In this paper, a new adaptive fuzzy backstepping control approach is developed for a class of nonlinear systems with unknown time-delay and unmeasured states. Using fuzzy logic systems to approximate the unknown nonli...In this paper, a new adaptive fuzzy backstepping control approach is developed for a class of nonlinear systems with unknown time-delay and unmeasured states. Using fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy state observer is designed for estimating the unmeasured states. On the basis of the state observer and applying the backstepping technique, an adaptive fuzzy observer control approach is developed. The main features of the proposed adaptive fuzzy control approach not only guarantees that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded, but also contain less adaptation parameters to be updated on-line. Finally, simulation results are provided to show the effectiveness of the proposed approach.展开更多
In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identified.The main gap is the weakness of current methods vs.noisy environments.The primary objective of this study is to present a...In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identified.The main gap is the weakness of current methods vs.noisy environments.The primary objective of this study is to present a more robust method against uncertainties.This paper proposes a new deep learning scheme for modeling and identification applications.The suggested approach is based on non-singleton type-3 fuzzy logic systems(NT3-FLSs)that can support measurement errors and high-level uncertainties.Besides the rule optimization,the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalmanfilter(SCKF).In the learn-ing algorithm,the presented NT3-FLSs are deeply learned,and their nonlinear structure is preserved.The designed scheme is applied for modeling carbon cap-ture and sequestration problem using real-world data sets.Through various ana-lyses and comparisons,the better efficiency of the proposed fuzzy modeling scheme is verified.The main advantages of the suggested approach include better resistance against uncertainties,deep learning,and good convergence.展开更多
Due to the advanced developments in communication technologies,Internet of vehicles and vehicular adhoc networks(VANET)offers numerous opportunities for effectively managing transportation problems.On the other,the cl...Due to the advanced developments in communication technologies,Internet of vehicles and vehicular adhoc networks(VANET)offers numerous opportunities for effectively managing transportation problems.On the other,the cloud environment needs to disseminate the emergency message to the vehicles which are consistently distributed on the roadway so that every vehicle gets the messages from closer vehicles in a straightforward way.To resolve this issue,clustering and routing techniques can be designed using computational intelligence approaches.With this motivation,this paper presents a new type-2 fuzzy sets based clustering with metaheuristic optimization based routing(T2FSCMOR)technique for secure communication in VANET.The T2FSC-MOR technique aims to elect CHs and optimal routes for secure intercluster data transmission in VANET.The proposed model involves T2FSC technique for the selection of CHs and construction of clusters.The T2FSC technique uses different parameters namely traveling speed(TS),link quality(LQ),trust factor(TF),inter-vehicle distance(IVD),and neighboring node count(NCC).The inclusion of trust factor helps to select the proper cluster heads(CHs)for secure data dissemination process.Moreover,trust aware seagull optimization based routing(TASGOR)approach was derived for the optimal selection of routes in VANET.In order to validate the enhanced performance of proposed technique,the set of simulations take place and the outcomes are examined interms of different measures.The experimental outcomes highlighted the improved performance of the proposed model over the other state of art techniques with a higher throughput of 98%.展开更多
For a class of chaotic systems with unknown functions and disturbances,asymptotic stabilisation of the chaotic systems is achieved by designing a stability controller based on the adaptive fuzzy logic systems.At first...For a class of chaotic systems with unknown functions and disturbances,asymptotic stabilisation of the chaotic systems is achieved by designing a stability controller based on the adaptive fuzzy logic systems.At first,based on the universal approximation property of fuzzy logic systems,the Mamdani-type fuzzy logic systems with the parameter adaptive laws are designed utilising the data information sampled from the inputs and outputs of unknown functions in the chaotic systems,then the fuzzy logic systems are used to design the stability controller with three parameter adaptive laws,but the three parameters have no relationship with the number of fuzzy rules,so the stability controller is not only able to achieve asymptotic stabilisation for the chaotic system’s states,but also to reduce the number of fuzzy rules and the no-line computational burden significantly.Finally,simulations are used to show the validity of the stabilisation method.展开更多
Human-in-the-loop(HiTL)control is promising for the cooperative control problem of multi-agent systems(MASs)under the complicated environment.By considering the effect of human intelligence and decision making,the sys...Human-in-the-loop(HiTL)control is promising for the cooperative control problem of multi-agent systems(MASs)under the complicated environment.By considering the effect of human intelligence and decision making,the system robustness and security are notably enhanced.Hence,a distributed fixed-time tracking control problem is investigated in this paper for heterogeneous MASs based on the HiTL idea.First,a lemma of practically fixed-time stable is given where an explicit relationship of settling time and convergence domain is clearly shown.Then,under the framework of the adaptive backstepping approach,a series of modified intermediate control signals is designed to avoid the singularity problem by taking advantage of power transformation,fuzzy logic systems,and inequality schemes.Finally,the numerical example and comparison results are utilized to testify the effectiveness of the proposed method.展开更多
Using graph theory, matrix theory, adaptive control, fuzzy logic systems and other tools, this paper studies the leader-follower global consensus of two kinds of stochastic uncertain nonlinear multi-agent systems(MAS)...Using graph theory, matrix theory, adaptive control, fuzzy logic systems and other tools, this paper studies the leader-follower global consensus of two kinds of stochastic uncertain nonlinear multi-agent systems(MAS). Firstly, the fuzzy logic systems replaces the feedback compensator as the feedforward compensator to describe the uncertain nonlinear dynamics. Secondly, based on the network topology, all followers are divided into two categories: One is the followers who can obtain the leader signal, and the other is the follower who cannot obtain the leader signal. Thirdly, based on the adaptive control method, distributed control protocols are designed for the two types of followers. Fourthly, based on matrix theory and stochastic Lyapunov stability theory, the stability of the closed-loop systems is analyzed. Finally, three simulation examples are given to verify the effectiveness of the proposed control algorithms.展开更多
A practical fixed-time adaptive fuzzy control strategy is investigated for uncertain nonlinear systems with time-varying asymmetric constraints and input quantization. To overcome the difficulties of designing control...A practical fixed-time adaptive fuzzy control strategy is investigated for uncertain nonlinear systems with time-varying asymmetric constraints and input quantization. To overcome the difficulties of designing controllers under the state constraints, a unified barrier function approach is employed to construct a coordinate transformation that maps the original constrained system to an equivalent unconstrained one, thus relaxing the time-varying asymmetric constraints upon system states and avoiding the feasibility check condition typically required in the traditional barrier Lyapunov function based control approach. Meanwhile, the “explosion of complexity” problem in the traditional backstepping approach arising from repeatedly derivatives of virtual controllers is solved by using the command filter method. It is verified via the fixed-time Lyapunov stability criterion that the system output can track a desired signal within a small error range in a predetermined time, and that all system states remain in the constraint range. Finally, two simulation examples are offered to demonstrate the effectiveness of the proposed strategy.展开更多
基金supported by National Natural Science Foundation of China (No.60674056)Outstanding Youth Funds of Liaoning Province (No.2005219001)Educational Department of Liaoning Province (No.2006R29,No.2007T80)
文摘In this paper, a new adaptive fuzzy backstepping control approach is developed for a class of nonlinear systems with unknown time-delay and unmeasured states. Using fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy state observer is designed for estimating the unmeasured states. On the basis of the state observer and applying the backstepping technique, an adaptive fuzzy observer control approach is developed. The main features of the proposed adaptive fuzzy control approach not only guarantees that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded, but also contain less adaptation parameters to be updated on-line. Finally, simulation results are provided to show the effectiveness of the proposed approach.
基金supported by the project of the National Social Science Fundation(21BJL052,20BJY020,20BJL127,19BJY090)the 2018 Fujian Social Science Planning Project(FJ2018B067)The Planning Fund Project of Humanities and Social Sciences Research of the Ministry of Education in 2019(19YJA790102),The grant has been received by Aoqi Xu.
文摘In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identified.The main gap is the weakness of current methods vs.noisy environments.The primary objective of this study is to present a more robust method against uncertainties.This paper proposes a new deep learning scheme for modeling and identification applications.The suggested approach is based on non-singleton type-3 fuzzy logic systems(NT3-FLSs)that can support measurement errors and high-level uncertainties.Besides the rule optimization,the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalmanfilter(SCKF).In the learn-ing algorithm,the presented NT3-FLSs are deeply learned,and their nonlinear structure is preserved.The designed scheme is applied for modeling carbon cap-ture and sequestration problem using real-world data sets.Through various ana-lyses and comparisons,the better efficiency of the proposed fuzzy modeling scheme is verified.The main advantages of the suggested approach include better resistance against uncertainties,deep learning,and good convergence.
文摘Due to the advanced developments in communication technologies,Internet of vehicles and vehicular adhoc networks(VANET)offers numerous opportunities for effectively managing transportation problems.On the other,the cloud environment needs to disseminate the emergency message to the vehicles which are consistently distributed on the roadway so that every vehicle gets the messages from closer vehicles in a straightforward way.To resolve this issue,clustering and routing techniques can be designed using computational intelligence approaches.With this motivation,this paper presents a new type-2 fuzzy sets based clustering with metaheuristic optimization based routing(T2FSCMOR)technique for secure communication in VANET.The T2FSC-MOR technique aims to elect CHs and optimal routes for secure intercluster data transmission in VANET.The proposed model involves T2FSC technique for the selection of CHs and construction of clusters.The T2FSC technique uses different parameters namely traveling speed(TS),link quality(LQ),trust factor(TF),inter-vehicle distance(IVD),and neighboring node count(NCC).The inclusion of trust factor helps to select the proper cluster heads(CHs)for secure data dissemination process.Moreover,trust aware seagull optimization based routing(TASGOR)approach was derived for the optimal selection of routes in VANET.In order to validate the enhanced performance of proposed technique,the set of simulations take place and the outcomes are examined interms of different measures.The experimental outcomes highlighted the improved performance of the proposed model over the other state of art techniques with a higher throughput of 98%.
基金supported by the Project Program of KLGHEI of China[2013CXZDA015]National Science Foundation of Guangdong Province[S2013010015768]+2 种基金Youth Program of Chongqing Three Gorges University[14QN30]Scientific,Technological Research Program of Chongqing Municipal Education Commission[KJ1401029]National Science Foundation of China[61273219].
文摘For a class of chaotic systems with unknown functions and disturbances,asymptotic stabilisation of the chaotic systems is achieved by designing a stability controller based on the adaptive fuzzy logic systems.At first,based on the universal approximation property of fuzzy logic systems,the Mamdani-type fuzzy logic systems with the parameter adaptive laws are designed utilising the data information sampled from the inputs and outputs of unknown functions in the chaotic systems,then the fuzzy logic systems are used to design the stability controller with three parameter adaptive laws,but the three parameters have no relationship with the number of fuzzy rules,so the stability controller is not only able to achieve asymptotic stabilisation for the chaotic system’s states,but also to reduce the number of fuzzy rules and the no-line computational burden significantly.Finally,simulations are used to show the validity of the stabilisation method.
基金the National Natural Science Foundation of China(Grant Nos.62373208,62003097,62033003,61873139,62103214 and 62203245)the Talent Introduction and Cultivation Plan for Youth Innovation of Universities in Shandong Province。
文摘Human-in-the-loop(HiTL)control is promising for the cooperative control problem of multi-agent systems(MASs)under the complicated environment.By considering the effect of human intelligence and decision making,the system robustness and security are notably enhanced.Hence,a distributed fixed-time tracking control problem is investigated in this paper for heterogeneous MASs based on the HiTL idea.First,a lemma of practically fixed-time stable is given where an explicit relationship of settling time and convergence domain is clearly shown.Then,under the framework of the adaptive backstepping approach,a series of modified intermediate control signals is designed to avoid the singularity problem by taking advantage of power transformation,fuzzy logic systems,and inequality schemes.Finally,the numerical example and comparison results are utilized to testify the effectiveness of the proposed method.
基金supported by Natural Science Foundation of China(No.61573013)。
文摘Using graph theory, matrix theory, adaptive control, fuzzy logic systems and other tools, this paper studies the leader-follower global consensus of two kinds of stochastic uncertain nonlinear multi-agent systems(MAS). Firstly, the fuzzy logic systems replaces the feedback compensator as the feedforward compensator to describe the uncertain nonlinear dynamics. Secondly, based on the network topology, all followers are divided into two categories: One is the followers who can obtain the leader signal, and the other is the follower who cannot obtain the leader signal. Thirdly, based on the adaptive control method, distributed control protocols are designed for the two types of followers. Fourthly, based on matrix theory and stochastic Lyapunov stability theory, the stability of the closed-loop systems is analyzed. Finally, three simulation examples are given to verify the effectiveness of the proposed control algorithms.
基金Project supported by Institutional Fund Projects(No.IFPIP:131-611-1443)。
文摘A practical fixed-time adaptive fuzzy control strategy is investigated for uncertain nonlinear systems with time-varying asymmetric constraints and input quantization. To overcome the difficulties of designing controllers under the state constraints, a unified barrier function approach is employed to construct a coordinate transformation that maps the original constrained system to an equivalent unconstrained one, thus relaxing the time-varying asymmetric constraints upon system states and avoiding the feasibility check condition typically required in the traditional barrier Lyapunov function based control approach. Meanwhile, the “explosion of complexity” problem in the traditional backstepping approach arising from repeatedly derivatives of virtual controllers is solved by using the command filter method. It is verified via the fixed-time Lyapunov stability criterion that the system output can track a desired signal within a small error range in a predetermined time, and that all system states remain in the constraint range. Finally, two simulation examples are offered to demonstrate the effectiveness of the proposed strategy.
