In this paper,a grid voltage sensorless model predictive control is proposed and verified by simulation and experimental tests for a PWM rectifier.The presented method is simple and cost effective due to no need of mo...In this paper,a grid voltage sensorless model predictive control is proposed and verified by simulation and experimental tests for a PWM rectifier.The presented method is simple and cost effective due to no need of modulator and voltage sensors.The developed sliding mode voltage observer(SMVO)can theoretically track the grid voltage accurately without phase lag and magnitude error.Based on the proposed SMVO,the finite control set-model predictive control(FCS-MPC)is incorporated for power regulation.The active power and reactive power are calculated and predicted using the measured current and the estimated grid voltage from the SMVO.With the predicated power for one-step delay compensation,the best voltage vector minimizing the tracking error is selected by FCS-MPC.The whole algorithm is implemented in stationary frame without using Park's transformation.Both the simulation and experimental results validate the effectiveness of the proposed method.展开更多
In this paper,a predictive sliding mode control method based on multi-sensor fusion is proposed to solve the problem of insufficient accuracy in trajectory tracking caused by actuator delay.The controller,based on the...In this paper,a predictive sliding mode control method based on multi-sensor fusion is proposed to solve the problem of insufficient accuracy in trajectory tracking caused by actuator delay.The controller,based on the kinematics model,uses an inner and outer two-layer structure to achieve decoupling of position control and heading control.A reference positional change rate is introduced into the design of controller,making the automatic guided vehicle(AGV)capable of real-time predictive control ability.A stability analysis and a proof of predictive sliding mode control theory are provided.The experimental results show that the new control algorithm can improve the performance of the AGV controller by referring to the positional change rate,thereby improving the AGV operation without derailing.展开更多
Autonomous vehicles are prone to instability when the motors of the four-wheel independent-driving electric vehicles fail at high driving speed on low-adhesion roads.To improve the vehicle tracking performance in the ...Autonomous vehicles are prone to instability when the motors of the four-wheel independent-driving electric vehicles fail at high driving speed on low-adhesion roads.To improve the vehicle tracking performance in the expected path and ensure vehicle stability when the motor fails,this paper designs an integrated path-following and passive fault-tolerant controller.The path-following controller is designed to improve the vehicle path-following performance based on model predictive control(MPC),while the passive fault-tolerant controller is used to ensure vehicle stability when the motor fails.First,a vehicle dynamic model is established and simplified,and an MPC controller based on a state-space equation is designed.Then,taking the motor fault as a fault factor,a first-order sliding mode fault-tolerant controller is developed.The first-order sliding mode fault-tolerant controller takes the vehicle’s yaw rate and sideslip angle into account.Furthermore,to address the chattering problem of the traditional first-order sliding mode fault-tolerant controller,a second-order sliding mode fault-tolerant controller with a disturbance observer is developed.Finally,the developed controller is tested using the Simulink/Carsim platform and applied to a Raspberry Pi 4B for controller hardware-in-the-loop experiment.Simulation and experi-ment results show the practicability and effectiveness of the proposed integrated control strategy.展开更多
In this paper,the optimal tracking control for robotic manipulators with state constraints and uncertain dynamics is investigated,and a sliding mode-based adaptive tube model predictive control method is proposed.Firs...In this paper,the optimal tracking control for robotic manipulators with state constraints and uncertain dynamics is investigated,and a sliding mode-based adaptive tube model predictive control method is proposed.First,utilizing the high-order fully actuated system approach,the nominal model of the robotic manipulator is constructed as the predictive model.Based on the nominal model,a nominal model predictive controller with the sliding mode is designed,which relaxes the terminal constraints,and realizes the accurate and stable tracking of the desired trajectory by the nominal system.Then,an auxiliary controller based on the node-adaptive neural networks is constructed to dynamically compensate nonlinear uncertain dynamics of the robotic manipulator.Furthermore,the estimation deviation between the nominal and actual states is limited to the tube invariant sets.At the same time,the recursive feasibility of nominal model predictive control is verified,and the ultimately uniformly boundedness of all variables is proved according to the Lyapunov theorem.Finally,experiments show that the robotic manipulator can achieve fast and efficient trajectory tracking under the action of the proposed method.展开更多
The mechanical system with backlash is distinguished between a＂backlash mode＂and a＂contact mode＂.The inherent switching between the two operating modes makes the system a prime example of hybrid system.For elimina...The mechanical system with backlash is distinguished between a＂backlash mode＂and a＂contact mode＂.The inherent switching between the two operating modes makes the system a prime example of hybrid system.For eliminating the bad effect of backlash, a piecewise affine（PWA） model of the mechanical servo system with backlash is built.The optimal control of constrained PWA system is obtained by taking advantage of model predictive control（MPC） method, and the explicit solution of MPC in a look-up table form is figured out by combining the dynamic programming and multi-parametric quadratic programming, thereby establishing an explicit hybrid model predictive controller.Furthermore, a piecewise quadratic（PWQ） function for guaranteeing the stability of closed-loop control is found by formulating the search of PWQ function as a semi-definite programming problem.In the tracking experiments, it is demonstrated that the explicit hybrid model predictive controller has a good traction control effect on the mechanical system with backlash.The error meets the demands of real system.Further, compared to the direct on-line computation, the computation burden is reduced by the explicit solution, thereby being suitable for real-time control of system with short sampling time.展开更多
We investigate a distributed game strategy for unmanned aerial vehicle(UAV)formations with external disturbances and obstacles.The strategy is based on a distributed model predictive control(MPC)framework and Levy fli...We investigate a distributed game strategy for unmanned aerial vehicle(UAV)formations with external disturbances and obstacles.The strategy is based on a distributed model predictive control(MPC)framework and Levy flight based pigeon inspired optimization(LFPIO).First,we propose a non-singular fast terminal sliding mode observer(NFTSMO)to estimate the influence of a disturbance,and prove that the observer converges in fixed time using a Lyapunov function.Second,we design an obstacle avoidance strategy based on topology reconstruction,by which the UAV can save energy and safely pass obstacles.Third,we establish a distributed MPC framework where each UAV exchanges messages only with its neighbors.Further,the cost function of each UAV is designed,by which the UAV formation problem is transformed into a game problem.Finally,we develop LFPIO and use it to solve the Nash equilibrium.Numerical simulations are conducted,and the efficiency of LFPIO based distributed MPC is verified through comparative simulations.展开更多
文摘In this paper,a grid voltage sensorless model predictive control is proposed and verified by simulation and experimental tests for a PWM rectifier.The presented method is simple and cost effective due to no need of modulator and voltage sensors.The developed sliding mode voltage observer(SMVO)can theoretically track the grid voltage accurately without phase lag and magnitude error.Based on the proposed SMVO,the finite control set-model predictive control(FCS-MPC)is incorporated for power regulation.The active power and reactive power are calculated and predicted using the measured current and the estimated grid voltage from the SMVO.With the predicated power for one-step delay compensation,the best voltage vector minimizing the tracking error is selected by FCS-MPC.The whole algorithm is implemented in stationary frame without using Park's transformation.Both the simulation and experimental results validate the effectiveness of the proposed method.
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.61903241,61304223,61603191,61873158,61573237)the China Postdoctoral Science Foundation(Grant No.2018M630424)the Natural Science Foundation of Shanghai Municipality(Grant No.18ZR1415100).
文摘In this paper,a predictive sliding mode control method based on multi-sensor fusion is proposed to solve the problem of insufficient accuracy in trajectory tracking caused by actuator delay.The controller,based on the kinematics model,uses an inner and outer two-layer structure to achieve decoupling of position control and heading control.A reference positional change rate is introduced into the design of controller,making the automatic guided vehicle(AGV)capable of real-time predictive control ability.A stability analysis and a proof of predictive sliding mode control theory are provided.The experimental results show that the new control algorithm can improve the performance of the AGV controller by referring to the positional change rate,thereby improving the AGV operation without derailing.
基金supported by Natural Science Foundation of Guangxi Province,(2020GXNSFAA297031,2021AA04006)National Natural Science Foundation of China(51605108)Innovation Project of Guilin University of Electronic Technology(2021YCXS007).
文摘Autonomous vehicles are prone to instability when the motors of the four-wheel independent-driving electric vehicles fail at high driving speed on low-adhesion roads.To improve the vehicle tracking performance in the expected path and ensure vehicle stability when the motor fails,this paper designs an integrated path-following and passive fault-tolerant controller.The path-following controller is designed to improve the vehicle path-following performance based on model predictive control(MPC),while the passive fault-tolerant controller is used to ensure vehicle stability when the motor fails.First,a vehicle dynamic model is established and simplified,and an MPC controller based on a state-space equation is designed.Then,taking the motor fault as a fault factor,a first-order sliding mode fault-tolerant controller is developed.The first-order sliding mode fault-tolerant controller takes the vehicle’s yaw rate and sideslip angle into account.Furthermore,to address the chattering problem of the traditional first-order sliding mode fault-tolerant controller,a second-order sliding mode fault-tolerant controller with a disturbance observer is developed.Finally,the developed controller is tested using the Simulink/Carsim platform and applied to a Raspberry Pi 4B for controller hardware-in-the-loop experiment.Simulation and experi-ment results show the practicability and effectiveness of the proposed integrated control strategy.
文摘In this paper,the optimal tracking control for robotic manipulators with state constraints and uncertain dynamics is investigated,and a sliding mode-based adaptive tube model predictive control method is proposed.First,utilizing the high-order fully actuated system approach,the nominal model of the robotic manipulator is constructed as the predictive model.Based on the nominal model,a nominal model predictive controller with the sliding mode is designed,which relaxes the terminal constraints,and realizes the accurate and stable tracking of the desired trajectory by the nominal system.Then,an auxiliary controller based on the node-adaptive neural networks is constructed to dynamically compensate nonlinear uncertain dynamics of the robotic manipulator.Furthermore,the estimation deviation between the nominal and actual states is limited to the tube invariant sets.At the same time,the recursive feasibility of nominal model predictive control is verified,and the ultimately uniformly boundedness of all variables is proved according to the Lyapunov theorem.Finally,experiments show that the robotic manipulator can achieve fast and efficient trajectory tracking under the action of the proposed method.
基金supported by the Beijing Education Committee Cooperation Building Foundation Project (XK100070532)
文摘The mechanical system with backlash is distinguished between a＂backlash mode＂and a＂contact mode＂.The inherent switching between the two operating modes makes the system a prime example of hybrid system.For eliminating the bad effect of backlash, a piecewise affine（PWA） model of the mechanical servo system with backlash is built.The optimal control of constrained PWA system is obtained by taking advantage of model predictive control（MPC） method, and the explicit solution of MPC in a look-up table form is figured out by combining the dynamic programming and multi-parametric quadratic programming, thereby establishing an explicit hybrid model predictive controller.Furthermore, a piecewise quadratic（PWQ） function for guaranteeing the stability of closed-loop control is found by formulating the search of PWQ function as a semi-definite programming problem.In the tracking experiments, it is demonstrated that the explicit hybrid model predictive controller has a good traction control effect on the mechanical system with backlash.The error meets the demands of real system.Further, compared to the direct on-line computation, the computation burden is reduced by the explicit solution, thereby being suitable for real-time control of system with short sampling time.
基金Project supported by the Science and Technology Innovation 2030 Key Project of“New Generation Artificial Intelligence,”China(No.2018AAA0100803)the National Natural Science Foundation of China(Nos.T2121003,U1913602,U20B2071,91948204,and U19B2033)。
文摘We investigate a distributed game strategy for unmanned aerial vehicle(UAV)formations with external disturbances and obstacles.The strategy is based on a distributed model predictive control(MPC)framework and Levy flight based pigeon inspired optimization(LFPIO).First,we propose a non-singular fast terminal sliding mode observer(NFTSMO)to estimate the influence of a disturbance,and prove that the observer converges in fixed time using a Lyapunov function.Second,we design an obstacle avoidance strategy based on topology reconstruction,by which the UAV can save energy and safely pass obstacles.Third,we establish a distributed MPC framework where each UAV exchanges messages only with its neighbors.Further,the cost function of each UAV is designed,by which the UAV formation problem is transformed into a game problem.Finally,we develop LFPIO and use it to solve the Nash equilibrium.Numerical simulations are conducted,and the efficiency of LFPIO based distributed MPC is verified through comparative simulations.
