一种不确定离散时间系统的变结构控制方法

针对离散时间系统变结构控制中存在的问题，利用指数趋近律的趋近特性，提出一种新的离散变结构控制方法，利用递推估计的方法设计变结构控制律，有效地抵消了线性系统中慢变不确定性的影响，实现了对慢变非线性系统的控制，该方法克服了以往控制方法中需已知扰动上界的限制，降低了抖振，加快了趋近速度。仿真结果证明了该方法的有效性。     

不确定离散时间系统的变结构控制设计

讨论了利用离散趋近律设计不确定离散时间系统的变结构控制问题,分析了离散趋 近律系数造成系统颤振的原因,给出了改进的离散趋近律,并利用它设计了变结构控制律,对 不确定部分建立灰色估计模型,估计出参数值.仿真结果表明该方法是可行的,有效地防止了 系统的颤振和不确定因素的影响.     

切换带厚度递减的离散变结构控制方案

对离散趋近律方法进行研究,针对利用该方法设计变结构控制时系统产生抖振的原因,提出一种准滑动模态带宽度单调减小的离散变结构控制方案.理论分析及仿真结果表明,该方案较好地克服了由离散趋近律引起的抖振问题,可以大大改善离散变结构控制系统的稳态性能.     

一种离散时间系统变结构控制的新方法

研究离散时间系统变结构控制问题,提出一种新的离散变结构趋近律.利用该趋近律设计的离散变结构控制器,不仅能大幅度削弱抖振,使系统运动最终趋干原点不存在稳态振荡,而且可使系统的准滑动模态保持步步穿越切换面的基本属性,有效地改善了控制品质,提高了系统的鲁棒性.仿真结果验证了该方法的有效性与合理性.     

不确定离散变结构控制系统的趋近律方法

分析了不确定离散变结构控制系统的常用设计方法的优缺点, 借助s型函数提出了一种改进的离散趋近律.应用该趋近律设计的变结构控制系统, 其原点稳定性和系统平稳性都优于指数趋近律和变速趋近律, 该设计方案保证了系统状态在趋近过程中能够保持某一趋近特性, 加快了系统状态的趋近速度, 同时降低了抖振, 易于变结构控制设计. 仿真结果验证了该设计方案的可行性和有效性.     

一种改进的离散时间系统的变结构控制

本文针对离散时间系统,讨论了利用散离趋律设计变结构问题,分析了趋近律系数造成系统抖振的原因,给出了的离散趋近律,并利用它的设计变结构控制,仿真结果表明该方法是可行的,且比原方法更加有效地减少了系统的抖振,保证了闭环系统的渐近稳定性。     

离散时间系统变结构控制分段趋近律

对高为炳先生提出的离散时间系统变结构控制的趋近律进行改进,提出了系统状态在进入准滑动模态带内和带外分别采用不同趋近律的分段式趋近律,该趋近律符合高氏关于离散变结构控制到达条件的6个特点,采用该趋近律设计的系统运动最终趋近于原点,从而具有快速趋近和降低抖振的品质。仿真结果说明了该方法的有效性。     

新的离散时间系统变结构趋近律

针对离散时间系统变结构控制的设计问题进行了研究。为了改善离散指数趋近律的趋近过程,提出了一种新的趋近律,利用该趋近律设计的变结构控制器,不仅能够大幅度削弱抖振,使系统运动最终趋于原点不存在稳态振荡,而且能使系统的准滑动模态保持步步穿越切换面的基本属性,有效地改善了趋近过程,提高了控制品质。仿真分析验证了该方法的合理性与有效性。     

离散时间系统变结构控制基于衰减控制的趋近律

讨论了利用离散趋近律设计离散时间系统的变结构控制问题, 在衰减控制的基础上, 提出了衰减变速趋近律和衰减幂次趋近律. 理论分析和仿真结果表明, 本文所提的方法是有效的, 既保证了闭环系统的渐进稳定性, 又大大减少了系统的抖动现象.     

基于趋近律方法的离散时间系统变结构控制

对变结构控制设计中的离散趋近律方法进行深入研究,提出了"理想趋近律"与"鲁棒趋近律"的概念.基于这些概念可以将离散趋近律区分为两类.研究了采用不同类型趋近律求取变结构控制时的异同及鲁棒性保证机理.澄清了使用趋近律方法设计变结构控制时存在的混乱和误解.     

Adaptive control of a class of slowly time varying systems with modeling uncertainties

In a recent work, a new linear adaptive controller based on certainty-equivalence and backstepping design, which promises a level of transient and asymptotic performance comparable to that of the tuning functions adaptive backstepping controller without using high order nonlinearities, was proposed for linear time invariant systems. The proposal was supplemented with robustness and performance analysis in the presence of modeling uncertainties. In this note, the same idea is used to develop a new linear adaptive controller for slowly time varying systems with modeling uncertainties. The new adaptive control scheme guarantees robustness with respect to modeling errors via normalizing damping, parameter projection, and static normalization. Use of normalizing damping is essential in protecting the "linearity" of the system, which plays a key role in reaching the stability and robustness results.     

Global output‐feedback stabilization for nonlinear systems with unknown relative degree

This paper is devoted to the global stabilization via output feedback for a class of nonlinear systems with unknown relative degree, dynamics uncertainties, unknown control direction, and nonparametric uncertain nonlinearities. In particular, the unknown relative degree is without known upper bound, which renders us to research for a filter with varying dimension rather than the ones with over dimensions in the existing literature. In comparison with more popular but a bit stronger input‐to‐state stable or input‐to‐state practically stable requirement, only bounded‐input bounded‐state stable requirement is imposed on the dynamics uncertainties, which affect the systems in a persistent intensity rather than in a decaying one. In this paper, to compensate multiple serious system uncertainties and realize global output‐feedback stabilization, a design scheme via switching logic together with varying dimensional filter is developed. In this scheme, 2 switching sequences, which separately generate the gains of the controller and act as the varying dimensions of the filter, are designed to overcome unknown control direction, dynamics uncertainties and nonparametric uncertain nonlinearities, and unknown relative degree, respectively. A 2‐mass lumped‐parameter structure is provided to show the effectiveness of the proposed method in this paper.     

Sliding control of non-linear systems containing time-varying uncertainties with unknown bounds

The sliding controller is very effective in dealing with system uncertainties defined in compact sets. If the bounds of the uncertainties are not available, the adaptive sliding controller might be designed. One restriction for the adaptive sliding scheme is that the unknown parameter should be constant, which is not always satisfied in practice. For a non-linear system with general uncertainties (i.e. time varying with unknown bounds), both the traditional sliding control and adaptive sliding control do not work properly. This paper proposes a new sliding control scheme for non-linear systems containing time-varying uncertainties with unknown bounds. The uncertainties are assumed to be piecewise continuous functions of time and satisfy the Dirichlet conditions. By representing these uncertainties in finite-term Fourier series, they can be estimated by updating the Fourier coefficients. Since the coefficients are time-invariant, update laws are easily obtained from the Lyapunov approach to guarantee output error convergence. Computer simulations are performed to show efficacy of the proposed schemes.     

基于状态观测器的非理想输入不确定组合大系统的输出反馈分散镇定

基于状态观测器,讨论了不确定相似组合系统的鲁棒分散输出反馈镇定问题,系统的输入是非理想的,不确定项存在于系统内部和各子系统的关联项中,它们可能是非线性或时变的,且满足通常的匹配条件,使用变结构原理设计控制器,所设计的控制器保证系统渐近稳定,研究结果表明,系统的相似性有助于简化对系统的分析和设计;仿真结果表明,本文的方法是有效的。     

Robust output tracking for non-linear systems

The problem of output tracking for a single-input single-output non-linear system in the presence of uncertainties is studied. The notions relative degree and minimum-phase for non-linear systems are reviewed. Given a bounded desired tracking signal with bounded derivatives, a control law is designed for minimum-phase non-linear systems which results in tracking of this signal by the output. This control law is modified in the presence of uncertainties associated with the model vector fields to reduce the effects of these uncertainties on the tracking errors. Two types of uncertainties are considered: those satisfying a generalized matching condition but otherwise unstructured, and linear parametric uncertainties. It is shown that for systems with the first type of uncertainty, high-gain control laws can result in small tracking errors of O(?), where e is a small design parameter. An alternative scheme based on variable structure control strategy is shown to yield zero tracking errors. Adaptive control techniques are used for systems with linear parametric uncertainties. For systems with relative degree larger than one, a new adaptive control scheme is presented which is considerably simpler than the augmented error scheme suggested previously by Narendra et al. (1978) for linear systems and by Sastry and Isidori (1987) for non-linear systems. Contrary to the augmented error scheme, however, this scheme results in small rather than zero tracking errors.     

Approximate feedback linearization of a class of nonlinear systems with time-varying delays in states via matrix inequality

In this paper, feedback linearization method is proposed for nonlinear systems with a time varying delay in states. The diffeomorphism is presented to linearize the state-delayed nonlinear system if time-varying delay is known. Furthermore, we propose a control scheme to stabilize the approximate feedback linearizable system under the proposed conditions if the first order derivative of timevarying delay and the parametric uncertainties are finite and measurable.     

Adaptive tracking control of uncertain MIMO nonlinear systems with time-varying delays and unmodeled dynamics

In this paper, adaptive neural tracking control is proposed based on radial basis function neural networks (RBFNNs) for a class of multi-input multi-output (MIMO) nonlinear systems with completely unknown control directions, unknown dynamic disturbances, unmodeled dynamics, and uncertainties with time-varying delay. Using the Nussbaum function properties, the unknown control directions are dealt with. By constructing appropriate Lyapunov-Krasovskii functionals, the unknown upper bound functions of the time-varying delay uncertainties are compensated. The proposed control scheme does not need to calculate the integral of the delayed state functions. Using Young s inequality and RBFNNs, the assumption of unmodeled dynamics is relaxed. By theoretical analysis, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded.     

An Intelligent Robust Tracking Control for a Class of Electrically Driven Mobile Robots

This paper addresses the problem of designing robust tracking control for a class of uncertain wheeled mobile robots actuated by brushed direct current motors. This class of electrically‐driven mechanical systems consists of the robot kinematics, the robot dynamics, and the wheel actuator dynamics. Via the backstepping technique, an intelligent robust tracking control scheme that integrates a kinematic controller and an adaptive neural network‐based (or fuzzy‐based) controller is developed such that all of the states and signals of the closed‐loop system are bounded and the tracking error can be made as small as possible. Two adaptive approximation systems are constructed to learn the behaviors of unknown mechanical and electrical dynamics. The effects of both the approximation errors and the unmodeled time‐varying perturbations in the input and virtual‐input weighting matrices are counteracted by suitably tuning the control gains. Consequently, the robust control scheme developed here can be employed to handle a broader class of electrically‐driven wheeled mobile robots in the presence of high‐degree time‐varying uncertainties. Finally, a simulation example is given to demonstrate the effectiveness of the developed control scheme.     

Stabilization of Arbitrary Switched Linear Systems With Unknown Time-Varying Delays

We consider continuous time switched systems that are stabilized via a computer. Several factors (sampling, computer computation, communications through a network, etc.) introduce model uncertainties produced by unknown varying feedback delays. These uncertainties can lead to instability when they are not taken into account. Our goal is to construct a switched digital control for continuous time switched systems that is robust to the varying feedback delay problem. The main contribution of this note is to show that the control synthesis problem in the context of unknown time varying delays can be expressed as a problem of stabilizability for uncertain systems with polytopic uncertainties.     

Efficient learning from adaptive control under sufficient excitation

Parameter convergence is desirable in adaptive control as it enhances the overall stability and robustness properties of the closed‐loop system. In existing online historical data (OHD)–driven parameter learning schemes, all OHD are exploited to update parameter estimates such that parameter convergence is guaranteed under a sufficient excitation (SE) condition which is strictly weaker than the classical persistent excitation condition. Nevertheless, the exploitation of all OHD not only results in possible unbounded adaptation but also loses the flexibility of handling slowly time‐varying uncertainties. This paper presents an efficient OHD‐driven parameter learning scheme for adaptive control, where a variable forgetting factor is specifically designed and is equipped with an estimation error feedback such that exponential parameter convergence is achieved under the SE condition without the aforesaid drawbacks. The proposed parameter learning scheme is incorporated with direct adaptive control to construct an OHD‐based composite learning control strategy. Numerical results have verified the effectiveness of the proposed approach.