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
Urban rail trains have undergone rapid development in recent years due to their punctuality,high capacity and energy efficiency.Urban trains require frequent start/stop operations and are,therefore,prone to high energ...Urban rail trains have undergone rapid development in recent years due to their punctuality,high capacity and energy efficiency.Urban trains require frequent start/stop operations and are,therefore,prone to high energy losses.As trains have high inertia,the energy that can be recovered from braking comes in short bursts of high power.To effectively recover such braking energy,an onboard supercapacitor system based on a radial basis function neural networkbased sliding mode control system is proposed,which provides robust adaptive performance.The supercapacitor energy storage system is connected to a bidirectional DC/DC converter to provide traction energy or absorb regenerative braking energy.In the Boost and Buck modes,the state-space averaging method is used to establish a model and perform exact linearization.An adaptive sliding mode controller is designed,and simulation results show that it can effectively solve the problems of low energy utilization and large voltage fluctuations in urban rail electricity grids,and maximise the recovery and utilization of regenerative braking energy.展开更多
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the Science and Technology Project of Henan Province under Grant No.14210221036.
文摘Urban rail trains have undergone rapid development in recent years due to their punctuality,high capacity and energy efficiency.Urban trains require frequent start/stop operations and are,therefore,prone to high energy losses.As trains have high inertia,the energy that can be recovered from braking comes in short bursts of high power.To effectively recover such braking energy,an onboard supercapacitor system based on a radial basis function neural networkbased sliding mode control system is proposed,which provides robust adaptive performance.The supercapacitor energy storage system is connected to a bidirectional DC/DC converter to provide traction energy or absorb regenerative braking energy.In the Boost and Buck modes,the state-space averaging method is used to establish a model and perform exact linearization.An adaptive sliding mode controller is designed,and simulation results show that it can effectively solve the problems of low energy utilization and large voltage fluctuations in urban rail electricity grids,and maximise the recovery and utilization of regenerative braking energy.
