In this paper,the authors propose an adaptive Barrier-Lyapunov-Functions(BLFs)based control scheme for nonlinear pure-feedback systems with full state constraints.Due to the coexist of the non-affine structure and ful...In this paper,the authors propose an adaptive Barrier-Lyapunov-Functions(BLFs)based control scheme for nonlinear pure-feedback systems with full state constraints.Due to the coexist of the non-affine structure and full state constraints,it is very difficult to construct a desired controller for the considered system.According to the mean value theorem,the authors transform the pure-feedback system into a system with strict-feedback structure,so that the well-known backstepping method can be applied.Then,in the backstepping design process,the BLFs are employed to avoid the violation of the state constraints,and neural networks(NNs)are directly used to online approximate the unknown packaged nonlinear terms.The presented controller ensures that all the signals in the closed-loop system are bounded and the tracking error asymptotically converges to zero.Meanwhile,it is shown that the constraint requirement on the system will not be violated during the operation.Finally,two simulation examples are provided to show the effectiveness of the proposed control scheme.展开更多
This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange formulation.The considered system can depict the behavior of a larg...This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange formulation.The considered system can depict the behavior of a large class of engineering systems,such as vehicular systems,robot manipulators and satellites.All these systems are often characterized by highly nonlinear characteristics,heavy modeling uncertainties and unknown perturbations,therefore,accurate-model-based nonlinear control approaches become unavailable.Motivated by the challenge,a reinforcement learning(RL)adaptive control methodology based on the actor-critic framework is investigated to compensate the uncertain mechanical dynamics.The approximation inaccuracies caused by RL and the exogenous unknown disturbances are circumvented via a continuous robust integral of the sign of the error(RISE)control approach.Different from a classical RISE control law,a tanh(·)function is utilized instead of a sign(·)function to acquire a more smooth control signal.The developed controller requires very little prior knowledge of the dynamic model,is robust to unknown dynamics and exogenous disturbances,and can achieve asymptotic output tracking.Eventually,co-simulations through ADAMS and MATLAB/Simulink on a three degrees-of-freedom(3-DOF)manipulator and experiments on a real-time electromechanical servo system are performed to verify the performance of the proposed approach.展开更多
The P-type update law has been the mainstream technique used in iterative learning control(ILC)systems,which resembles linear feedback control with asymptotical convergence.In recent years,finite-time control strategi...The P-type update law has been the mainstream technique used in iterative learning control(ILC)systems,which resembles linear feedback control with asymptotical convergence.In recent years,finite-time control strategies such as terminal sliding mode control have been shown to be effective in ramping up convergence speed by introducing fractional power with feedback.In this paper,we show that such mechanism can equally ramp up the learning speed in ILC systems.We first propose a fractional power update rule for ILC of single-input-single-output linear systems.A nonlinear error dynamics is constructed along the iteration axis to illustrate the evolutionary converging process.Using the nonlinear mapping approach,fast convergence towards the limit cycles of tracking errors inherently existing in ILC systems is proven.The limit cycles are shown to be tunable to determine the steady states.Numerical simulations are provided to verify the theoretical results.展开更多
In this paper, the control problem of auxiliary power unit （APU） for hybrid electric vehicles is investigated. An adaptive controller is provided to achieve the coordinated control between the engine speed and the b...In this paper, the control problem of auxiliary power unit （APU） for hybrid electric vehicles is investigated. An adaptive controller is provided to achieve the coordinated control between the engine speed and the battery charging voltage. The proposed adaptive coordinated control laws for the throttle angle of the engine and the voltage of the power-converter can guarantee not only the asymptotic tracking performance of the engine speed and the regulation of the battery charging voltage, but also the robust stability of the closed loop system under external load changes. Simulation results are given to verify the performance of the proposed adaptive controller.展开更多
This paper investigates the homing control problem of a flexible aerial delivery system with external wind,at-mospheric turbulence,and aerodynamic uncertainties.An accurate homing control strategy is presented,consist...This paper investigates the homing control problem of a flexible aerial delivery system with external wind,at-mospheric turbulence,and aerodynamic uncertainties.An accurate homing control strategy is presented,consisting of a trajectory generation algorithm and a lateral tracking controller.A high-altitude trajectory generation is de-veloped with system characteristics explicitly considered to generate the desired trajectory for the aerial delivery control system design.It significantly compensates for the altitude deviation of the existing multiphase theory based trajectory generation methodologies.A novel adaptive vector field control law is then designed to accomplish the lateral tracking maneuvers.The key feature of the proposed method is that the complete lateral closed-loop control,including position and heading angle loops,is achieved in the presence of disturbances and dynamic uncertainties.The homing control with high landing accuracy is therefore achieved.Simulation and hardware-in-loop testing results are finally presented to validate the proposed method’s effectiveness compared to a conventional homing control scheme.展开更多
为实现欠驱动四旋翼飞行器的位置及偏航渐近跟踪,本文基于浸入和不变技术(immersion and invariance,I&I)提出非线性跟踪控制方法.针对四旋翼飞行器的欠驱动特性,设计了内/外环分别为姿态和位置的双闭环结构,并采用浸入和不变技术...为实现欠驱动四旋翼飞行器的位置及偏航渐近跟踪,本文基于浸入和不变技术(immersion and invariance,I&I)提出非线性跟踪控制方法.针对四旋翼飞行器的欠驱动特性,设计了内/外环分别为姿态和位置的双闭环结构,并采用浸入和不变技术构建内/外环的稳定跟踪控制器,利用李雅普诺夫定理保证闭环系统的渐近稳定性.最后,在MATLAB/Simulink环境下进行数值仿真,验证了I&I控制方案的有效性.展开更多
This paper is concerned with the adaptive tracking control problem of nonlinear time-varyingsystems. Based on the backstepping technology, an event-based prescribed performance controlscheme is developed. And the time...This paper is concerned with the adaptive tracking control problem of nonlinear time-varyingsystems. Based on the backstepping technology, an event-based prescribed performance controlscheme is developed. And the time-varying uncertainties of the system are handled byutilising bound estimation method. The proposed controller not only ensures the prescribedtracking performance, but also reduces the communication burden. By using Lyapunov stabilityanalysis, it is proven that all of the closed-loop signals are bounded, and the tracking errorcan converge to zero. Simultaneously, Zeno behaviour is excluded. Finally, the simulation resultsare utilised to illustrate the effectiveness of the proposed adaptive control scheme.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant No.62303278in part by the Taishan Scholar Project of Shandong Province of China under Grant No.tsqn201909078。
文摘In this paper,the authors propose an adaptive Barrier-Lyapunov-Functions(BLFs)based control scheme for nonlinear pure-feedback systems with full state constraints.Due to the coexist of the non-affine structure and full state constraints,it is very difficult to construct a desired controller for the considered system.According to the mean value theorem,the authors transform the pure-feedback system into a system with strict-feedback structure,so that the well-known backstepping method can be applied.Then,in the backstepping design process,the BLFs are employed to avoid the violation of the state constraints,and neural networks(NNs)are directly used to online approximate the unknown packaged nonlinear terms.The presented controller ensures that all the signals in the closed-loop system are bounded and the tracking error asymptotically converges to zero.Meanwhile,it is shown that the constraint requirement on the system will not be violated during the operation.Finally,two simulation examples are provided to show the effectiveness of the proposed control scheme.
基金supported in part by the National Key R&D Program of China under Grant 2021YFB2011300the National Natural Science Foundation of China under Grant 52075262。
文摘This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange formulation.The considered system can depict the behavior of a large class of engineering systems,such as vehicular systems,robot manipulators and satellites.All these systems are often characterized by highly nonlinear characteristics,heavy modeling uncertainties and unknown perturbations,therefore,accurate-model-based nonlinear control approaches become unavailable.Motivated by the challenge,a reinforcement learning(RL)adaptive control methodology based on the actor-critic framework is investigated to compensate the uncertain mechanical dynamics.The approximation inaccuracies caused by RL and the exogenous unknown disturbances are circumvented via a continuous robust integral of the sign of the error(RISE)control approach.Different from a classical RISE control law,a tanh(·)function is utilized instead of a sign(·)function to acquire a more smooth control signal.The developed controller requires very little prior knowledge of the dynamic model,is robust to unknown dynamics and exogenous disturbances,and can achieve asymptotic output tracking.Eventually,co-simulations through ADAMS and MATLAB/Simulink on a three degrees-of-freedom(3-DOF)manipulator and experiments on a real-time electromechanical servo system are performed to verify the performance of the proposed approach.
基金supported by the National Natural Science Foundation of China(62173333)Australian Research Council Discovery Program(DP200101199)。
文摘The P-type update law has been the mainstream technique used in iterative learning control(ILC)systems,which resembles linear feedback control with asymptotical convergence.In recent years,finite-time control strategies such as terminal sliding mode control have been shown to be effective in ramping up convergence speed by introducing fractional power with feedback.In this paper,we show that such mechanism can equally ramp up the learning speed in ILC systems.We first propose a fractional power update rule for ILC of single-input-single-output linear systems.A nonlinear error dynamics is constructed along the iteration axis to illustrate the evolutionary converging process.Using the nonlinear mapping approach,fast convergence towards the limit cycles of tracking errors inherently existing in ILC systems is proven.The limit cycles are shown to be tunable to determine the steady states.Numerical simulations are provided to verify the theoretical results.
文摘In this paper, the control problem of auxiliary power unit （APU） for hybrid electric vehicles is investigated. An adaptive controller is provided to achieve the coordinated control between the engine speed and the battery charging voltage. The proposed adaptive coordinated control laws for the throttle angle of the engine and the voltage of the power-converter can guarantee not only the asymptotic tracking performance of the engine speed and the regulation of the battery charging voltage, but also the robust stability of the closed loop system under external load changes. Simulation results are given to verify the performance of the proposed adaptive controller.
基金supported by the National Natural Science Foundation of China(No.61873207)the IndustryUniversity-Research Innovation Fund in Ministry of Education of China(No.2021ZYA03006)the Key R&D Plan of Shaanxi Province of China(No.2020SF-376)。
文摘This paper investigates the homing control problem of a flexible aerial delivery system with external wind,at-mospheric turbulence,and aerodynamic uncertainties.An accurate homing control strategy is presented,consisting of a trajectory generation algorithm and a lateral tracking controller.A high-altitude trajectory generation is de-veloped with system characteristics explicitly considered to generate the desired trajectory for the aerial delivery control system design.It significantly compensates for the altitude deviation of the existing multiphase theory based trajectory generation methodologies.A novel adaptive vector field control law is then designed to accomplish the lateral tracking maneuvers.The key feature of the proposed method is that the complete lateral closed-loop control,including position and heading angle loops,is achieved in the presence of disturbances and dynamic uncertainties.The homing control with high landing accuracy is therefore achieved.Simulation and hardware-in-loop testing results are finally presented to validate the proposed method’s effectiveness compared to a conventional homing control scheme.
文摘为实现欠驱动四旋翼飞行器的位置及偏航渐近跟踪,本文基于浸入和不变技术(immersion and invariance,I&I)提出非线性跟踪控制方法.针对四旋翼飞行器的欠驱动特性,设计了内/外环分别为姿态和位置的双闭环结构,并采用浸入和不变技术构建内/外环的稳定跟踪控制器,利用李雅普诺夫定理保证闭环系统的渐近稳定性.最后,在MATLAB/Simulink环境下进行数值仿真,验证了I&I控制方案的有效性.
基金the Funds of National Science of China[grant number 61973146]in part by the Distinguished Young Scientifific Research Talents Plan in Liaoning Province[grant number XLYC1907077]in part by the Taishan Scholar Project of Shandong Province ofChina[grant number tsqn201909097].
文摘This paper is concerned with the adaptive tracking control problem of nonlinear time-varyingsystems. Based on the backstepping technology, an event-based prescribed performance controlscheme is developed. And the time-varying uncertainties of the system are handled byutilising bound estimation method. The proposed controller not only ensures the prescribedtracking performance, but also reduces the communication burden. By using Lyapunov stabilityanalysis, it is proven that all of the closed-loop signals are bounded, and the tracking errorcan converge to zero. Simultaneously, Zeno behaviour is excluded. Finally, the simulation resultsare utilised to illustrate the effectiveness of the proposed adaptive control scheme.
