Adaptive stabilization of a class of uncertain switched nonlinear systems with backstepping control

In this paper, we focus on the problem of adaptive stabilization for a class of uncertain switched nonlinear systems, whose non-switching part consists of feedback linearizable dynamics. The main result is that we propose adaptive controllers such that the considered switched systems with unknown parameters can be stabilized under arbitrary switching signals. First, we design the adaptive state feedback controller based on tuning the estimations of the bounds on switching parameters in the transformed system, instead of estimating the switching parameters directly. Next, by incorporating some augmented design parameters, the adaptive output feedback controller is designed. The proposed approach allows us to construct a common Lyapunov function and thus the closed-loop system can be stabilized without the restriction on dwell-time, which is needed in most of the existing results considering output feedback control. A numerical example and computer simulations are provided to validate the proposed controllers.     

基于故障诊断观测器的输出反馈容错控制设计

针对自适应故障诊断观测器需要误差系统满足苛刻的严格正实条件(Strictly positive real, SPR)和难于处理输出存在扰动的不确定性系统等问题, 提出了一种新型的增广故障诊断观测器的设计方法, 不仅显著地拓宽了自适应故障诊断观测器的适用范围, 而且其具有处理系统扰动的良好性能. 在故障估计的基础上, 提出了动态输出反馈容错控制的设计方法, 避免了基于观测器的状态反馈容错控制的设计难点. 同时, 故障诊断观测器和输出反馈容错控制是分开设计的, 并且又考虑了各自的性能, 简化了设计过程. 最后, 通过仿真实验验证了所提方法的有效性.     

Output Feedback Adaptive Neural Control Without Seeking SPR Condition

For output‐feedback adaptive control of affine nonlinear systems based on feedback linearization and function approximation, the observation error dynamics usually should be augmented by a low‐pass filter to satisfy a strictly positive real (SPR) condition so that output feedback can be realized. Yet, this manipulation results in filtering basis functions of approximators, which makes the order of the controller dynamics very large. This paper presents a novel output‐feedback adaptive neural control (ANC) scheme to avoid seeking the SPR condition. A saturated output‐feedback control law is introduced based on a state‐feedback indirect ANC structure. An adaptive neural network (NN) observer is applied to estimate immeasurable system state variables. The output estimation error rather than the basis functions is filtered and the filter output is employed to update NNs. Under given initial conditions and sufficient control parameter constraints, it is proved that the closed‐loop system is uniformly ultimately bounded stable in the sense that both the state estimation errors and the tracking errors converge to small neighborhoods of zero. An illustrative example is provided to demonstrate the effectiveness of this approach.     

Discrete time output feedback sliding mode tracking control for systems with uncertainties

This paper describes a method for designing discrete time static output feedback sliding mode tracking controllers for uncertain systems that are not necessarily minimum phase or of relative degree one. In this work, a procedure for realizing discrete time controllers via a particular set of extended outputs is presented for systems with uncertainties. The conditions for existence of a sliding manifold guaranteeing a stable sliding motion are given. A procedure to synthesize a control law that minimizes the effect of the disturbance on the sliding mode dynamics and the augmented outputs is given. The proposed control law is then applied to a benchmark aircraft problem taken from the literature that represents the lateral dynamics of a F‐14 aircraft under powered approach. Copyright © 2013 John Wiley & Sons, Ltd.     

An output feedback sliding mode control strategy for MIMO systems of arbitrary relative degree

This paper develops an output feedback sliding mode controller for multi‐input multi‐output (MIMO) systems of any relative degree. A minimal set of outputs and output derivatives are identified to determine an augmented system which is relative degree 1, and a robust sliding mode differentiator is presented as the means to construct the extended output signal. It is shown that the transmission zeroes of the original plant appear directly in the reduced order sliding mode dynamics relating to the augmented system. A super twisting control algorithm is shown to provide robust control performance. Simulation results for a rationalized helicopter model taken from the literature are used to demonstrate the attraction of the approach. Copyright © 2010 John Wiley & Sons, Ltd.     

Design and Experimental Evaluation of a Discrete‐time ASPR‐based Adaptive Output Feedback Control System Using FRIT

This paper describes the design of an adaptive output feedback control system in discrete‐time, based on almost strictly positive real (ASPR)‐ness with a feedforward input. It is well‐known that an adaptive output feedback control system based on ASPR conditions can achieve asymptotic stability via a constant feedback gain. Unfortunately, most realistic systems are not ASPR because of the severe conditions. The introduction of a parallel feedforward compensator (PFC) is an efficient way to alleviate such restrictions. However, the problem remains that there exists a steady state error between the output of the augmented system and the output of the original system. The proposed scheme provides a strategy wherein the feedforward input is utilized such that the steady state error is removed. Furthermore, the fictitious reference iterative tuning (FRIT) approach is employed to determine the control parameters using one‐shot input/output experimental data directly, without prior information about the control system. This paper explains how the FRIT approach is applied in designing an adaptive output feedback control system. The effectiveness of the proposed scheme is confirmed experimentally, by using a motor application.     

Receding horizon output feedback control for constrained uncertain systems using periodic invariance

In this article, we consider a receding horizon output feedback control (RHOC) method for linear discrete-time systems with polytopic model uncertainties and input constraints. First, we derive a set of estimator gains and then we obtain, on the basis of the periodic invariance, a series of state feedback gains stabilising the augmented output feedback system with these estimator gains. These procedures are formulated as linear matrix inequalities. An RHOC strategy is proposed based on these state feedback and state estimator gains in conjunction with their corresponding periodically invariant sets. The proposed RHOC strategy enhances the performance in comparison with the case in which static periodic gains are used, and increases the size of the stabilisable region by introducing a degree of freedom to steer the augmented state into periodically invariant sets.     

Robust adaptive control of uncertain non-linear systems using neural networks

This paper presents a robust adaptive output feedback control design method for uncertain non-affine non-linear systems, which does not rely on state estimation. The approach is applicable to systems with unknown but bounded dimensions and with known relative degree. A neural network is employed to approximate the unknown modelling error. In fact, a neural network is considered to approximate and adaptively make ineffective unknown plant non-linearities. An adaptive law for the weights in the hidden layer and the output layer of the neural network are also established so that the entire closed-loop system is stable in the sense of Lyapunov. Moreover, the robustness of the system against the approximation error of neural network is achieved with the aid of an additional adaptive robustifying control term. In addition, the tracking error is guaranteed to be uniformly and asymptotically stable, rather than uniformly ultimately bounded, by using this additional control term. The proposed control algorithm is relatively straightforward and no restrictive conditions on the design parameters for achieving the systems stability are required. The effectiveness of the proposed scheme is shown through simulations of a non-affine non-linear system with unmodelled dynamics, and is compared with a second-sliding mode controller.     

Output feedback preview tracking control for discrete-time polytopic time-varying systems

This paper focuses on the problem of static output feedback preview tracking control for discrete-time systems with time-varying parameters subject to a previewable reference signal. We develop a design method of a robust controller with integral and preview actions achieving robust tracking performance. First, an augmented error system including future information on reference signal is constructed by introducing two new related auxiliary variables to the original system state and input. This leads to a tracking problem being transformed into a regulator problem. Then, a previewable reference signal is fully utilised through reformulation of the output equation for the augmented error system while considering a static output feedback. Meanwhile, the static output preview control gains are solved explicitly by the proposed conditions. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method.     

Distributed adaptive output feedback tracking control for a class of uncertain nonlinear multi-agent systems

This paper addresses the distributed output feedback tracking control problem for multi-agent systems with higher order nonlinear non-strict-feedback dynamics and directed communication graphs. The existing works usually design a distributed consensus controller using all the states of each agent, which are often immeasurable, especially in nonlinear systems. In this paper, based only on the relative output between itself and its neighbours, a distributed adaptive consensus control law is proposed for each agent using the backstepping technique and approximation technique of Fourier series (FS) to solve the output feedback tracking control problem of multi-agent systems. The FS structure is taken not only for tracking the unknown nonlinear dynamics but also the unknown derivatives of virtual controllers in the controller design procedure, which can therefore prevent virtual controllers from containing uncertain terms. The projection algorithm is applied to ensure that the estimated parameters remain in some known bounded sets. Lyapunov stability analysis shows that the proposed control law can guarantee that the output of each agent synchronises to the leader with bounded residual errors and that all the signals in the closed-loop system are uniformly ultimately bounded. Simulation results have verified the performance and feasibility of the proposed distributed adaptive control strategy.     

Quantized output feedback stabilization of uncertain systems with input nonlinearities via sliding mode control

This paper is concerned with the quantized output feedback stabilization problem for a class of uncertain systems with nonsmooth nonlinearities in the actuator device via sliding mode control schemes. It is assumed that system signals are quantized before being transmitted through communication channels. First, a dynamical compensator is developed to estimate unmeasurable system state. Then a sliding surface, in the augmented space using the system output and the estimated state, is proposed, and an adaptive sliding mode control scheme with a static adjustment law of the quantization parameter is established. It is shown that the proposed quantized feedback control strategy is able to tackle parameter uncertainty, external disturbances, and nonsymmetric input nonlinearity simultaneously and guarantees the reachability of the sliding modes of the uncertain system. Finally, an example is given to verify the validity of the theoretical results. Copyright © 2012 John Wiley & Sons, Ltd.     

Neural-network control of nonaffine nonlinear system with zero dynamics by state and output feedback

This paper focuses on adaptive control of nonaffine nonlinear systems with zero dynamics using multilayer neural networks. Through neural network approximation, state feedback control is firstly investigated for nonaffine single-input-single-output (SISO) systems. By using a high gain observer to reconstruct the system states, an extension is made to output feedback neural-network control of nonaffine systems, whose states and time derivatives of the output are unavailable. It is shown that output tracking errors converge to adjustable neighborhoods of the origin for both state feedback and output feedback control.     

Adaptive fuzzy backstepping output feedback control for strict feedback nonlinear systems with unknown sign of high-frequency gain

In this paper, an adaptive fuzzy robust output feedback control approach is proposed for a class of SISO nonlinear strict-feedback systems with unknown sign of high-frequency gain and the unmeasured states. The nonlinear systems addressed in this paper are assumed to possess the unmodeled dynamics, dynamical disturbances and unknown nonlinear functions, where the unknown nonlinear functions are not linearly parameterized, and no prior knowledge of their bounds is available. In the recursive designing, fuzzy logic systems are used to approximate the unknown nonlinear functions, K-filters are designed to estimate the unmeasured states, and a dynamical signal and Nussbaum gain functions are introduced to handle the unmodeled dynamics and the unknown sign of the high-frequency gain, respectively. Based on Lyapunov function method, a stable adaptive fuzzy output feedback control scheme is developed. It is mathematically proved that the proposed adaptive fuzzy control approach can guarantee that all the signals of the closed-loop system are uniformly ultimately bounded, the output converges to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated by the simulation examples.     

Parallel Control for Optimal Tracking via Adaptive Dynamic Programming

This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems. Unlike existing optimal state feedback control, the control input of the optimal parallel control is introduced into the feedback system. However, due to the introduction of control input into the feedback system, the optimal state feedback control methods can not be applied directly. To address this problem, an augmented system and an augmented performance index function are proposed firstly. Thus, the general nonlinear system is transformed into an affine nonlinear system. The difference between the optimal parallel control and the optimal state feedback control is analyzed theoretically. It is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index function. Moreover, an adaptive dynamic programming (ADP) technique is utilized to implement the optimal parallel tracking control using a critic neural network (NN) to approximate the value function online. The stability analysis of the closed-loop system is performed using the Lyapunov theory, and the tracking error and NN weights errors are uniformly ultimately bounded (UUB). Also, the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference signals. Finally, the effectiveness of the developed optimal parallel control method is verified in two cases.      

一类非线性系统的加速度规划输出跟踪动态控制

针对一类严格反馈形式的非线性二阶多输入多输出系统,提出一种带有加速度规划的输出跟踪动态控制策略.引入一个代替时间变量的路径参数用以规划路径跟踪时的加速度,回避了设计内环加速度控制回路的常规方法,简化了控制器的设计过程.对二阶系统的控制项求导进行系统扩维,基于新的增广系统,设计了使系统输出收敛于期望路径的反馈线性化动态控制律.再对加速度跟踪误差基于梯度法设计更新律使其渐近收敛于零,最后通过调节期望加速度实现定常速度控制.理论分析表明,误差闭环系统一致渐近稳定,速度误差有界.动力定位船舶循迹控制仿真结果表明了所提出控制器的有效性.     

Adaptive tracking control for a class of uncertain pure‐feedback systems

This paper concerns the global output tracking for uncertain pure‐feedback systems. By adding an integrator, the original systems with nonaffine‐in‐control are transformed into the augmented affine‐in‐control systems. Through a novel change of coordinates, an adaptive control scheme is proposed via backstepping approach, which eliminates the need of overparametrization. It is proven that the proposed control scheme is sufficient to ensure the global asymptotic tracking, as well as the boundedness of all the closed‐loop signals. An illustrative example is provided to demonstrate the feasibility of the proposed method. Copyright © 2015 John Wiley & Sons, Ltd.     

An RBF neural network-based adaptive control for SISO linearisable nonlinear systems

An RBF neural network-based adaptive control is proposed for Single-Input and Single-Output (SISO) linearisable nonlinear systems in this paper. It is shown that a SISO nonlinear system is first linearised by using the differential geometric approach in the state space, and the linearised nonlinear system is then treated as a partially known system. The known dynamics are used to design a nominal feedback controller to stabilise the nominal system, and an adaptive RBF neural network-based compensator is then designed to compensate for the effects of uncertain dynamics. The main function of the RBF neural network in this work is to adaptively learn the upper bound of the system uncertainty, and the output of the neural network is then used to adaptively adjust the gain of the compensator so that the strong robustness with respect to unknown dynamics can be obtained, and the tracking error between the plant output and the desired reference signal can asymptotically converge to zero. A simulation example is performed in support of the proposed scheme.     

Neural network robust control of a 3-DOF hydraulic manipulator with asymptotic tracking

Multiaxial hydraulic manipulators are complicated systems with highly nonlinear dynamics and various modeling uncertainties, which hinders the development of high-performance controller. In this paper, a neural network feedforward with a robust integral of the sign of the error (RISE) feedback is proposed for high precise tracking control of hydraulic manipulator systems. The established nonlinear model takes three-axis dynamic coupling, hydraulic actuator dynamics, and nonlinear friction effects into consideration. A radial basis function neural network (RBFNN) is synthesized to approximate the uncertain system dynamics and external disturbance, which can greatly reduce the dependence on accurate system model. In addition, a continuous RISE feedback law is judiciously integrated to deal with the residual unknown dynamics. Since the major unknown dynamics can be estimated by the RBFNN and then compensated in the feedforward design, the high-gain feedback issue in RISE feedback control will be avoided. The proposed RISE-based neural network robust controller theoretically guarantees an excellent semi-global asymptotic stability. Comparative simulation is performed on a 3-DOF hydraulic manipulator, and the obtained results verify the effectiveness of the proposed controller.     

含未建模动态的非线性系统输出反馈DSC设计

针对一类含有未建模动态和未知控制增益符号的非线性系统,提出了一种输出反馈自适应跟踪控制方案.首先利用Kreisselmeier观测器实现了不可测状态的估计,在此基础上以回归设计方式设计了输出反馈动态面控制系统,通过引入Nussbaum函数解决了控制方向未知问题.该方案解克服了传统反推控制方法中的微分爆炸现象,并且所有未...     

Robust adaptive fuzzy backstepping output feedback tracking control for nonlinear system with dynamic uncertainties

In this paper, an adaptive fuzzy output feedback control approach based on backstepping design is proposed for a class of SISO strict feedback nonlinear systems with unmeasured states, nonlinear uncertainties, unmodeled dynamics, and dynamical disturbances. Fuzzy logic systems are employed to approximate the nonlinear uncertainties, and an adaptive fuzzy state observer is designed for the states estimation. By combining backstepping technique with the fuzzy adaptive control approach, a stable adaptive fuzzy...