基于离散时变趋近律的准滑模控制

针对不确定离散时间系统,提出一种基于离散时变趋近律的准滑模控制方法.该方法不仅能够加快系统趋近模态的趋近速度,缩短到达时间,而且能够减小准滑动模态带宽,有效改善系统动态品质,增强系统鲁棒性,并无控制抖振和稳态抖振产生.控制律形式简单、利于实现.仿真结果表明了所提出方法的有效性.     

基于扰动补偿趋近律的准滑模控制

针对不确定离散时间系统,提出了一种基于扰动补偿趋近律的准滑模控制方法.该方法不仅能够加快系统趋近模态的趋近速度,缩短到达时间,而且能够缩窄系统的准滑动模态带宽,增强系统鲁棒性,有效改善系统动态品质,并无控制抖振和稳态抖振产生.仿真结果表明了该方法的有效性.     

无抖振离散准滑模控制

基于对常规离散准滑动模态及其抖振的分析,提出将离散趋近等效控制相结合的控制策略,既保证了趋近模态具有良好品质,又降低了准滑动模态带。该控制策略可避免系统状态步步反向穿越滑动面的抖振运动,从面消除了控制的抖振,并以较少的控制能量获得较好的系统性能。     

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.     

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。     

一种改进的离散时间系统变结构控制设计方法

在分析了几种离散变结构趋近律趋近过程的基础上, 提出了一种改进的趋近律, 该方法能够大幅度削弱抖振, 使系统运动最终趋于原点且不存在稳态振荡, 能使系统的准滑动模态保持步步穿越切换面的基本属性, 改善了趋近过程.     

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。     

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,使系统运动最终趋于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．

     

基于幂次趋近律的一类离散时间系统的变结构控制

针对一类离散时间系统,提出一种变结构控制设计方法.通过构造幂次趋近律,使得系统的准滑动模态不仅能保持步步穿越切换面的基本属性,而且能大幅度削弱抖振,有效地改善控制品质,提高系统的鲁棒性.采样周期越短,该控制方法的效果越明显.仿真实例表明了所设计控制器的可行性和有效性.     

Adaptive and Robust Stabilization of Feedforward Nonlinear Systems Driven by Symmetric and Non‐symmetric Dead‐zone Inputs

The stabilization of feedforward nonlinear systems subject to hard‐input nonlinearities is a challenging problem due to the presence of input uncertainties. This paper deals with adaptive control of a class of feedforward nonlinear systems driven by unknown dead‐zone inputs. The unknown dead‐zone input nonlinearity is assumed to be either symmetric or non‐symmetric. The control design is based on the combination of the invariant‐manifold stabilization technique with the classical adaptive and robust compensation methods. Simulation results showed that the presence of the dead‐zone inputs in the system dynamics can be handled even for arbitrary large dead‐zone parameters.     

Robust adaptive output feedback control of a class of discrete‐time nonlinear systems with nonlinear uncertainties and unknown control directions

In this paper, robust adaptive output feedback control is studied for a class of discrete‐time nonlinear systems with functional nonlinear uncertainties of the Lipschitz type and unknown control directions. In order to construct an output feedback control, the system is transformed into the form of a nonlinear autoregressive moving average with eXogenous inputs (NARMAX) model. In order to avoid the noncausal problem in the control design, future output prediction laws and parameter update laws with the dead‐zone technique are constructed on the basis of the NARMAX model. With the employment of the predicted future outputs, a constructive output feedback adaptive control is proposed, where the discrete Nussbaum gain technique and the dead‐zone technique are used in parameter update laws. The effect of the functional nonlinear uncertainties is compensated for, such that an asymptotic tracking performance is achieved, whereas other signals in the closed‐loop systems are guaranteed to be bounded. Simulation studies are performed to demonstrate the effectiveness of the proposed approach. Copyright © 2012 John Wiley & Sons, Ltd.     

Evolutionary algorithms for continuous-space optimisation

From a global viewpoint, evolutionary algorithms (EAs) working on continuous search-spaces can be regarded as homogeneous Markov chains (MCs) with discrete time and continuous state. We analyse from this viewpoint the (1?+?1)EA on the inclined plane fitness landscape, and derive a closed-form expression for the probability of occupancy of an arbitrary target zone, at an arbitrary iteration of the EA. For the hitting-time of an arbitrary target zone, we provide lower and upper bounds, as well as an asymptotic limit. Discretization leads to an MC with discrete time, whose simple structure is exploited to carry out efficient numerical investigations of the theoretical results obtained. The numerical results thoroughly confirm the theoretical ones, and also suggest various conjectures which go beyond the theory.     

互质因子摄动系统最优l1鲁棒控制:连续性与自适应控制

针对具有互质因子摄动和未知干扰的离散时间系统研究了一种自适应鲁棒控制策略. 本文的主要工作包括三个方面.首先建立了互质因子摄动系统最优l1鲁棒控制设计的连续性. 然后,提出了一种带变死区的参数鲁棒估计投影算法.最后,结合所提出的参数估计算法和最优 l1鲁棒控制,利用确定性等价原理提出了互质因子摄动系统的一种新的自适应鲁棒控制方法.基 于本文建立的l1优化设计的连续性,证明了自适应鲁棒控制的全局稳定性,给出了自适应控制系 统稳定性的后验可计算条件.     

时滞离散系统的非线性准滑模鲁棒控制

针对一类变时滞离散系统,基于一种新型的非线性时变准滑模面设计方案,研究了系统的鲁棒滑模控制问题.首先,给出了该种非线性切换函数的一般形式,该类准滑模面具有时变的特征,且能够动态地改善系统的运动品质.利用自由权矩阵与线性矩阵不等式技术,给出了该类时滞离散系统非线性准滑模面的设计方法,并得到了稳定的非线性准滑模面存在的充分条件;其次,基于一种改进的离散趋近律方法,设计了相应的准滑模控制器,以保证系统的状态在有限时间内到达准滑模,从而将此类非线性系统的滑模变结构控制的分析与综合问题推广到了时滞非线性系统.最后,仿真结果表明,在本文所设计的准滑模面与准滑模变结构控制器的作用下,系统的状态是稳定的,且具有响应速度快、超调量小、调节时间较小等优点,从而说明了本文所设计方法的有效性.     

Adaptive neural control for a class of stochastic nonlinear time‐delay systems with unknown dead zone using dynamic surface technique

This paper investigates the problem of adaptive control for a class of stochastic nonlinear time‐delay systems with unknown dead zone. A neural network‐based adaptive control scheme is developed by using the dynamic surface control (DSC) technique and the minimal learning parameters algorithm. The dynamic surface control technique, which can avoid the problem of ‘explosion of complexity’ inherent in the conventional backstepping design procedure, is first extended to the stochastic nonlinear time‐delay system with unknown dead zone. The unknown nonlinearities are approximated by the function approximation technique using the radial basis function neural network. For the purpose of reducing the numbers of parameters, which are updated online for each subsystem in the process of approximating the unknown functions, the minimal learning parameters algorithm is then introduced. Also, the adverse effects of unknown time‐delay are removed by using the appropriate Lyapunov–Krasovskii functionals. In addition, the proposed control scheme is systematically derived without requiring any information on the boundedness of the dead zone parameters and avoids the possible controller singularity problem in the approximation‐based adaptive control schemes with feedback linearization technique. It is shown that the proposed control approach can guarantee that all the signals of the closed‐loop system are bounded in probability, and the tracking errors can be made arbitrary small by choosing the suitable design parameters. Finally, a simulation example is provided to illustrate the performance of the proposed control scheme. Copyright © 2015 John Wiley & Sons, Ltd.     

Prescribed performance adaptive fault tolerant control approach for nonlinear large-scale systems with time delay interconnection and dead zone input

In this study, a prescribed performance adaptive fault tolerant tracking control scheme is presented for a class of nonlinear large-scale systems with time delay interconnection, dead zone input, and actuator fault. The radial basis function neural networks are used to approximate unknown nonlinear functions. Different from the barrier Lyapunov functions used to achieve the symmetrical prescribed performance, a new error transformation is introduced in this study to achieve the desired asymmetrical prescribed performance. In addition, Nussbaum function is introduced to solve the difficulties caused by dead zone input and actuator fault. Based on the appropriate Lyapunov–Krasovskii functions, the effect of time delay interconnection could be compensated. By using backstepping procedures, an adaptive fault tolerant tracking control approach is developed for the considered large-scale systems, and the stability of the closed-loop systems is analyzed by Lyapunov theory. Meanwhile, the prescribed performance of the tracking error could be guaranteed. Finally, the effectiveness of the proposed control approach is illustrated by two simulation examples.     

Adaptive fuzzy ILC of nonlinear discrete-time systems with unknown dead zones and control directions

This paper presents an adaptive fuzzy iterative learning control (ILC) design for non-parametrized nonlinear discrete-time systems with unknown input dead zones and control directions. In the proposed adaptive fuzzy ILC algorithm, a fuzzy logic system (FLS) is used to approximate the desired control signal, and an additional adaptive mechanism is designed to compensate for the unknown input dead zone. In dealing with the unknown control direction of the nonlinear discrete-time system, a discrete Nussbaum gain technique is exploited along the iteration axis and applied to the adaptive fuzzy ILC algorithm. As a result, it is proved that the proposed adaptive fuzzy ILC scheme can drive the ILC tracking errors beyond the initial time instants into a tunable residual set as iteration number goes to infinity, and keep all the system signals bounded in the adaptive ILC process. Finally, a simulation example is used to demonstrate the feasibility and effectiveness of the adaptive fuzzy ILC scheme.