非线性扩展结构大系统自适应神经网络跟踪控制

针对一类非线性关联大系统在结构扩展时的跟踪控制问题, 提出一种采用自适应神经网络的控制方法. 该方法要求在不改变原结构系统控制律的前提下设计新加入子系统的控制律和自适应律, 使扩展后所有子系统都具有很好的跟踪性能. 这里主要利用神经网络的逼近功能以及Backstepping 技术来设计自适应律和控制律, 通过Lyapunov 理论证明在该控制器的作用下闭环系统的所有信号均是有界的, 并可使系统准确跟踪. 仿真结果验证了所提出方法的有效性.

     

分散模型参考自适应控制

本文针对由参数未知、存在有界扰动和非线性关联的子系统组成的大规模互联系统,提出 了一种新的分散模型参考自适应控制法.它适用于孤立子系统传递函数的相对阶次n*i为任 意值的情况.根据李雅普诺夫稳定性理论,文中证明了这种分散自适应控制系统全局稳定的 充分条件.与有关文献所介绍的方法相比,本文的方法可用于n*i>2的场合,因而它更具有 一般性和实用性.     

时滞不确定组合系统的鲁棒分散输出控制

讨论了时滞不确定组合系统的鲁棒分散输出控制问题．不确定项存在于子系统内部,可以是非线性或时交的：而且时滞存在互联项中,并满足匹配条件．不确定项是有界的,但界是未知的．利用自适应律来估计未知的上界,设计出分散无记忆输出控制器．基于Lyapunov稳定性理论和Lyapunov-Krasovskii函数,该控制器能够保证闭环系统的解是终极一致有界的．最后的仿真结果说明该设计方法是有效的．     

Decentralized adaptive stabilization of interconnected nonlinear systems with unknown non-symmetric dead-zone inputs

The decentralized adaptive stabilization method is proposed for uncertain interconnected nonlinear systems with unknown non-symmetric dead-zone inputs. The class of systems considered in this paper consists of strict-feedback nonlinear subsystems with unknown non-symmetric dead-zone inputs which interact through their outputs. The unknown nonlinear interaction terms are assumed to be bounded by nonlinear functions with unknown parameters. For the simple controller design, the local controller for each subsystem is systematically derived based on the dynamic surface design technique, without constructing the dead-zone inverse and requiring the bound information of dead-zone parameters (slopes and break-points). All unknown parameters of interconnected nonlinear systems are compensated by the adaptive technique. From Lyapunov stability theorem, it is proved that all signals in the interconnected closed-loop system with decentralized adaptive controllers are semi-globally bounded. Simulation results for tripled inverted pendulums demonstrate the effectiveness of the proposed approach.     

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.     

Adaptive fuzzy decentralized control for interconnected MIMO nonlinear subsystems

This paper describes an adaptive fuzzy control strategy for decentralized control for a class of interconnected nonlinear systems with MIMO subsystems. An adaptive robust tracking control schemes based on fuzzy basis function approach is developed such that all the states and signals are bounded. In addition, each subsystem is able to adaptively compensate for disturbances and interconnections with unknown bounds. The resultant adaptive fuzzy decentralized control with multi-controller architecture guarantees stability and convergence of the output errors to zero asymptotically by local output-feedback. An extensive application example of a three-machine power system is discussed in detail to verify the effectiveness of the proposed algorithm.     

Adaptive neural decentralized control for strict feedback nonlinear interconnected systems via backstepping

An approximation based adaptive neural decentralized output tracking control scheme for a class of large-scale unknown nonlinear systems with strict-feedback interconnected subsystems with unknown nonlinear interconnections is developed in this paper. Within this scheme, radial basis function RBF neural networks are used to approximate the unknown nonlinear functions of the subsystems. An adaptive neural controller is designed based on the recursive backstepping procedure and the minimal learning parameter technique. The proposed decentralized control scheme has the following features. First, the controller singularity problem in some of the existing adaptive control schemes with feedback linearization is avoided. Second, the numbers of adaptive parameters required for each subsystem are not more than the order of this subsystem. Lyapunov stability method is used to prove that the proposed adaptive neural control scheme guarantees that all signals in the closed-loop system are uniformly ultimately bounded, while tracking errors converge to a small neighborhood of the origin. The simulation example of a two-spring interconnected inverted pendulum is presented to verify the effectiveness of the proposed scheme.     

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.     

Decentralized adaptive robust control for a class of large-scale systems including delayed state perturbations in the interconnections

The problem of decentralized stabilization is considered for a class of large-scale time-varying systems with delayed state perturbations in the interconnections. In this note, the upper bounds of the uncertainties in the interconnections are assumed to be unknown. The adaptation laws are proposed to estimate such unknown bounds, and by making use of their updated values, a class of decentralized memoryless state feedback controllers is constructed. Based on Lyapunov stability theory and Lyapunov-Krasovskii functional, it is shown that the solutions to the resulting adaptive closed-loop large-scale time-delay system can be guaranteed to be uniformly ultimately bounded. Finally, a numerical example is given to demonstrate the validity of the results.     

Decentralized robust controller design for a class ofinterconnected uncertain systems: with unknown bound of uncertainty

In this paper, a simple decentralized robust control scheme is proposed for a class of interconnected time-varying systems with uncertainties. The uncertainties may appear in the interconnections between the subsystems and also within the subsystems, and they are possibly nonlinear and time-varying. The uncertainties are bounded, but the bounds of the uncertainties are unknown in controller design. When certain matching conditions are satisfied for the uncertain interconnections and the uncertainties within the subsystems, the proposed decentralized control scheme guarantees the controlled system to converge exponentially with a prescribed degree toward the equilibrium of the system, or a residual ball around the equilibrium     

Stabilization of Time‐Varying and Disturbed Complex Dynamical Networks with Different‐Dimensional Nodes and Uncertain Nonlinearities

This paper investigates the stabilization problem for time‐varying and disturbed complex dynamical networks (CDNs) with different‐dimensional nodes and uncertain nonlinearities. To be consistent with the properties of real‐world networks, both the disturbances of our networks and the nonlinear structures of the nodes permit are completely unknown but bounded. Furthermore, the norm bounds of the uncertain nonlinearities and disturbances (NBUND) are applied to design the stabilization controllers. When the NBUND are known in advance, some decentralized state feedback controllers are proposed to stabilize our networks. And when they are unknown, adaptive decentralized stabilization schemes are brought forward for our network models. The effectiveness and feasibility of our theoretical results are verified by two simulation examples.     

Solution of optimal control problem of linear system with input derivative through Liapunov's method

Decentralized stabilization and control schemes for large-scale power systems are presented in this paper. The interconnections between the subsystems are assumed to be unknown, but bounded. The control design for the first scheme utilizes the knowledge of the bounds on the interconnections between the subsystems, whereas this information is not required in the second control design due to its adaptive nature. These schemes are applied to a power system with two generators and an infinite bus connected through a network of transformers and transmission lines. Simulation studies for unknown interconnections arising due to faults and subsequent line switchings in the transmission network are carried out. The studies not only illustrate the simplicity of the developed controls, but also validate the fact that connective stability and robust performance is maintained for unknown interconnection topology using these schemes.     

Adaptive decentralized fault‐tolerant tracking control for large‐scale nonlinear systems with input quantization

In this paper, an adaptive decentralized tracking control scheme is designed for large‐scale nonlinear systems with input quantization, actuator faults, and external disturbance. The nonlinearities, time‐varying actuator faults, and disturbance are assumed to exist unknown upper and lower bounds. Then, an adaptive decentralized fault‐tolerant tracking control method is designed without using backstepping technique and neural networks. In the proposed control scheme, adaptive mechanisms are used to compensate the effects of unknown nonlinearities, input quantization, actuator faults, and disturbance. The designed adaptive control strategy can guarantee that all the signals of each subsystem are bounded and the tracking errors of all subsystems converge asymptotically to zero. Finally, simulation results are provided to illustrate the effectiveness of the designed approach.     

一类分布式系统的自适应跟踪和扰动抑制

研究直接自适应状态反馈控制策略解决一类有故障和摄动关联链接的分布式大系统渐进跟踪和扰动抑制问题. 根据特殊的分布式结构, 在所有关联故障因子, 关联通道摄动和子系统外部干扰的上界都未知下, 提出自适应率在线升级控制器参数. 基于自适应策略信息, 构造一类分布式状态反馈控制器自动补偿故障和摄动影响, 同时抑制外部扰动. 在关联通道有故障摄动和外部扰动情况下, 所提出的自适应鲁棒跟踪控制器可以保证所得自适应闭环大系统稳定, 及每个子系统渐进输出跟踪所对应参考信号. 最后由一个仿真例子评估所提技术的有效性.     

Adaptive backstepping control of a class of uncertain nonlinear systems with unknown backlash-like hysteresis

In this note, we consider the same class of systems as in a previous paper, i.e., a class of uncertain dynamic nonlinear systems preceded by unknown backlash-like hysteresis nonlinearities, where the hysteresis is modeled by a differential equation, in the presence of bounded external disturbances. By using backstepping technique, robust adaptive backstepping control algorithms are developed. Unlike some existing control schemes for systems with hysteresis, the developed backstepping controllers do not require the uncertain parameters within known intervals. Also, no knowledge is assumed on the bound of the "disturbance-like" term, a combination of the external disturbances and a term separated from the hysteresis model. It is shown that the proposed controllers not only can guarantee global stability, but also transient performance.     

Adaptive control of system with unknown output backlash

Adaptive control schemes for systems with unknown backlash at the plant output are developed. In the case of known backlash, a backlash inverse controller guarantees exact output tracking. When the backlash characteristics are unknown, adaptive laws are designed to update the controller parameters and to guarantee bounded input-output stability. Simulations show significant improvements of the system performance achieved by such adaptive backlash inverse controllers     

Decentralized risk-sensitive controller design for strict-feedback systems

This paper studies decentralized control of uncertain systems. The class of systems focused on is a set of stochastic strict-feedback systems which interact through their outputs, and performance is measured with respect to a risk sensitive cost criterion. The unknown nonlinear interconnection terms are assumed to be bounded by some known functions of the outputs of the subsystems, multiplied by some unknown parameters. The controllers designed for each subsystem have access only to the information available with regard to the respective subsystem, and they achieve an arbitrarily small value for the risk-sensitive cost for the overall system. Under this completely decentralized control scheme, all closed-loop signals remain bounded in probability.     

Robust Adaptive Neural Network Control For A Class Of Multiple‐Input Multiple‐Output Nonlinear Time Delay System With Hysteresis Inputs And Dynamic Uncertainties

This paper studies an adaptive neural control for nonlinear multiple‐input multiple‐output systems with dynamic uncertainties, hysteresis input, and time delay. The studied systems are composed of N nonlinear time‐delay subsystems and the interconnection terms are contained in every equation of each subsystem. Adaptive neural control algorithms are developed by introducing a well‐defined smooth function. The unknown time‐varying delays and the unmodeled dynamics are dealt with by constructing appropriate Lyapunov–Krasovskii functions and introducing an available dynamic signal. The main advantage of the proposed controllers is that they contain fewer parameter estimates that need to be updated online. Consequently, the accuracy of ultimate tracking errors asymptotically approaches a pre‐defined bound, and all signals in the closed‐loop systems are also ensured to be uniformly ultimately bounded. Finally, a simulation example is provided to illustrate the effectiveness and merits of the proposed adaptive neural network control schemes.     

关联系统的分散自适应控制

本文研究了一类大系统的分散自适应控制,导出了一组鲁棒性较强的自适应控制律.当 各子系统间存在参数未知的非线性的任意关联和有界外扰时,若系统相关阶次小于或等于2r 那么状态和参数误差以指数收敛到某有界余集.在外扰为常数时,输出能实现完全跟踪.     

不确定组合大系统的自适应分散镇定控制

考虑具有非线性关联作用的不确定时变线性组合大系统的自适应分散镇定问题.针对 系统不确定界完全未知的情形,首先从理论上证明了可设计自适应鲁棒分散控制器确保受控系 统渐近稳定;进而从工程实际应用的角度,给出了确保受控系统实用稳定的自适应鲁棒分散控 制器的设计方案.仿真说明该设计方案是有效的.