A new scheme of adaptive control is proposed for a class of linear time-invariant（ LTI） dynamical systems,especially in aerospace,with matched parametric uncertainties and input constraints. Based on a typical and c...A new scheme of adaptive control is proposed for a class of linear time-invariant（ LTI） dynamical systems,especially in aerospace,with matched parametric uncertainties and input constraints. Based on a typical and conventional direct model reference adaptive control scheme,various modifications have been employed to achieve the goal. ＂C omposite model reference adaptive control＂of higher performance is seam-lessly combined with ＂positive μ-mod＂,which consequently results in a smooth tracking trajectory despite of the input constraints. In addition,bounded-gain forgetting is utilized to facilitate faster convergence of parameter estimates. The stability of the closed-loop systemcan be guaranteed by using Lyapunov theory.The merits and effectiveness of the proposed method are illustrated by a numerical example of the longitudinal dynamical systems of a fixed-wing airplane.展开更多
In this paper,an active fault accommodate strategy is proposed for the plant in the presence of actuator fault and input constraints,which is a combination of a direct adaptive control algorithm with multiple model sw...In this paper,an active fault accommodate strategy is proposed for the plant in the presence of actuator fault and input constraints,which is a combination of a direct adaptive control algorithm with multiple model switching.The μ-modification is introduced in the model reference architecture to construct the adaptive controller.The proof of stability is based on the candidate Lyapunov function,while appropriate switching of multiple models guarantees asymptotic tracking of the system states and the boundedness of all signals.Simulation results illustrate the efficiency of the proposed method.展开更多
In this paper,we present an optimal neuro-control scheme for continuous-time(CT)nonlinear systems with asymmetric input constraints.Initially,we introduce a discounted cost function for the CT nonlinear systems in ord...In this paper,we present an optimal neuro-control scheme for continuous-time(CT)nonlinear systems with asymmetric input constraints.Initially,we introduce a discounted cost function for the CT nonlinear systems in order to handle the asymmetric input constraints.Then,we develop a Hamilton-Jacobi-Bellman equation(HJBE),which arises in the discounted cost optimal control problem.To obtain the optimal neurocontroller,we utilize a critic neural network(CNN)to solve the HJBE under the framework of reinforcement learning.The CNN's weight vector is tuned via the gradient descent approach.Based on the Lyapunov method,we prove that uniform ultimate boundedness of the CNN's weight vector and the closed-loop system is guaranteed.Finally,we verify the effectiveness of the present optimal neuro-control strategy through performing simulations of two examples.展开更多
The attitude synchronization problem for multiple spacecraft with input constraints is investigated in this paper. Two distributed control laws are presented and analyzed. First, by intro- ducing bounded function, a d...The attitude synchronization problem for multiple spacecraft with input constraints is investigated in this paper. Two distributed control laws are presented and analyzed. First, by intro- ducing bounded function, a distributed asymptotically stable control law is proposed. Such a con- trol scheme can guarantee attitude synchronization and the control inputs of each spacecraft can be a priori bounded regardless of the number of its neighbors. Then, based on graph theory, homoge- neous method, and Lyapunov stability theory, a distributed finite-time control law is designed. Rig- orous proof shows that attitude synchronization of multiple spacecraft can be achieved in finite time, and the control scheme satisfies input saturation requirement. Finally, numerical simulations are presented to demonstrate the effectiveness and feasibility of the oroDosed schemes.展开更多
This work studies the problem of control design for linear systems with input saturation.It is well known that integral quadratic constraints(IQC) can be used to describe input saturation and that the use of IQC in an...This work studies the problem of control design for linear systems with input saturation.It is well known that integral quadratic constraints(IQC) can be used to describe input saturation and that the use of IQC in analysis can lead to less conservative performance bound and larger domain of attraction.In this work,it is shown that a class of commonly used IQCs may not help in control synthesis.That is,the use of these IQCs does not enlarge the guaranteed domain of performance for synthesis.展开更多
To solve the problem of attitude tracking of a rigid spacecraft with an either known or measurable desired attitude trajectory, three types of time-varying sliding mode controls are introduced under consideration of c...To solve the problem of attitude tracking of a rigid spacecraft with an either known or measurable desired attitude trajectory, three types of time-varying sliding mode controls are introduced under consideration of control input constraints. The sliding surfaces of the three types initially pass arbitrary initial values of the system, and then shift or rotate to reach predetermined ones. This way, the system trajectories are always on the sliding surfaces, and the system work is guaranteed to have robustness against parameter uncertainty and external disturbances all the time. The controller parameters are optimized by means of genetic algorithm to minimize the index consisting of the weighted index of squared error （ISE） of the system and the weighted penalty term of violation of control input constraint. The stability is verified with Lyapunov method. Compared with the conventional sliding mode control, simulation results show the proposed algorithm having better robustness against inertia matrix uncertainty and external disturbance torques.展开更多
Automotive engine control has been continuously improved due to the strong demands from the society and the market since introducing electronic controls but not always following control theories. Therefore, it is not ...Automotive engine control has been continuously improved due to the strong demands from the society and the market since introducing electronic controls but not always following control theories. Therefore, it is not easy for researchers from academia and even engineers from the automotive industry to grasp the whole aspect of engine control. To encounter the issue, important features of engine control are extracted and generalized from the standpoint of control engineering. Comparisons of the control and model predictive control （MPC） showed an outstanding performance of the control generalized from engine controls and how to apply MPC in the framework.展开更多
A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and th...A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and the orthogonalleast square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented. This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.展开更多
针对机械臂在运行过程中因输入输出受限、外界未知干扰以及自身动力学参数不确定性导致控制精度低和抗干扰能力差的问题,提出基于时变正切型障碍李雅普诺夫函数的自适应反步滑模控制方法(adaptive backstepping sliding mode control fo...针对机械臂在运行过程中因输入输出受限、外界未知干扰以及自身动力学参数不确定性导致控制精度低和抗干扰能力差的问题,提出基于时变正切型障碍李雅普诺夫函数的自适应反步滑模控制方法(adaptive backstepping sliding mode control for time-varying tangent barrier Lyapunov,TTBLF-ABSMC)。首先,通过设计饱和补偿系统解决输入受限问题,提高控制系统的稳定性;其次,将外界未知干扰与自身动力学参数不确定性视为复合干扰,设计反馈自适应律对其做出准确估计;同时,采用时变正切型障碍李雅普诺夫函数将位置误差和速度误差限制在时变范围内;最后,通过李雅普诺夫理论分析证明闭环控制系统是有界稳定的。仿真实验结果表明:与采取时变对数型障碍李雅普诺夫函数的控制方法相比,该控制方法使机械臂关节1、2的跟踪误差分别降低了58%、33%;相比于未考虑输入受限方法,该控制方法在关节1、2的轨迹跟踪响应速度分别提升了69%、50%,有效的提高了系统的控制精度和抗干扰能力。展开更多
基金Supported by Deep Exploration Technology and Experimentation Project(201311194-04)
文摘A new scheme of adaptive control is proposed for a class of linear time-invariant（ LTI） dynamical systems,especially in aerospace,with matched parametric uncertainties and input constraints. Based on a typical and conventional direct model reference adaptive control scheme,various modifications have been employed to achieve the goal. ＂C omposite model reference adaptive control＂of higher performance is seam-lessly combined with ＂positive μ-mod＂,which consequently results in a smooth tracking trajectory despite of the input constraints. In addition,bounded-gain forgetting is utilized to facilitate faster convergence of parameter estimates. The stability of the closed-loop systemcan be guaranteed by using Lyapunov theory.The merits and effectiveness of the proposed method are illustrated by a numerical example of the longitudinal dynamical systems of a fixed-wing airplane.
基金supported by the Aeronautics Science Foundation of China(No.2007ZC52039)the National Natural Science Foundation of China(No.90816023)
文摘In this paper,an active fault accommodate strategy is proposed for the plant in the presence of actuator fault and input constraints,which is a combination of a direct adaptive control algorithm with multiple model switching.The μ-modification is introduced in the model reference architecture to construct the adaptive controller.The proof of stability is based on the candidate Lyapunov function,while appropriate switching of multiple models guarantees asymptotic tracking of the system states and the boundedness of all signals.Simulation results illustrate the efficiency of the proposed method.
基金supported by the National Natural Science Foundation of China(61973228,61973330)
文摘In this paper,we present an optimal neuro-control scheme for continuous-time(CT)nonlinear systems with asymmetric input constraints.Initially,we introduce a discounted cost function for the CT nonlinear systems in order to handle the asymmetric input constraints.Then,we develop a Hamilton-Jacobi-Bellman equation(HJBE),which arises in the discounted cost optimal control problem.To obtain the optimal neurocontroller,we utilize a critic neural network(CNN)to solve the HJBE under the framework of reinforcement learning.The CNN's weight vector is tuned via the gradient descent approach.Based on the Lyapunov method,we prove that uniform ultimate boundedness of the CNN's weight vector and the closed-loop system is guaranteed.Finally,we verify the effectiveness of the present optimal neuro-control strategy through performing simulations of two examples.
基金supported by the Natural Science Foundation of Heilongjiang Province (No. F201326)
文摘The attitude synchronization problem for multiple spacecraft with input constraints is investigated in this paper. Two distributed control laws are presented and analyzed. First, by intro- ducing bounded function, a distributed asymptotically stable control law is proposed. Such a con- trol scheme can guarantee attitude synchronization and the control inputs of each spacecraft can be a priori bounded regardless of the number of its neighbors. Then, based on graph theory, homoge- neous method, and Lyapunov stability theory, a distributed finite-time control law is designed. Rig- orous proof shows that attitude synchronization of multiple spacecraft can be achieved in finite time, and the control scheme satisfies input saturation requirement. Finally, numerical simulations are presented to demonstrate the effectiveness and feasibility of the oroDosed schemes.
文摘This work studies the problem of control design for linear systems with input saturation.It is well known that integral quadratic constraints(IQC) can be used to describe input saturation and that the use of IQC in analysis can lead to less conservative performance bound and larger domain of attraction.In this work,it is shown that a class of commonly used IQCs may not help in control synthesis.That is,the use of these IQCs does not enlarge the guaranteed domain of performance for synthesis.
文摘To solve the problem of attitude tracking of a rigid spacecraft with an either known or measurable desired attitude trajectory, three types of time-varying sliding mode controls are introduced under consideration of control input constraints. The sliding surfaces of the three types initially pass arbitrary initial values of the system, and then shift or rotate to reach predetermined ones. This way, the system trajectories are always on the sliding surfaces, and the system work is guaranteed to have robustness against parameter uncertainty and external disturbances all the time. The controller parameters are optimized by means of genetic algorithm to minimize the index consisting of the weighted index of squared error （ISE） of the system and the weighted penalty term of violation of control input constraint. The stability is verified with Lyapunov method. Compared with the conventional sliding mode control, simulation results show the proposed algorithm having better robustness against inertia matrix uncertainty and external disturbance torques.
文摘Automotive engine control has been continuously improved due to the strong demands from the society and the market since introducing electronic controls but not always following control theories. Therefore, it is not easy for researchers from academia and even engineers from the automotive industry to grasp the whole aspect of engine control. To encounter the issue, important features of engine control are extracted and generalized from the standpoint of control engineering. Comparisons of the control and model predictive control （MPC） showed an outstanding performance of the control generalized from engine controls and how to apply MPC in the framework.
基金This Project was supported by the National Natural Science Foundation of China (60374037 and 60574036)the Opening Project Foundation of National Lab of Industrial Control Technology (0708008).
文摘A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and the orthogonalleast square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented. This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.
文摘针对机械臂在运行过程中因输入输出受限、外界未知干扰以及自身动力学参数不确定性导致控制精度低和抗干扰能力差的问题,提出基于时变正切型障碍李雅普诺夫函数的自适应反步滑模控制方法(adaptive backstepping sliding mode control for time-varying tangent barrier Lyapunov,TTBLF-ABSMC)。首先,通过设计饱和补偿系统解决输入受限问题,提高控制系统的稳定性;其次,将外界未知干扰与自身动力学参数不确定性视为复合干扰,设计反馈自适应律对其做出准确估计;同时,采用时变正切型障碍李雅普诺夫函数将位置误差和速度误差限制在时变范围内;最后,通过李雅普诺夫理论分析证明闭环控制系统是有界稳定的。仿真实验结果表明:与采取时变对数型障碍李雅普诺夫函数的控制方法相比,该控制方法使机械臂关节1、2的跟踪误差分别降低了58%、33%;相比于未考虑输入受限方法,该控制方法在关节1、2的轨迹跟踪响应速度分别提升了69%、50%,有效的提高了系统的控制精度和抗干扰能力。
