一类不确定时滞系统的模糊滑模控制

利用T-S模糊模型逼近一类非线性不确定时滞系统,将非线性系统模糊化为局部线性系统．基于李亚普诺夫稳定性定理设计出使模糊系统全局稳定的滑模控制器,该控制器对满足匹配条件和不满足匹配条件的不确定性系统均适用．最后以Truck—Trailer模型为例进行仿真研究,其结果验证了设计方案的可行性和有效性．     

基于虚拟模型的自适应解耦抗扰控制

针对一类多变量非线性耦合系统,提出了一种基于虚拟模型的非线性自适应控制器.首先将非线性系统线性化处理并将其作为虚拟模型,对该模型设计线性自适应控制律.然后将线性控制律分别应用在虚拟系统和受控的实际非线性系统上,根据两者的输出误差设计补偿控制律,以达到对实际被控对象进行自适应解耦抗扰的目的.利用李雅普诺夫稳定理论给出了控制系统稳定性条件.实验仿真验证了控制算法的有效性.     

多区域互联电力系统的自适应滑模负载频率控制

本文研究了一类计及电动汽车的电力系统中的负荷频率控制问题, 首先, 将电动汽车模型与传统的负载频率控制模型相结合,在未知扰动波动范围的条件下设计了自适应滑模控制律. 其次, 分别考虑了电网调频中的匹配扰动和不匹配扰动问题, 并利用李亚普诺夫稳定性理论导出了匹配和不匹配条件下的系统稳定的充分条件. 最后, 两个区域电力系统的仿真结果表明, 电动汽车作为电源和负载都可以提高电网的频率稳定性, 所设计的控制器可以有效地调节电网的频率波动.     

基于模糊模型和神经网络的多时滞不确定非线性系统的鲁棒H∞控制

针对一类具有多时滞的不确定非线性系统,提出了一种基于模糊模型和神经网络的组 合控制方法.利用具有多时滞的模糊T-S模型对系统进行近似建模并给出基于线性矩阵不等式 (LMI)的模糊H∞控制律.提出完全自适应RBF神经网络控制方法,通过在线自适应调整RBF 神经网络的权重、函数中心和宽度,来对消系统的未知不确定性和模糊建模误差的影响,不要求 系统的不确定项和模糊建模误差满足任何匹配条件或约束,并证明了闭环系统的稳定性.最后, 将所提出的方法应用到一具有多时滞的非线性混沌系统,仿真结果表明了该方法的有效性.     

带有非线性不确定奇异系统的积分滑模控制

针对一类含有非线性不确定的奇异系统, 提出了一种面向性能的鲁棒控制器. 控制器由3部分组成: 积分滑模控制、附加的非线性控制及复合非线性反馈控制. 积分滑模控制可将匹配不确定完全抵消并使系统轨迹进入理想滑模; 附加的非线性控制用来抑制理想滑动模态上非匹配不确定对系统稳定性和性能的影响; 复合非线性反馈控制则保证闭环系统输出按性能要求渐近地跟踪参考输入信号. 最后通过算例说明所提算法的有效性.     

确定性Hammerstein模型的自适应控制算法及其全局收敛性

本文对广义的Hammerstein模型描述的一类非线性系统,提出一种复合的自适应控制算 法.在适当的条件下,证明了这类非线性系统的稳定性和算法的全局收敛性.本文提出的算 法可以适用于开环不稳定且具有"非最小相位"特性的系统.     

非线性多变量零阶接近有界系统的多模型自适应控制

针对一类多变量非线性离散时间系统,提出一种新的基于神经网络的多模型自适应控制方法.为了将非线性系统的高阶非线性项的限制条件放宽到零阶接近有界,该方法引入了一种新的非线性模型.该模型在传统线性回归模型基础上增加了非线性补偿项,使模型的估计误差有界.一个神经网络模型与非线性模型同时被用来对系统进行辨识.基于性能指标的切换机构选择性能较好的模型对应的控制器 对系统进行控制. 理论分析证明了零阶接近有界多模型自适应控制系统的有界输 入和有界输出稳定性. 仿真实验说明了提出的多模型自适应控制方法的有效性.     

基于T-S模型的非线性系统的最终滑动模态控制

采用T_S模糊动态模型逼近非线性系统, 将非线性系统模糊化为局部线性模型. 用Lyapunov稳定性理论设计出确保T-S模型全局渐近稳定的变结构控制器. 采用单位向量控制形式的最终滑动模态控制器, 对满足匹配条件和不满足匹配条件的不确定性均适用. 以倒立摆为模型的仿真实验, 验证了方案的有效性.     

机器人系统非线性分散重复学习轨迹跟踪控制

采用一类具有"小误差放大、大误差饱和"功能的非线性饱和函数来改进传统重复学习控制(Repetitive control, RC)机器人系统动力学控制, 形成一类新的非线性分散重复学习控制(Nonlinear decentralized repetitive control, NRC),使得在不增加驱动力矩的条件下获得了更快的响应速度和更高的轨迹跟踪精度. 应用Lyapunov直接稳定性理论和LaSalle不变性原理证明了闭环系统的全局渐近稳定性. 三自由度机器人系统数值仿真结果表明了所提出的非线性分散重复学习控制具有良好的控制品质.     

一类非线性严格反馈系统基于干扰观测器的抗干扰控制

针对一类带有干扰的非线性严格反馈系统, 研究其抗干扰控制问题. 系统干扰满足不匹配条件, 代表一类部分信息已知的干扰. 通过设计非线性干扰观测器, 提出基于非线性干扰观测器和back-stepping 的抗干扰控制方法来补偿干扰, 该方法可以保证闭环系统所有信号是半全局最终一致有界的. 最后, 通过与现有方法的对比验证了所提出方法的正确性和有效性.

     

严格反馈系统的事件触发学习控制

本文针对一类严格反馈非线性系统,提出了基于确定学习的事件触发控制方案.首先,在本地控制测试端设计自适应神经网络控制,并在控制过程中实现系统未知动态的知识获取和存储.随后,基于常值权值,设计了新颖的事件触发控制器和事件触发条件.结合李雅普诺夫稳定性分析和非线性脉冲动态系统原理,验证了所提方案能够保证跟踪误差收敛到零的小邻域内以及所有闭环信号是最终一致有界的.此外,本文所提方案采用常值权值代替了估计权值,使得所提方案易于实现,暂态性能好和网络资源占用少.最后,通过对比仿真结果证明了所提方案的有效性.     

Adaptive robust control for servo manipulators

In this paper, an adaptive robust control scheme is developed which is suitable for the control of a class of uncertain nonlinear systems, typical of many servo manipulators. The control scheme is comprised of a model reference adaptive controller (MRAC) augmented with a nonlinear compensator based on an adaptive radial basis function (RBF). The RBF compensator is used to neutralise the effects of uncertain and possibly nonlinear dynamics, so that the equivalent system as seen by the MRAC is reduced to one without significant unstructured modelling errors. A stability analysis is provided to show the uniform stability and the asymptotic tracking capabilities of the proposed control system. Real-time experiment results verify the effectiveness of the control scheme.     

基于神经网络的一类非线性系统自适应跟踪控制

提出一种非线性系统的自适应神经跟踪控制方案。通过利用RBF神经网络对未知非线性系统建模,并用一个滑模控制项消除网络建模误差和外部干扰的影响,从而能够保证闭环系统的全局稳定性和输出跟踪误差渐近收敛于零。     

一类仿射非线性网络控制系统的稳定性分析

利用采样数字控制系统的方法分析了一类混杂动态系统模型描述的仿射非线性网络控制系统的稳定性问题.针对一类仿射非线性对象和线性数字控制器组成的网络控制系统,考虑了网络诱导延时对系统稳定性的影响,得到了仿射非线性网络控制系统一致渐近稳定的条件．仿真实例验证了理论分析的正确性．     

基于神经网络补偿的非线性时滞系统时滞正反馈控制

A new adaptive time-delay positive feedback controller (ATPFC) is presented for a class of nonlinear time-delay systems. The proposed control scheme consists of a neural networks-based identification and a time-delay positive feedback controller. Two high-order neural networks (HONN) incorporated with a special dynamic identification model are employed to identify the nonlinear system. Based on the identified model, local linearization compensation is used to deal with the unknown nonlinearity of the system. A time-delay-free inverse model of the linearized system and a desired reference model are utilized to constitute the feedback controller, which can lead the system output to track the trajectory of a reference model. Rigorous stability analysis for both the identification and the tracking error of the closed-loop control system is provided by means of Lyapunov stability criterion. Simulation results are included to demonstrate the effectiveness of the proposed scheme.     

Decentralized adaptive control of a class of large-scaleinterconnected nonlinear systems

Decentralized adaptive control design for a class of large-scale interconnected nonlinear systems with unknown interconnections is considered. The motivation behind this work is to develop decentralized control for a class of large-scale systems which do not satisfy the matching condition requirement. To this end, large-scale nonlinear systems transformable to the decentralized strict feedback form are considered. Coordinate-free geometric conditions under which any general interconnected nonlinear system can be transformed to this form are obtained. The interconnections are assumed to be bounded by polynomial-type nonlinearities. Global stability and asymptotic regulation are established using classical Lyapunov techniques. The controller is shown to maintain robustness for a wide class of systems obtained by perturbation in the dynamics of the original system. Furthermore, appending additional subsystems does not require controller redesign for the original subsystems. Finally, the scheme is extended to the model reference tracking problem when global uniform boundedness of the tracking error to a compact set is established     

Adaptive discrete-time sliding-mode control of nonlinear systems described by Wiener models

In this paper, we propose an adaptive control scheme that can be applied to nonlinear systems with unknown parameters. The considered class of nonlinear systems is described by the block-oriented models, specifically, the Wiener models. These models consist of dynamic linear blocks in series with static nonlinear blocks. The proposed adaptive control method is based on the inverse of the nonlinear function block and on the discrete-time sliding-mode controller. The parameters adaptation are performed using a new recursive parametric estimation algorithm. This algorithm is developed using the adjustable model method and the least squares technique. A recursive least squares (RLS) algorithm is used to estimate the inverse nonlinear function. A time-varying gain is proposed, in the discrete-time sliding mode controller, to reduce the chattering problem. The stability of the closed-loop nonlinear system, with the proposed adaptive control scheme, has been proved. An application to a pH neutralisation process has been carried out and the simulation results clearly show the effectiveness of the proposed adaptive control scheme.     

Robust adaptive control of a class of nonlinear systems including actuator hysteresis with Prandtl-Ishlinskii presentations

This paper deals with robust adaptive control of a class of nonlinear systems preceded by unknown hysteresis nonlinearities. By using a Prandtl-Ishlinskii model with play and stop operators, we attempt to fuse the model of hysteresis with the available control techniques without necessarily constructing a hysteresis inverse. A robust adaptive control scheme is therefore proposed. The global stability of the adaptive system and tracking a desired trajectory to a certain precision are achieved. Simulation results attained for a nonlinear system are presented to illustrate and further validate the effectiveness of the proposed approach.     

Barrier function based finite-time tracking control for a class of uncertain nonlinear systems with input saturation

In this article, a novel robust finite-time tracking control scheme is proposed for a class of uncertain nonlinear systems subject to the model uncertainty, external disturbance, and input saturation. A barrier function based disturbance observer (BFDO) with finite-time convergence performance is developed to estimate the non-smooth nonlinear compound disturbance, which includes the uncertainty, disturbance of system and input saturation. In addition, an adaptive continuous nonsingular terminal sliding mode controller, based on the barrier function and the estimate of the BFDO is developed. The Lyapunov stability and finite-time convergence of the proposed control scheme are proved. The effectiveness and performance advantage of the proposed control scheme is demonstrated by numerical simulations and comparison with existing works.     

Multiple model self-tuning control for a class of nonlinear systems

This study develops a novel nonlinear multiple model self-tuning control method for a class of nonlinear discrete-time systems. An increment system model and a modified robust adaptive law are proposed to expand the application range, thus eliminating the assumption that either the nonlinear term of the nonlinear system or its differential term is global-bounded. The nonlinear self-tuning control method can address the situation wherein the nonlinear system is not subject to a globally uniformly asymptotically stable zero dynamics by incorporating the pole-placement scheme. A novel, nonlinear control structure based on this scheme is presented to improve control precision. Stability and convergence can be confirmed when the proposed multiple model self-tuning control method is applied. Furthermore, simulation results demonstrate the effectiveness of the proposed method.