinearfunction and actuator saturation.Fuzzy logic sys...In this paper,an adaptive fuzzy state feedback control method is proposed for the single-link robotic manipulator system.The considered system contains unknown nonlinearfunction and actuator saturation.Fuzzy logic systems(FLSs)and a smooth function are used to approximate the unknownnonlinearities and the actuator saturation,respectively.By com-bining the command-filter technique with the backsteppingdesign algorithm,a novel adaptive fuuzy tracking backsteppingcontrol method is developed.It is proved that the adaptive fuuzycontrol scheme can guarantee that all the variables in the closed-loop system are bounded,and the system output can track thegiven reference signal as close as possible.Simulation results areprovided to illustrate the effectiveness of the proposed approach.展开更多
In this paper, synchronization for a class of uncertain fractional-order neural networks with external disturbances is discussed by means of adaptive fuzzy control. Fuzzy logic systems, whose inputs are chosen as sync...In this paper, synchronization for a class of uncertain fractional-order neural networks with external disturbances is discussed by means of adaptive fuzzy control. Fuzzy logic systems, whose inputs are chosen as synchronization errors,are employed to approximate the unknown nonlinear functions. Based on the fractional Lyapunov stability criterion, an adaptive fuzzy synchronization controller is designed, and the stability of the closed-loop system, the convergence of the synchronization error, as well as the boundedness of all signals involved can be guaranteed. To update the fuzzy parameters,fractional-order adaptations laws are proposed. Just like the stability analysis in integer-order systems, a quadratic Lyapunov function is used in this paper. Finally, simulation examples are given to show the effectiveness of the proposed method.展开更多
To solve the problem that the choice of softening factor in conventional adaptive strong tracking filter( STF) greatly relies on the experience and computer simulation,a new concept of softening factor matrix is intro...To solve the problem that the choice of softening factor in conventional adaptive strong tracking filter( STF) greatly relies on the experience and computer simulation,a new concept of softening factor matrix is introduced and a fuzzy adaptive strong tracking cubature Kalman filter( FASTCKF) based on fuzzy logic controller is proposed. This method monitors residual absolute mean and standard deviation of each measurement component with fuzzy logic adaptive controller( FLAC),and adjusts the softening factor matrix dynamically by fuzzy rules,which is capable to modify suboptimal fading factor of STF adaptively and improve the filter's robust adaptive capacity. The simulation results show that the improved filtering performance is superior to the conventional square root cubature Kalman filter( SCKF) and the strong tracking square root cubature Kalman filter( STSCKF).展开更多
The current and future status of the internet is represented by the upcoming Internet of Things(IoT).The internet can connect the huge amount of data,which contains lot of processing operations and efforts to transfer...The current and future status of the internet is represented by the upcoming Internet of Things(IoT).The internet can connect the huge amount of data,which contains lot of processing operations and efforts to transfer the pieces of information.The emerging IoT technology in which the smart ecosystem is enabled by the physical object fixed with software electronics,sensors and network connectivity.Nowadays,there are two trending technologies that take the platform i.e.,Software Defined Network(SDN)and IoT(SD-IoT).The main aim of the IoT network is to connect and organize different objects with the internet,which is managed with the control panel and data panel in the SD network.The main issue and the challenging factors in this network are the increase in the delay and latency problem between the controllers.It is more significant for wide area networks,because of the large packet propagation latency and the controller placement problem is more important in every network.In the proposed work,IoT is implementing with adaptive fuzzy controller placement using the enhanced sunflower optimization(ESFO)algorithm and Pareto Optimal Controller placement tool(POCO)for the placement problem of the controller.In order to prove the efficiency of the proposed system,it is compared with other existing methods like PASIN,hybrid SD and PSO in terms of load balance,reduced number of controllers and average latency and delay.With 2 controllers,the proposed method obtains 400 miles as average latency,which is 22.2%smaller than PSO,76.9%lesser than hybrid SD and 91.89%lesser than PASIN.展开更多
focus of all countries.As an effective new energy,the fuel cell has attracted the attention of scholars.However,due to the particularity of proton exchange membrane fuel cell(PEMFC),the performance of traditional PI c...focus of all countries.As an effective new energy,the fuel cell has attracted the attention of scholars.However,due to the particularity of proton exchange membrane fuel cell(PEMFC),the performance of traditional PI controlled phase-shifted full-bridge power electronics DC-DC converter cannot meet the needs of practical application.In order to further improve the dynamic performance of the converter,this paper first introduces several main topologies of the current mainstream front-end DC-DC converter,and analyzes their performance in the fuel cell system.Then,the operation process of the phase-shifted fullbridge power electronics DC-DC converter is introduced,and the shortcomings of the traditional PI control are analyzed.Finally,a double closed-loop adaptive fuzzy PI controller is proposed,which is characterized by dynamically adjusting PI parameters according to different working states to complete the intelligent control of phase-shifted full-bridge DC-DC converter.The simulation results in MATLAB/Simulink show that the proposed algorithm has good a control effect.Compared with the traditional algorithm,the overshoot and stabilization time of the system are shorter.The algorithm can effectively suppress the fluctuation of the output current of the fuel cell converter,and is a very practical control method.展开更多
This paper presents a novel control method for accommodating actuator faults in a class of multiple-input multiple-output (MIMO) nonlinear uncertain systems.The designed control scheme can tolerate both the time-varyi...This paper presents a novel control method for accommodating actuator faults in a class of multiple-input multiple-output (MIMO) nonlinear uncertain systems.The designed control scheme can tolerate both the time-varying lock-in-place and loss of effectiveness actuator faults.In each subsystem of the considered MIMO system,the controller is obtained from a backstepping procedure;an adaptive fuzzy approximator with minimal learning parameterization is employed to approximate the package of unknown nonlinear functions in each design step.Additional control effort is taken to deal with the approximation error and external disturbance together.It is proven that the closed-loop stability and desired tracking performance can be guaranteed by the proposed control scheme.An example is used to show the effectiveness of the designed controller.展开更多
Space manipulator with free-swinging joint failure simultaneously contains kinematic and dynamic coupling relationships,so it belongs to a new underactuated system.To allow the manipulator to carry on tasks,an effecti...Space manipulator with free-swinging joint failure simultaneously contains kinematic and dynamic coupling relationships,so it belongs to a new underactuated system.To allow the manipulator to carry on tasks,an effective robust underactuated control method for the space manipulator with free-swinging joint failure is studied in this paper.Considering the effect of model uncertainty and joint torque disturbance,a robust underactuated control system based on the Terminal Sliding Mode Controller(TSMC)is designed,but two drawbacks are discussed:(A)Robustness depraves with eliminating chattering.(B)Control parameters are difficult to be determined under unknown uncertainty and disturbance.To improve the TSMC,the adaptive fuzzy controller is introduced to estimate the real effect of unknown uncertainty and disturbance according to deviations of sliding mode and its reaching law.The estimated result is directly compensated into active joints torque.In simulation,the space manipulator with free-swinging joint executes tasks based on the TSMC and the Adaptive Fuzzy Terminal Sliding Mode Controller(AFTSMC)respectively.Same tasks can be finished with smaller joints torque and stronger robustness based on the AFTSMC.Therefore,AFTSMC can serve as an effective robust control method for the space manipulator with free-swinging joint failure under unknown model uncertainty and torque disturbance.展开更多
This paper presents a nonlinear control approach to variable speed wind turbine(VSWT)with a wind speed estimator.The dynamics of the wind turbine(WT)is derived from single mass model.In this work,a modified Newton Rap...This paper presents a nonlinear control approach to variable speed wind turbine(VSWT)with a wind speed estimator.The dynamics of the wind turbine(WT)is derived from single mass model.In this work,a modified Newton Raphson estimator has been considered for exact estimation of effective wind speed.The main objective of this work is to extract maximum energy from the wind at below rated wind speed while reducing drive train oscillation.In order to achieve the above objectives,VSWT should operate close to the optimal power coefficient.The generator torque is considered as the control input to achieve maximum energy capture.From the literature,it is clear that existing linear and nonlinear control techniques suffer from poor tracking of WT dynamics,increased power loss and complex control law.In addition,they are not robust with respect to input disturbances.In order to overcome the above drawbacks,adaptive fuzzy integral sliding mode control(AFISMC)is proposed for VSWT control.The proposed controller is tested with different types of disturbances and compared with other nonlinear controllers such as sliding mode control and integral sliding mode control.The result shows the better performance of AFISMC and its robustness to input disturbances.In this paper,the discontinuity in integral sliding mode controller is smoothed by using hyperbolic tangent function,and the sliding gain is adapted using a fuzzy technique which makes the controller more robust.展开更多
In order to overcome the shortcomings of the traditional sling suspension method,such as complex structure of suspension truss,large running resistance,and low precision of position servo system,a gravity compensation...In order to overcome the shortcomings of the traditional sling suspension method,such as complex structure of suspension truss,large running resistance,and low precision of position servo system,a gravity compensation method of lunar rover based on the combination of active suspension and active position following of magnetic levitation is proposed,and the overall design is carried out.The dynamic model of the suspension module of microgravity compensation system was established,and the decoupling control between the constant force component and the position servo component was analyzed and verified.The constant tension control was achieved by using hybrid force/position control.The position following control was realized by using fuzzy adaptive PID(proportional⁃integral⁃differential)control.The stable suspension control was realized based on the principle of force balance.The simulation results show that the compensation accuracy of constant tension could reach more than 95%,the position deviation was less than 5 mm,the position deviation angle was less than 0.025°,and the air gap recovered stability within 0.1 s.The gravity compensation system has excellent dynamic performance and can meet the requirements of microgravity simulation experiment of lunar rover.展开更多
This paper presents a fuzzy adaptive sliding mode controller(FASMC)for electrically driven wheeled mobile robot for trajectory tracking task in the presence of uncertainties and disturbances.First,a finite-time kinema...This paper presents a fuzzy adaptive sliding mode controller(FASMC)for electrically driven wheeled mobile robot for trajectory tracking task in the presence of uncertainties and disturbances.First,a finite-time kinematic controller is developed to compute the auxiliary velocity vector.Second,the FASMC,based on the nonlinear dynamic model of the robot and its actuators,is used to guarantee the stability and the convergence of the closed-loop system.Moreover,by employing the advantages of the fuzzy logic systems,the developed controller ensures the robustness of the system against dynamic disturbances and uncertainties,the smoothness of the computing voltage against the chattering phenomenon,and the optimal convergence of the velocity and posture errors.The Lyapunov theory is used to analyse the stability of this algorithm.In order to evaluate the effectiveness of the developed method,numerical simulations are done in the Mahlab/Simulink environment.展开更多
A Simplified Grey Wolf Optimizer(SGWO)is suggested for resolving optimization tasks.The simplification in the original Grey Wolf Optimizer(GWO)method is introduced by ignoring the worst category wolves while giving pr...A Simplified Grey Wolf Optimizer(SGWO)is suggested for resolving optimization tasks.The simplification in the original Grey Wolf Optimizer(GWO)method is introduced by ignoring the worst category wolves while giving priority to the better wolves during the search process.The advantage of the presented SGWO over GWO is a better solution taking less execution time and is demonstrated by taking unimodal,multimodal,and fixed dimension test functions.The results are also contrasted to the Gravitational Search Algorithm,the Particle Swarm Optimization,and the Sine Cosine Algorithm and this shows the superiority of the proposed SGWO technique.Practical application in a Distributed Power Generation System(DPGS)with energy storage is then considered by designing an Adaptive Fuzzy PID(AFPID)controller using the suggested SGWO method for frequency control.The DPGS contains renewable generation such as photovoltaic,wind,and storage elements such as battery and flywheel,in addition to plug-in electric vehicles.It is demonstrated that the SGWO method is superior to the GWO method in the optimal controller design task.It is also seen that SGWO based AFPID controller is highly efficacious in regulating the frequency compared to the standard PID controller.A sensitivity study is also performed to examine the impact of the unpredictability in the parameters of the investigated system on system performance.Finally,the novelty of the paper is demonstrated by comparing with the existing publications in an extensively used two-area test system.展开更多
As a nonlinear,strong coupling and multi-variable system,the drive performance of bearingless switched reluctance motor(BLSRM)is always limited by its complicated electromagnetic properties.Generally,conventional PID ...As a nonlinear,strong coupling and multi-variable system,the drive performance of bearingless switched reluctance motor(BLSRM)is always limited by its complicated electromagnetic properties.Generally,conventional PID methods are used to achieve the basic control requirement in wide industrial applications,however its inherent operating principle limits its use on suspending control of BLSRM.In this paper,the suspending force system,which is separately controlled from torque system,is built based on an adaptive fuzzy PID controller to limit the rotor eccentric displacement with proper generation of radial force.When compared with a system adopted using conventional PID method for suspending force control,the proposed adaptive fuzzy PID method has superior performance in shortening the response time,reducing the maximum eccentric displacement error and higher speed range of operation due to its online self-turning of controller parameters.Both in simulation and experimental cases,comparison of results of the above two methods validates the effectiveness of the adaptive fuzzy PID controller for BLSRM drive system.展开更多
文摘For electro-discharge machining, only in the optimum state could the highest material removal rate be realized. In practical machining process, the timely elevation of the tool electrode is needed to eliminate chipping, which ordinarily occupies quite a lot of time. Therefore, besides the control of the machining parameters, the control of the optimum discharge gap and the conversion of different machining states is also needed. In this paper, the adaptive fuzzy control system of servomechanism for EDM combined with ultrasonic vibration is studied, the servomechanism of which is composed of the stepping motor comprising variable steps and the inductive synchronizer. The fuzzy control technology is used to realize the control of the frequency and the step of the servomechanism. The adaptive fuzzy controller has three inputs and two outputs, which can well meet the actual control requirements. The constitution of the fuzzy control regulation for the step frequency is the key to the design of the whole fuzzy control system of the servomechanism. The step frequency is mainly determined by the position error and the change rate of the position error. When the value of the position error is high or medium, the controlled parameters are selected to eliminate the error; when the position error is lower, the controlled parameters are selected to avoid the over-orientation and thus keep the stability of the system. According to these, a fuzzy control table is established in advanced, which is used to express the relations between the fuzzy input parameters and the fuzzy output parameters. The input parameters and the output parameters are all expressed by the level-values in fuzzy field. Therefore, the output parameters used for control can be obtained for the fuzzy control table according to the detected actual input parameters, by which the EDM combined with ultrasonic vibration is improved and the machining efficiency is increased. In addition, a stimulation program is designed by means of Microsoft Visual Basic
文摘The advantage of fuzzy controllers in working with inaccurate and nonlinear inputs is that there is no need for an accurate mathematical model and fast convergence and minimal fluctuations in the maximum power point detector.The capability of online fuzzy tracking systems is maximum power,resistance to radiation and temperature changes,and no need for external sensors to measure radiation intensity and temperature.However,the most important issue is the constant changes in the amount of sunlight that cause the maximum power point to be constantly changing.The controller used in the maximum power point tracking(MPPT)circuit must be able to adapt to the new radiation conditions.Therefore,in this paper,to more accurately track the maximumpower point of the solar system and receive more electrical power at its output,an adaptive fuzzy control was proposed,the parameters of which are optimized by the whale algorithm.The studies have repeated under different irradiation conditions and the proposed controller performance has been compared with perturb and observe algorithm(P&O)method,which is a practical and high-performance method.To evaluate the performance of the proposed algorithm,the particle swarm algorithm optimized the adaptive fuzzy controller.The simulation results show that the adaptive fuzzy control system performs better than the P&O tracking system.Higher accuracy and consequently more production power at the output of the solar panel is one of the salient features of the proposed control method,which distinguishes it from other methods.On the other hand,the adaptive fuzzy controller optimized by the whale algorithm has been able to perform relatively better than the controller designed by the particle swarm algorithm,which confirms the higher accuracy of the proposed algorithm.
基金the project of science and technology of Henan province under Grant No.14210221036.
文摘Multi-joint manipulator systems are subject to nonlinear influences such as frictional characteristics,random disturbances and load variations.To account for uncertain disturbances in the operation of manipulators,we propose an adaptive manipulator control method based on a multi-joint fuzzy system,in which the upper bound information of the fuzzy system is constant and the state variables of the manipulator control system are measurable.The control algorithm of the system is a MIMO(multi-input-multi-output)fuzzy system that can approximate system error by using a robust adaptive control law to eliminate the shadow caused by approximation error.It can ensure the stability of complex manipulator control systems and reduce the number of fuzzy rules required.Comparison of experimental and simulation data shows that the controller designed using this algorithm has highly-precise trajectory-tracking control and can control robotic systems with complex characteristics of non-linearity,coupling and uncertainty.Therefore,the proposed algorithm has good practical application prospects and promotes the development of complex control systems.
基金partially supported by the National Natural Science Foundation of China(62322307)Sichuan Science and Technology Program,China(2023NSFSC1968).
文摘The paper investigates the practical prescribed-time fuzzy tracking control problem for a category of nonlinear system subject to time-varying actuator faults.The presence of unknown nonlinear dynamics and actuator faults makes achieving tracking control within a prescribed-time challenging.To tackle this issue,we propose a novel practical prescribed-time fuzzy tracking control strategy,which is independent of the initial state of the system and does not rely on precise modeling of the system and actuators.We apply the approximation capabilities of fuzzy logic systems to handle the unknown nonlinear functions and unidentified actuator faults in the system.The piecewise controller and adaptive law constructed based on piecewise prescribed time-varying function and backstepping technique method establish the theoretical framework of practical prescribed-time tracking control,and extend the range of prescribed-time tracking control to infinity.Regardless of the initial conditions,the proposed control strategy can guarantee that all signals remain uniformly bounded within the practical prescribed time in the presence of unknown nonlinear item and time-varying actuator faults.Simulation example is presented to demonstrate the effectiveness of the proposed control strategy.
基金supported in part by the National Natural Science Foundation of China (61773051,61773072,61761166011)the Fundamental Research Fund for the Central Universities (2016RC021,2017JBZ003)
文摘In this paper, we propose an adaptive fuzzy dynamic surface control(DSC) scheme for single-link flexible-joint robotic systems with input saturation. A smooth function is utilized with the mean-value theorem to deal with the difficulties associated with input saturation. An adaptive DSC design with an auxiliary first-order filter is used to solve the "explosion of complexity"problem. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood around zero. The main advantage of the proposed method is that only one adaptation parameter needs to be updated,which reduces the computational burden significantly. Simulation results demonstrate the feasibility of the proposed scheme and the comparison results show that the improved DSC method can reduce the computational burden by almost two thirds in comparison with the standard DSC method.
基金supported by the National Natural Science Foundation of China (No. 60974028,60804021)
文摘This paper presents an improved observer-based indirect adaptive fuzzy control scheme for multiinput-multioutput (MIMO) nonlinear time-delay systems.The control scheme synthesizes adaptive fuzzy control with adaptive fuzzy identification.An observer is designed to observe the system state,and an identifier is developed to identify the unknown parts of the system.The update laws for parameters utilize two types of errors in the adaptive time-delay fuzzy logic systems,the observation error and the identification error.Performance analysis proves the superiority of the update laws in terms of faster and improved tracking and parameter convergence.Simulation results of two-link manipulator demonstrate the effectiveness of the improved control scheme.
基金Thisworkwas supported in part by theNationalNatural Science Foundation ofChina[grant number 61773188].
文摘In this paper,a survey of adaptive fuzzy for uncertain nonlinear systems is presented.The first part introduces adaptive fuzzy control emergence and some typical control methods for uncertain nonlinear systems with matching conditions(single-input singleoutput systems,multi-input multi-output systems).The last part presents the adaptive fuzzy state feedback and output-feedback control methods for uncertain nonlinear systems with non-matching conditions based on the backstepping technique,including strictfeedback systems,pure-feedback systems and non-strict-feedback systems.
基金the Funds of National Science ofChina[grant number 61273011],[grant number 61174215].
文摘This paper studies the robust adaptive fuzzy cooperative tracking control problem for a class of uncertain non-linear multi-agent systems with multiple time delays and dead-zone non-linearities.First,based on the implicit function theorem,the non-affine form of the multi-agent system can be converted into the corresponding affine form.Then,using the local state information of neighbouring agents,a novel adaptive fuzzy cooperative tracking controller with the corresponding parameter-updated laws is designed based on undirected communication topologies.Furthermore,it is shown that all the closed-loop signals are bounded,and all follower nodes asymptotically tracking to the leader can be achieved in the presence of time-delayed perturbations and unknown dead-zone inputs.Finally,simulation results are given to demonstrate the effectiveness of the proposed adaptive control scheme.
基金This work was supported by the National Natural Science Foundation of China(61573175,61374113)Liaoning BaiQianWan Talents Program.
文摘In this paper,an adaptive fuzzy state feedback control method is proposed for the single-link robotic manipulator system.The considered system contains unknown nonlinearfunction and actuator saturation.Fuzzy logic systems(FLSs)and a smooth function are used to approximate the unknownnonlinearities and the actuator saturation,respectively.By com-bining the command-filter technique with the backsteppingdesign algorithm,a novel adaptive fuuzy tracking backsteppingcontrol method is developed.It is proved that the adaptive fuuzycontrol scheme can guarantee that all the variables in the closed-loop system are bounded,and the system output can track thegiven reference signal as close as possible.Simulation results areprovided to illustrate the effectiveness of the proposed approach.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11401243 and 61403157)the Foundation for Distinguished Young Talents in Higher Education of Anhui Province,China(Grant No.GXYQZD2016257)+3 种基金the Fundamental Research Funds for the Central Universities of China(Grant No.GK201504002)the Natural Science Foundation for the Higher Education Institutions of Anhui Province of China(Grant Nos.KJ2015A256 and KJ2016A665)the Natural Science Foundation of Anhui Province,China(Grant No.1508085QA16)the Innovation Funds of Graduate Programs of Shaanxi Normal University,China(Grant No.2015CXB008)
文摘In this paper, synchronization for a class of uncertain fractional-order neural networks with external disturbances is discussed by means of adaptive fuzzy control. Fuzzy logic systems, whose inputs are chosen as synchronization errors,are employed to approximate the unknown nonlinear functions. Based on the fractional Lyapunov stability criterion, an adaptive fuzzy synchronization controller is designed, and the stability of the closed-loop system, the convergence of the synchronization error, as well as the boundedness of all signals involved can be guaranteed. To update the fuzzy parameters,fractional-order adaptations laws are proposed. Just like the stability analysis in integer-order systems, a quadratic Lyapunov function is used in this paper. Finally, simulation examples are given to show the effectiveness of the proposed method.
基金National Natural Science Foundations of China(Nos.51175082,60874092,51375088)
文摘To solve the problem that the choice of softening factor in conventional adaptive strong tracking filter( STF) greatly relies on the experience and computer simulation,a new concept of softening factor matrix is introduced and a fuzzy adaptive strong tracking cubature Kalman filter( FASTCKF) based on fuzzy logic controller is proposed. This method monitors residual absolute mean and standard deviation of each measurement component with fuzzy logic adaptive controller( FLAC),and adjusts the softening factor matrix dynamically by fuzzy rules,which is capable to modify suboptimal fading factor of STF adaptively and improve the filter's robust adaptive capacity. The simulation results show that the improved filtering performance is superior to the conventional square root cubature Kalman filter( SCKF) and the strong tracking square root cubature Kalman filter( STSCKF).
文摘The current and future status of the internet is represented by the upcoming Internet of Things(IoT).The internet can connect the huge amount of data,which contains lot of processing operations and efforts to transfer the pieces of information.The emerging IoT technology in which the smart ecosystem is enabled by the physical object fixed with software electronics,sensors and network connectivity.Nowadays,there are two trending technologies that take the platform i.e.,Software Defined Network(SDN)and IoT(SD-IoT).The main aim of the IoT network is to connect and organize different objects with the internet,which is managed with the control panel and data panel in the SD network.The main issue and the challenging factors in this network are the increase in the delay and latency problem between the controllers.It is more significant for wide area networks,because of the large packet propagation latency and the controller placement problem is more important in every network.In the proposed work,IoT is implementing with adaptive fuzzy controller placement using the enhanced sunflower optimization(ESFO)algorithm and Pareto Optimal Controller placement tool(POCO)for the placement problem of the controller.In order to prove the efficiency of the proposed system,it is compared with other existing methods like PASIN,hybrid SD and PSO in terms of load balance,reduced number of controllers and average latency and delay.With 2 controllers,the proposed method obtains 400 miles as average latency,which is 22.2%smaller than PSO,76.9%lesser than hybrid SD and 91.89%lesser than PASIN.
基金This work was supported in part by the Natural Science Foundation of Jiangsu Province under Grant BK20200969(L.Z.,URL:http://std.jiangsu.gov.cn/)in part by the Natural Science Foundation for Universities of Jiangsu Province under Grant 20KJB520008(Y.Y.,URL:http://jyt.jiangsu.gov.cn/)+2 种基金in part by the Nantong Science and Technology Plan Project under Grant JC2020148(Y.Y.,URL:http://kjj.nantong.gov.cn/)JC2020151(Y.C.,URL:http://kjj.nantong.gov.cn/)JC2019095(L.R.,URL:http://kjj.nantong.gov.cn/).
文摘focus of all countries.As an effective new energy,the fuel cell has attracted the attention of scholars.However,due to the particularity of proton exchange membrane fuel cell(PEMFC),the performance of traditional PI controlled phase-shifted full-bridge power electronics DC-DC converter cannot meet the needs of practical application.In order to further improve the dynamic performance of the converter,this paper first introduces several main topologies of the current mainstream front-end DC-DC converter,and analyzes their performance in the fuel cell system.Then,the operation process of the phase-shifted fullbridge power electronics DC-DC converter is introduced,and the shortcomings of the traditional PI control are analyzed.Finally,a double closed-loop adaptive fuzzy PI controller is proposed,which is characterized by dynamically adjusting PI parameters according to different working states to complete the intelligent control of phase-shifted full-bridge DC-DC converter.The simulation results in MATLAB/Simulink show that the proposed algorithm has good a control effect.Compared with the traditional algorithm,the overshoot and stabilization time of the system are shorter.The algorithm can effectively suppress the fluctuation of the output current of the fuel cell converter,and is a very practical control method.
基金supported by the Research Start Project for Talents of Huaqiao University (No. 10BS108)the Project of Technology Plan of Fujian Province (No. 2009H0033)+1 种基金the Funds for Creative Research Groups of China (No. 60821063)the National Natural Science Foundation of China(No. 60974043,60904010)
文摘This paper presents a novel control method for accommodating actuator faults in a class of multiple-input multiple-output (MIMO) nonlinear uncertain systems.The designed control scheme can tolerate both the time-varying lock-in-place and loss of effectiveness actuator faults.In each subsystem of the considered MIMO system,the controller is obtained from a backstepping procedure;an adaptive fuzzy approximator with minimal learning parameterization is employed to approximate the package of unknown nonlinear functions in each design step.Additional control effort is taken to deal with the approximation error and external disturbance together.It is proven that the closed-loop stability and desired tracking performance can be guaranteed by the proposed control scheme.An example is used to show the effectiveness of the designed controller.
基金co-supported by the Fundamental Research Funds for the Central Universities of China(No.2019PTB012)the National Natural Science Foundation of China(No.51975059)。
文摘Space manipulator with free-swinging joint failure simultaneously contains kinematic and dynamic coupling relationships,so it belongs to a new underactuated system.To allow the manipulator to carry on tasks,an effective robust underactuated control method for the space manipulator with free-swinging joint failure is studied in this paper.Considering the effect of model uncertainty and joint torque disturbance,a robust underactuated control system based on the Terminal Sliding Mode Controller(TSMC)is designed,but two drawbacks are discussed:(A)Robustness depraves with eliminating chattering.(B)Control parameters are difficult to be determined under unknown uncertainty and disturbance.To improve the TSMC,the adaptive fuzzy controller is introduced to estimate the real effect of unknown uncertainty and disturbance according to deviations of sliding mode and its reaching law.The estimated result is directly compensated into active joints torque.In simulation,the space manipulator with free-swinging joint executes tasks based on the TSMC and the Adaptive Fuzzy Terminal Sliding Mode Controller(AFTSMC)respectively.Same tasks can be finished with smaller joints torque and stronger robustness based on the AFTSMC.Therefore,AFTSMC can serve as an effective robust control method for the space manipulator with free-swinging joint failure under unknown model uncertainty and torque disturbance.
文摘This paper presents a nonlinear control approach to variable speed wind turbine(VSWT)with a wind speed estimator.The dynamics of the wind turbine(WT)is derived from single mass model.In this work,a modified Newton Raphson estimator has been considered for exact estimation of effective wind speed.The main objective of this work is to extract maximum energy from the wind at below rated wind speed while reducing drive train oscillation.In order to achieve the above objectives,VSWT should operate close to the optimal power coefficient.The generator torque is considered as the control input to achieve maximum energy capture.From the literature,it is clear that existing linear and nonlinear control techniques suffer from poor tracking of WT dynamics,increased power loss and complex control law.In addition,they are not robust with respect to input disturbances.In order to overcome the above drawbacks,adaptive fuzzy integral sliding mode control(AFISMC)is proposed for VSWT control.The proposed controller is tested with different types of disturbances and compared with other nonlinear controllers such as sliding mode control and integral sliding mode control.The result shows the better performance of AFISMC and its robustness to input disturbances.In this paper,the discontinuity in integral sliding mode controller is smoothed by using hyperbolic tangent function,and the sliding gain is adapted using a fuzzy technique which makes the controller more robust.
基金the National Natural Science Foundation of China(Grant Nos.51305384 and 52075466)。
文摘In order to overcome the shortcomings of the traditional sling suspension method,such as complex structure of suspension truss,large running resistance,and low precision of position servo system,a gravity compensation method of lunar rover based on the combination of active suspension and active position following of magnetic levitation is proposed,and the overall design is carried out.The dynamic model of the suspension module of microgravity compensation system was established,and the decoupling control between the constant force component and the position servo component was analyzed and verified.The constant tension control was achieved by using hybrid force/position control.The position following control was realized by using fuzzy adaptive PID(proportional⁃integral⁃differential)control.The stable suspension control was realized based on the principle of force balance.The simulation results show that the compensation accuracy of constant tension could reach more than 95%,the position deviation was less than 5 mm,the position deviation angle was less than 0.025°,and the air gap recovered stability within 0.1 s.The gravity compensation system has excellent dynamic performance and can meet the requirements of microgravity simulation experiment of lunar rover.
文摘This paper presents a fuzzy adaptive sliding mode controller(FASMC)for electrically driven wheeled mobile robot for trajectory tracking task in the presence of uncertainties and disturbances.First,a finite-time kinematic controller is developed to compute the auxiliary velocity vector.Second,the FASMC,based on the nonlinear dynamic model of the robot and its actuators,is used to guarantee the stability and the convergence of the closed-loop system.Moreover,by employing the advantages of the fuzzy logic systems,the developed controller ensures the robustness of the system against dynamic disturbances and uncertainties,the smoothness of the computing voltage against the chattering phenomenon,and the optimal convergence of the velocity and posture errors.The Lyapunov theory is used to analyse the stability of this algorithm.In order to evaluate the effectiveness of the developed method,numerical simulations are done in the Mahlab/Simulink environment.
文摘A Simplified Grey Wolf Optimizer(SGWO)is suggested for resolving optimization tasks.The simplification in the original Grey Wolf Optimizer(GWO)method is introduced by ignoring the worst category wolves while giving priority to the better wolves during the search process.The advantage of the presented SGWO over GWO is a better solution taking less execution time and is demonstrated by taking unimodal,multimodal,and fixed dimension test functions.The results are also contrasted to the Gravitational Search Algorithm,the Particle Swarm Optimization,and the Sine Cosine Algorithm and this shows the superiority of the proposed SGWO technique.Practical application in a Distributed Power Generation System(DPGS)with energy storage is then considered by designing an Adaptive Fuzzy PID(AFPID)controller using the suggested SGWO method for frequency control.The DPGS contains renewable generation such as photovoltaic,wind,and storage elements such as battery and flywheel,in addition to plug-in electric vehicles.It is demonstrated that the SGWO method is superior to the GWO method in the optimal controller design task.It is also seen that SGWO based AFPID controller is highly efficacious in regulating the frequency compared to the standard PID controller.A sensitivity study is also performed to examine the impact of the unpredictability in the parameters of the investigated system on system performance.Finally,the novelty of the paper is demonstrated by comparing with the existing publications in an extensively used two-area test system.
文摘As a nonlinear,strong coupling and multi-variable system,the drive performance of bearingless switched reluctance motor(BLSRM)is always limited by its complicated electromagnetic properties.Generally,conventional PID methods are used to achieve the basic control requirement in wide industrial applications,however its inherent operating principle limits its use on suspending control of BLSRM.In this paper,the suspending force system,which is separately controlled from torque system,is built based on an adaptive fuzzy PID controller to limit the rotor eccentric displacement with proper generation of radial force.When compared with a system adopted using conventional PID method for suspending force control,the proposed adaptive fuzzy PID method has superior performance in shortening the response time,reducing the maximum eccentric displacement error and higher speed range of operation due to its online self-turning of controller parameters.Both in simulation and experimental cases,comparison of results of the above two methods validates the effectiveness of the adaptive fuzzy PID controller for BLSRM drive system.
