We propose a new method for robust adaptive backstepping control of nonlinear systems with parametric uncertainties and disturbances in the strict feedback form. The method is called dynamic surface control. Traditio...We propose a new method for robust adaptive backstepping control of nonlinear systems with parametric uncertainties and disturbances in the strict feedback form. The method is called dynamic surface control. Traditional backstepping algorithms require repeated differentiations of the modelled nonlinearities. The addition of n first order low pass filters allows the algorithm to be implemented without differentiating any model nonlinearities, thus ending the complexity arising due to the 'explosion of terms' that makes other methods difficult to implement in practice. The combined robust adaptive backstepping/first order filter system is proved to be semiglobally asymptotically stable for sufficiently fast filters by a singular perturbation approach. The simulation results demonstrate the feasibility and effectiveness of the controller designed by the method.展开更多
To study the application of the generalized predictive adaptive control algorithm in missile control system, the algorithm is presented based on the recursive least square estimation, and a controller of the pitch c...To study the application of the generalized predictive adaptive control algorithm in missile control system, the algorithm is presented based on the recursive least square estimation, and a controller of the pitch channel of a missile is designed by using this algorithm. The simulations verify that the designed controller can meet the demands of the task well.展开更多
To achieve excellent tracking accuracy,a coarse-fine dual-stage control system is chosen for inertially stabilized platform.The coarse stage is a conventional inertially stabilized platform,and the fine stage is a sec...To achieve excellent tracking accuracy,a coarse-fine dual-stage control system is chosen for inertially stabilized platform.The coarse stage is a conventional inertially stabilized platform,and the fine stage is a secondary servo mechanism to control lens motion in the imaging optical path.Firstly,the dual-stage dynamics is mathematically modeled as a coupling multi-input multi-output(MIMO)control system.Then,by incorporating compensation of adaptive model to deal with parameter variations and nonlinearity,a systematic robust H∞control scheme is designed,which can achieve good tracking performance,as well as improve system robustness against model uncertainties.Lyapunov stability analysis confirmed the stability of the overall control system.Finally,simulation and experiment results are provided to demonstrate the feasibility and effectiveness of the proposed control design method.展开更多
In this note, a robust adaptive control scheme is proposed for a class of nonlinear systems that have unknown multi-plicative terms. Unlike previous results, except for the unknown control directions, we do not requir...In this note, a robust adaptive control scheme is proposed for a class of nonlinear systems that have unknown multi-plicative terms. Unlike previous results, except for the unknown control directions, we do not require a priori bounds on the unknown parameters. We also allow the unknown parameters to be time-varying provided that they are bounded. Our proposed robust adaptive controller is designed to identify on-line the unknown control directions and is a switching type controller, in which the controller parameters are tuned in a switching manner via a switching logic. Global stability of the closed-loop systems have been proved.展开更多
A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network(ATNN) and neural state feedback c...A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network(ATNN) and neural state feedback compensation are used, and then to compensate the approximation error and external disturbance, a robust control term is employed. By Lyapunov stability analysis for the closed-loop system, it is proven that tracking errors asymptotically converge to zero. Moreover, an observer is designed to estimate the system states because all the states may not be available for measurements. Furthermore, the adaptation laws of neural networks and the robust controller are given based on the Lyapunov stability theory. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed control method. Finally, two simulation examples show that the proposed method exhibits strong robustness, fast response and small tracking error, even for the non-affine nonlinear system with external disturbance, which confirms the effectiveness of the proposed approach.展开更多
文摘We propose a new method for robust adaptive backstepping control of nonlinear systems with parametric uncertainties and disturbances in the strict feedback form. The method is called dynamic surface control. Traditional backstepping algorithms require repeated differentiations of the modelled nonlinearities. The addition of n first order low pass filters allows the algorithm to be implemented without differentiating any model nonlinearities, thus ending the complexity arising due to the 'explosion of terms' that makes other methods difficult to implement in practice. The combined robust adaptive backstepping/first order filter system is proved to be semiglobally asymptotically stable for sufficiently fast filters by a singular perturbation approach. The simulation results demonstrate the feasibility and effectiveness of the controller designed by the method.
文摘To study the application of the generalized predictive adaptive control algorithm in missile control system, the algorithm is presented based on the recursive least square estimation, and a controller of the pitch channel of a missile is designed by using this algorithm. The simulations verify that the designed controller can meet the demands of the task well.
基金Project (61174203) supported by the National Natural Science Foundation of China
文摘To achieve excellent tracking accuracy,a coarse-fine dual-stage control system is chosen for inertially stabilized platform.The coarse stage is a conventional inertially stabilized platform,and the fine stage is a secondary servo mechanism to control lens motion in the imaging optical path.Firstly,the dual-stage dynamics is mathematically modeled as a coupling multi-input multi-output(MIMO)control system.Then,by incorporating compensation of adaptive model to deal with parameter variations and nonlinearity,a systematic robust H∞control scheme is designed,which can achieve good tracking performance,as well as improve system robustness against model uncertainties.Lyapunov stability analysis confirmed the stability of the overall control system.Finally,simulation and experiment results are provided to demonstrate the feasibility and effectiveness of the proposed control design method.
基金Project supported by the National Natural Science Foundation ofChina (No. 60474010), and the Scientific Research Foundation for theReturned Chinese Scholars, State Education Ministry, China
文摘In this note, a robust adaptive control scheme is proposed for a class of nonlinear systems that have unknown multi-plicative terms. Unlike previous results, except for the unknown control directions, we do not require a priori bounds on the unknown parameters. We also allow the unknown parameters to be time-varying provided that they are bounded. Our proposed robust adaptive controller is designed to identify on-line the unknown control directions and is a switching type controller, in which the controller parameters are tuned in a switching manner via a switching logic. Global stability of the closed-loop systems have been proved.
基金Project(61433004)suppouted by the National Natural Science Foundation of China
文摘A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network(ATNN) and neural state feedback compensation are used, and then to compensate the approximation error and external disturbance, a robust control term is employed. By Lyapunov stability analysis for the closed-loop system, it is proven that tracking errors asymptotically converge to zero. Moreover, an observer is designed to estimate the system states because all the states may not be available for measurements. Furthermore, the adaptation laws of neural networks and the robust controller are given based on the Lyapunov stability theory. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed control method. Finally, two simulation examples show that the proposed method exhibits strong robustness, fast response and small tracking error, even for the non-affine nonlinear system with external disturbance, which confirms the effectiveness of the proposed approach.
