Truncated adaptation design for decentralised neural dynamic surface control of interconnected nonlinear systems under input saturation

This paper studies decentralised neural adaptive control of a class of interconnected nonlinear systems, each subsystem is in the presence of input saturation and external disturbance and has independent system order. Using a novel truncated adaptation design, dynamic surface control technique and minimal-learning-parameters algorithm, the proposed method circumvents the problems of ‘explosion of complexity’ and ‘dimension curse’ that exist in the traditional backstepping design. Comparing to the methodology that neural weights are online updated in the controllers, only one scalar needs to be updated in the controllers of each subsystem when dealing with unknown systematic dynamics. Radial basis function neural networks (NNs) are used in the online approximation of unknown systematic dynamics. It is proved using Lyapunov stability theory that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. The tracking errors of each subsystems, the amplitude of NN approximation residuals and external disturbances can be attenuated to arbitrarily small by tuning proper design parameters. Simulation results are given to demonstrate the effectiveness of the proposed method.     

Adaptive output tracking of inherently nonlinear systems with nonlinear parameterization

We address the problem of output tracking for a class of nonlinearly parameterized systems with unstabilizable linearization. To achieve practical output tracking globally in the case when both the bound of reference signals and the bound of unknown time-varying parameters are not known a priori, we present a robust adaptive control method that is based on the idea of universal control combined with the new adaptive feedback design method developed recently for controlling uncertain systems with nonlinear parameterization. Continuous adaptive tracking controllers are explicitly constructed in this paper. The proposed adaptive tracking controllers use only the information of a prescribed reference signal but not its derivatives, nor its bound.     

Online optimal control of nonlinear discrete-time systems using approximate dynamic programming

In this paper,the optimal control of a class of general affine nonlinear discrete-time(DT) systems is undertaken by solving the Hamilton Jacobi-Bellman(HJB) equation online and forward in time.The proposed approach,referred normally as adaptive or approximate dynamic programming(ADP),uses online approximators(OLAs) to solve the infinite horizon optimal regulation and tracking control problems for affine nonlinear DT systems in the presence of unknown internal dynamics.Both the regulation and tracking contro...     

Universal controllers for robust control problems

In this paper we discuss the construction of “universal” controllers for a class of robust stabilization problems. We give a general theorem on the construction of these controllers, which requires that a certain nonlinear inequality is solvablepointwisely or, equivalently, that arobust control Lyapunov function does exist. The constructive procedure producesalmost smooth controllers. The robust control Lyapunov functions extend to uncertain systems the concept of control Lyapunov functions. If such a robust control Lyapunov function also satisfies a small control property, the resulting stabilizing controller is also continuous in the origin of the state space. Applications of our results range from optimal to robust control.     

Optimal damping of forced oscillations in discrete-time systems

In this paper we consider a linear discrete-time control system affected by an additive sinusoidal disturbance with known frequencies but unknown amplitudes and phases. The problem is to damp this forced oscillation in an optimal fashion. We show that the natural solution from the point of view of optimal control is neither robust with respect to errors in the frequencies, and thus not optimal in practice, nor independent of the unknown amplitudes and phases. The main result of this paper concerns the existence and design of a realizable, robust optimal regulator, which is universal in the sense that it does not depend on the unknown amplitudes and phases and is optimal for all choices of such parameters. The regulator allows for a considerable degree of design freedom to satisfy other design specifications. Finally, it is shown that this regulator is optimal also for a wide class of stochastic control problems     

Output-Feedback Based Simplified Optimized Backstepping Control for Strict-Feedback Systems with Input and State Constraints

In this paper, an adaptive neural-network (NN) output feedback optimal control problem is studied for a class of strict-feedback nonlinear systems with unknown internal dynamics, input saturation and state constraints. Neural networks are used to approximate unknown internal dynamics and an adaptive NN state observer is developed to estimate immeasurable states. Under the framework of the backstepping design, by employing the actor-critic architecture and constructing the tan-type Barrier Lyapunov function (BLF), the virtual and actual optimal controllers are developed. In order to accomplish optimal control effectively, a simplified reinforcement learning (RL) algorithm is designed by deriving the updating laws from the negative gradient of a simple positive function, instead of employing existing optimal control methods. In addition, to ensure that all the signals in the closed-loop system are bounded and the output can follow the reference signal within a bounded error, all state variables are confined within their compact sets all times. Finally, a simulation example is given to illustrate the effectiveness of the proposed control strategy.      

Command-filter-based distributed containment control of nonlinear multi-agent systems with actuator failures

In this paper, the problem of distributed containment fault-tolerant control for a class of nonlinear multi-agent systems in strict-feedback form is studied. The considered nonlinear multi-agent systems are subject to unknown nonlinear functions and actuator faults with loss of effectiveness and lock-in-place. By resorting to the universal approximation capability of fuzzy logical systems, the command filtered backstepping technique and nonlinear fault-tolerant control theory, distributed controllers are designed recursively. From the Lyapunov stability theory, it is proved that all signals of the resulting closed-loop systems are cooperatively semi-globally uniformly ultimately bounded and the containment errors converge to a small neighbourhood of origin by properly tuning the design parameters. Finally, a numerical example is provided to show the effectiveness of the proposed control method.     

Decentralized adaptive robust control for a class of large scale systems with uncertainties in the interconnections

The problem of decentralized control is considered for a class of time-varying large scale systems with uncertainties and external disturbances in the interconnections. In this paper, the upper bounds of the uncertainties and external disturbances are assumed to be unknown. The adaptation laws are proposed to estimate such unknown bounds, and by making use of the updated values of these unknown bounds, a class of decentralized linear and non-linear state feedback controllers are constructed. It is shown that by employing the proposed decentralized non-linear state feedback controllers, the solutions of the resulting adaptive closed-loop large scale system can be guaranteed to be uniformly bounded, and the states are uniformly asymptotically stable. By using the decentralized linear state feedback controllers, one can guarantee the uniform ultimate boundedness of the resulting adaptive closed-loop large scale system. Finally, a numerical example is given to demonstrate the validity of the results.     

Fastest recovery after feedback disruption

The problem of quickly reducing operating errors during recovery from a feedback disruption is considered. The objective is to design controllers that reduce operating errors as quickly as possible, once feedback has been restored. It is shown that robust optimal feedback controllers that achieve this objective do exist. Furthermore, it is shown that the performance of optimal controllers can be approximated as closely as desired by controllers that generate bang–bang input signals for the controlled system. Controllers that generate bang–bang signals are relatively easy to derive and implement, since bang–bang signals are characterised by a finite list of scalars – their switching times.     

Decentralized supervisory control with communicating controllers

The decentralized control problem for discrete-event systems addressed in this paper is that of several communicating supervisory controllers, each with different information, working in concert to exactly achieve a given legal sublanguage of the uncontrolled system's language model. A novel information structure model is presented for dealing with this class of problems. Existence results are given for the cases of when controllers do and do not anticipate future communications, and a synthesis procedure is given for the case when controllers do not anticipate communications. Several conditions for optimality of communication policies are presented, and it is shown that the synthesis procedure yields solutions, when they exist for this class of controllers, that are optimal with respect to one of these conditions     

基于扩张状态观测器的四旋翼吊挂飞行系统非线性控制

针对一类四旋翼飞行器吊挂飞行系统的负载摆动抑制和轨迹跟踪精确控制的问题, 考虑系统存在未知外界扰动和模型动态不确定的情况, 提出一种基于扩张状态观测器(Extended state observer, ESO)的吊挂负载摆动抑制的非线性轨迹跟踪控制方法. 将四旋翼吊挂飞行系统分解为姿态、位置和负载摆动控制三个动态子系统, 分别设计非线性控制器实现欠驱动约束下的解耦控制; 设计一种扩张状态观测器, 用以估计和补偿四旋翼与吊挂负载耦合飞行的未知外界扰动与模型动态不确定性, 并验证了闭环系统的稳定性, 跟踪误差及吊挂负载摆动所有信号的一致最终有界. 最后, 利用Quanser公司的Qball2飞行器进行三维空间螺旋轨迹的跟踪控制, 仿真结果验证了未知干扰下基于扩张状态观测器的四旋翼吊挂飞行非线性控制的有效性和优越性, 实现了四旋翼吊挂系统轨迹跟踪的精确控制和飞行过程中负载摆动的快速抑制.     

一类非线性不确定时滞系统的混杂状态反馈H∞鲁棒控制

利用混杂状态反馈控制策略研究一类非线性不确定时滞系统的H∞鲁棒控制问题.假定在给定的控制器集合中有有限个备选的状态反馈控制器,并且每个单一的连续控制器都不能使系统具有鲁棒H∞性能.当控制器的增益矩阵已知时,基于单Lyapunov函数技术和凸组合条件给出控制器切换方案以确保非线性不确定时滞系统具有鲁棒H∞性能.当控制器的增益矩阵未知时,使用多Lyapunov函数技术得到了问题可解的另一个充分条件,同时还给出了混杂状态反馈H∞控制器的设计.     

牵引车–飞机系统的自适应滑模变结构控制

将自动转向技术应用于牵引车–飞机系统, 并以侧偏位移和相对横摆角作为反馈, 提出一种牵引车四轮主动转向控制策略. 重点考虑牵引车和飞机的侧向和横摆运动, 建立含铰接角在内的牵引车–飞机系统非线性动力学模型. 将牵引车和飞机的轮胎侧偏刚度视为有界的不确定性参数, 将侧向风等因素视为未知的外在扰动, 采用自适应滑模变结构控制方法设计牵引车转向角控制器. 仿真结果表明, 设计出的前、后轮转向控制器能使控制系统同时获得很好的轨迹跟踪性和操纵稳定性, 并且能够有效的克服参数摄动和外界干扰对系统操作性的影响.     

Asymptotic sliding mode control approach to adaptive distributed tracking problem for multi-agent nonlinear delayed systems

In this article, a sliding mode coordinated decentralised state-feedback model reference adaptive control is developed for a class of large-scale uncertain multi-agent systems with time-varying delays in the nonlinear interconnections. The design procedure is based on a combination of the model coordination concept and a sliding mode control methodology. Novel decentralised controller parameterisations that are robust to unknown information exchange delays and to external disturbances with unknown bounds are proposed. Two different controllers are designed: one with discontinuous and one with continuous control action, respectively.     

Adaptive neural control using reinforcement learning for a class of robot manipulator

In this paper, an adaptive control algorithm is proposed for a class of robot manipulator systems with unknown functions and dead-zone input by using a reinforcement learning scheme. The parameters of the dead zone are supposed to be unknown but bounded. The unknown functions can be approximated based on the neural networks, which is one part of the reinforcement learning scheme, namely an action network. The other part is called critic network which is used to approximate the reinforcement signal. Then, the prominent advantage of the proposed approach is that an optimal control input can be obtained by using two networks compared with the results of robot manipulator with dead zone: an additional term is given to compensate for the effect of the dead zone, and a special design procedure to solve the difficulties in constructing the controllers and adaptation laws. Based on the Lyapunov analysis theory, all the signals of the closed-loop system are proved to be bounded and the system output can track the reference signal to a bounded compact set. Finally, a simulation example is given to illustrate the effectiveness of the approach.     

Direct adaptive fuzzy control of a class of MIMO non-affine nonlinear systems

An adaptive fuzzy control approach is proposed for a class of multiple-input–multiple-output (MIMO) nonlinear systems with completely unknown non-affine functions. The global implicit function theorem is first used to prove the existence of an unknown ideal implicit controller that can achieve the control objectives. Within this scheme, fuzzy systems are employed the approximate the unknown ideal implicit controller, and robustifying control terms are used to compensate the approximation errors and external disturbances. The adjustable parameters of the used fuzzy systems are deduced from the stability analysis of the closed-loop system in the sense of Lyapunov. To show the efficiency of the proposed controllers, two simulation examples are presented.     

不确定时滞组合系统的分散鲁棒镇定

讨论不确定时滞组合系统的分散自适应鲁棒镇定问题．外部扰动存在于子系统内部,可以是非线性或时变的,且不确定项和时滞存在于互联项中．不确定项和外部扰动是有界的,但上界未知．利用自适应律估计未知的上界,设计了非线性无记忆控制器．采用非线性控制器可保证闭环组合系统的解一致有界。且系统状态是一致渐近稳定的．仿真结果表明了该设计方法的有效性.     

Robust adaptive‐sliding mode tracking of state delayed nonlinear plants with actuator failures

In this paper, we develop two sliding mode—model reference adaptive control (MRAC) schemes for a class of delayed nonlinear dynamic systems under actuator failure that are robust with respect to actuator failures, to an unknown plant delay, to a nonlinear perturbation, and to an external disturbance with unknown bounds. Appropriate Lyapunov–Krasovskii‐type functionals with ‘virtual’ adaptation gains are introduced to design the adaptation algorithms, and to prove stability. Two different controllers are designed: one with discontinuous and another with continuous control actions, respectively. Copyright © 2011 John Wiley & Sons, Ltd.     

不确定线性时滞系统的一种混杂状态反馈保成本控制及优化设计方法

本文研究一类不确定线性时滞系统的混杂状态反馈保成本控制及优化设计问题.假设存在有限个备选的控制增益已知的控制器,并且其中任何单一的状态反馈控制器都不能镇定系统,基于单Lyapunov函数的方法,给出了混杂状态反馈保成本控制的充分条件及优化设计方案.当备选的控制增益未知时,利用多Lyapunov函数法,同样给出混杂状态反馈保成本控制的充分条件及相应的优化设计方法.最后用仿真验证了文中方法的有效性.     

Adaptive robust tracking and model following of uncertain dynamical systems with multiple time delays

The problem of robust tracking and model following is considered for a class of linear systems with multiple delayed state perturbations, time-varying uncertain parameters, and disturbance. In this note, it is assumed that the upper bounds of the delayed state perturbations, uncertainties, and external disturbances, are unknown. An improved adaptation law with /spl sigma/-modification is proposed to estimate such unknown bounds, and on the basis of the updated values of these unknown bounds, a class of continuous memoryless state feedback controllers is constructed for robust tracking of dynamical signals. The proposed adaptive robust tracking controller can guarantee that the tracking error decreases asymptotically to zero in the presence of multiple delayed state perturbations, time-varying uncertain parameters, and disturbance. In addition, it is also shown that this improved adaptation law with /spl sigma/-modification can be applied to the general adaptive control problems to obtain some more exact control results. Finally, a numerical example is given to demonstrate the validity of the results.