Adaptive Output‐Feedback Control of Nonlinear Systems with Multiple Uncertainties

This paper addresses the global stabilization via adaptive output‐feedback for a class of uncertain nonlinear systems. Remarkably, the systems under investigation are with multiple uncertainties: unknown control directions, unknown growth rates and unknown input bias, and can be used to describe more physical plants. Multiple uncertainties, which usually cannot be compensated by a sole compensation technique, may give rise to big technical difficulty for controller design. To overcome such difficulty and to achieve the global stabilization, a new adaptive output‐feedback scheme is proposed in this paper, by flexibly combining Nussbaum‐type function, tuning function technique and extended state observer. It is shown that, under the designed controller, the system states globally converge to zero. A simulation example on non‐zero set‐point regulation is given to demonstrate the effectiveness of the theoretical results.     

Global Output‐Feedback Stabilization for Stochastic Nonlinear Systems with Function Control Coefficients

This paper investigates the global output‐feedback stabilization for a class of stochastic nonlinear systems with function control coefficients. Notably, the systems in question possess control coefficients that are functions of output, rather than constants; hence, they are different from the existing literature on stochastic stabilization. To solve the control problem, an appropriate reduced‐order observer is introduced to reconstruct the unmeasured system states before a smooth output‐feedback controller is designed using the backstepping method, which guarantees that the closed‐loop system is globally asymptotically stable in probability. This paper combines the related results in the deterministic and stochastic setting and gives the first treatment on the global output‐feedback stabilization for the stochastic nonlinear systems with function control coefficients. A simulation example is given also to illustrate the effectiveness of the proposed approach.     

NN‐Based Output‐Feedback Control for Stochastic Nonlinear Systems with Unknown Control Directions

This paper addresses the neural network‐based output‐feedback control problem for a class of stochastic nonlinear systems with unknown control directions. The restrictions on the drift and diffusion terms are removed and the conditions on unknown control directions are relaxed. By introducing a proper coordinate transformation, and combining dynamic surface control (DSC) technique with radial basis function neural network (RBF NN) approximation approach, we construct an adaptive output‐feedback controller to guarantee the closed‐loop system to be mean square semi‐globally uniformly ultimately bounded (M‐SGUUB). A simulation example demonstrates the effectiveness of the proposed scheme.     

Global Practical Tracking for Nonlinear Systems With More Unknowns via Adaptive Output‐Feedback

This paper investigates the global practical tracking via adaptive output‐feedback for a class of uncertain nonlinear systems. Essentially different from the closely related literature, the system under investigation possesses unknown time‐varying control coefficients and a polynomial‐of‐output growth rate, and meanwhile, the system nonlinearities and the reference signal allow serious unknowns. For this, an adaptive observer is designed to reconstruct the system unmeasured states, where a new dynamic gain is introduced to compensate the serious unknowns in the system nonlinearities and the reference signal. Based on this and by backstepping technique, an adaptive output‐feedback controller is successfully designed, such that all the states of the closed‐loop system are bounded, and the tracking error will be prescribed sufficiently small after a finite time. A numerical simulation is provided to demonstrate the effectiveness of the proposed method.     

Robust Adaptive Output‐Feedback Dynamic Surface Control of a Class of Nonlinear Systems with Unmodeled Dynamics

This paper presents a robust adaptive output‐feedback dynamic surface control (DSC) for a class of nonlinear systems with unmodeled dynamics or/and uncertain time‐varying disturbances. Based on traditional K‐filters, the proposed adaptive DSC scheme is able to guarantee semi‐global stability of the closed‐loop system without applying any approximation techniques. The adaptive law is necessary only at the first design step, which, together with the introduction of a first‐order filter at each design step, makes the control law easy to implement. Moreover, it is shown that the tracking error can converge to an arbitrarily small residual set by adjusting only one design parameter.     

非线性系统的间接自适应模糊输出反馈监督控制

In this paper, an indirect adaptive fuzzy output feedback controller with supervisory mode for a class of unknown nonlinear systems is developed. The proposed approach does not need the availability of the state variables, moreover, a supervisory controller is appended to the adaptive fuzzy controller to force the state to be within the constraint set. Therefore, if the adaptive fuzzy controller cannot maintain the stability, the supervisory controller starts to work to guarantee stability. On the other hand, if the adaptive fuzzy controller works well, the supervisory controller will be deactivated. The overall adaptive fuzzy control scheme guarantees the stability of the whole closed-loop systems. The simulation results confirm the effectiveness of the proposed method.     

Indirect Adaptive Fuzzy Output Feedback Control with Supervisory Controller for Uncertain Nonlinear Systems

In this paper, an indirect adaptive fuzzy output feedback controller with supervisory mode for a class of unknown nonlinear systems is developed. The proposed approach does not need the availability of the state variables, moreover, a supervisory controller is appended to the adaptive fuzzy controller to force the state to be within the constraint set. Therefore, if the adaptive fuzzy controller cannot maintain the stability, the supervisory controller starts to work to guarantee stability. On the other hand, if the adaptive fuzzy controller works well, the supervisory controller will be de-activated. The overall adaptive fuzzy control scheme guarantees the stability of the whole closed-loop systems. The simulation results confirm the effectiveness of the proposed method.     

一类确定性多变量非线性系统的自适应控制

本文研究了可用确定性多变量Hammerstein模型描述的一类多变量非线性系统的自适应控制,基于对一步最优控制律所产生的闭环系统的稳定性条件的分析,提出了一种适用于线性部分为具有任意关联矩阵的非最小相位系统的简单次优控制律及相应的直接自适应控制算法,并证明了算法的全局收敛性.     

Observer‐Based Adaptive Output Feedback Fault Tolerant Control for Nonlinear Hydro‐Turbine Governing System with State Delay

This paper focuses on the problem of adaptive output feedback fault tolerant control for a nonlinear hydro‐turbine governing system. A dynamic mathematical model of the system is established, which aims to investigate the dynamic performance of the model under servomotor delay and actuator faults. Then, a fault estimation adaptive observer is proposed to achieve online real‐time diagnosis of system faults. Based on the online fault estimation information, an observer‐based adaptive output feedback fault tolerant controller is designed. Furthermore, under reasonable assumptions, the results demonstrate that the closed‐loop control system can achieve global asymptotic stability by Lyapunov function. Finally, the numerical simulation results are presented to indicate the satisfaction control effectiveness of the proposed scheme.     

Adaptive Dynamic Output Feedback Stabilisation of Nonlinear Systems

An adaptive dynamical output feedback controller is introduced for a class of nonlinear non‐minimum phase systems. This adaptive controller achieves practical stabilisation, that means the output will asymptotically tend to a pre‐specified neighbourhood of the origin. In case of linear systems, we can even prove adaptive stabilisation.     

Robust Adaptive Dynamic Surface Control for a Class of MIMO Nonlinear Systems with Unknown Non‐Symmetric Dead‐Zone

This paper is devoted to adaptive output tracking for a class of multi‐input multi‐output nonlinear systems with unknown non‐symmetric dead‐zone. With the aid of a matrix factorization and a similarity transformation, a robust adaptive dynamic surface control scheme is proposed and the difficulty caused by the control gain matrix and the dead‐zone is circumvented. By introducing a surface error modification and an initialization technique, we show that the performance of the tracking errors can be guaranteed. Moreover, the proposed scheme contains only one updated parameter at each design step, which significantly reduces the computational burden. It is proven that all signals of the closed‐loop system are semi‐globally uniformly bounded. Simulation results on coupled inverted double pendulums are presented to illustrate the effectiveness of the proposed scheme.     

非线性系统的一种加权自适应控制方法

非线性系统的一种加权自适应控制方法１）许向阳祝和云（浙江大学工业控制研究所杭州３１００２７）关键词自适应控制,非线性系统,间隙非线性．１）国家级工业控制技术重点实验室资助课题．收稿日期１９９５－０３－０７１引言由于非线性系统的多样性,不能用统一的模型...     

一类非线性系统的间接自适应输出反馈模糊控制

针对一类未知非线性系统,提出一种输出反馈控制方法。首先在假设系统状态已知的情况下设计状态反馈控制器,实现跟踪性能。然后在系统状态不完全可测的情况下,通过设计高增益观测器对系统的状态进行估计,实现输出反馈控制器设计。最后证明所设计的输出反馈控制器可获得状态反馈控制器所取得的最大最小问题的跟踪性能。     

一类多变量非线性动态系统的模糊自适应控制

对一类非线性多变量未知动态系统，提出了一种模糊处在适应控制策略。证明了该控制算法能保证闭环系统稳定，跟踪误差收敛。     

非线性系统的模糊自适应输出反馈控制

针对一类未知非线性系统,考虑系统状态不完全可测的情况,利用Lyapunov综合方法设计了一种基于高增益观测器的模糊鲁棒自适应输出反馈控制器,并证明在一定条件下,所设计的输出反馈控制器能获得状态反馈控制器的性能。     

一类非线性系统的自适应观测器

This paper investigates the means to design the ob- server for a class of nonlinear systems with Lipschitz conditions and unknown parameters.A new design approach of full-order state adaptive observer is proposed.The constructed observer could guarantee the error of state and the error of parameter estimation to asymptotically converge to zero.Furthermore,a numerical example is provided to verify the effectiveness of the observer.     

Distributed Observer‐based Stabilization of Nonlinear Multi‐agent Systems with Sampled‐data Control

In this paper, the distributed observer‐based stabilization problem of multi‐agent systems under a directed graph is investigated. Distributed observer‐based control protocol with sampled‐data information is proposed. The dynamics of each agent contain a nonlinear part, which is supposed to be general Lipschitz. In order to stabilize the states of the whole network, all the nodes utilize the relative output estimation error at sampling instants and only a small fraction of nodes use the absolute output estimation error additionally. By virtue of the input‐to‐state stability (ISS) property and the Lyapunov stability theory, an algorithm to design the control gain matrix, observer gain matrix, coupling strength as well as the allowable sampling period are derived. The conditions are in the form of LMIs and algebraic inequality, which are simple in form and easy to verify. Some further discussions about the solvability of obtained linear matrix inequalities (LMIs) are also given. Lastly, an example is simulated to further validate the obtained results.     

随机非线性系统鲁棒自适应反馈控制器的积分反推方法设计

考虑了一类随机非线性系统的鲁棒自适应控制问题.采用Ito随机微分方程描述系统,进而在概率意义下研究系统的鲁棒稳定性.应用积分反推(backstepping)方法,系统地给出了设计状态反馈及输出反馈鲁棒自适应控制器的方法.同时构造出了适当形式的四次型的自适应控制Lyapunov函数(CLF).     

一类非线性系统的自适应观测器

研究一类非线性系统的观测器设计方法, 这类非线性系统满足 Lipschitz 条件且含有未知参数. 提出了全状态自适应观测器设计的新方法. 构造的观测器能保证状态估计误差及参数估计误差渐近收敛于零. 文中给出数值例验证了观测器的有效性.