VSS global performance improvement based on AW concepts

The influence of the reaching mode on the global performance of variable structure systems (VSS) undergoing sliding regimes is stressed. A comparative analysis between the behaviour during this reaching mode of operation and the problem of windup is realized. Based on the similarities between both control problems, some tools of the control theory of constrained linear systems are exploited to improve the reaching mode of VSS.     

一类具有变时滞不确定分布参数系统的滑动模控制

研究一类不确定变时滞分布参数系统的滑动模控制问题．首先设计一种无记忆功能的变结构控制器;然后分析了在滑动模切换面上滑动模控制系统关于不确定量的不变性特征;最后给出了从任意初始位置出发的轨线到达滑动模态区的时间估计．研究表明,所设计的控制器结构简单．容易实现．     

Optimal and suboptimal smoothing algorithms for identification of time-varying systems with randomly drifting parameters

Noncausal estimation algorithms, which involve smoothing, can be used for off-line identification of nonstationary systems. Since smoothing is based on both past and future data, it offers increased accuracy compared to causal (tracking) estimation schemes, incorporating past data only. It is shown that efficient smoothing variants of the popular exponentially weighted least squares and Kalman filter-based parameter trackers can be obtained by means of backward-time filtering of the estimates yielded by both algorithms. When system parameters drift according to the random walk model and the adaptation gain is sufficiently small, the properly tuned two-stage Kalman filtering/smoothing algorithm, derived in the paper, achieves the Cramér-Rao type lower smoothing bound, i.e. it is the optimal noncausal estimation scheme. Under the same circumstances performance of the modified exponentially weighted least-squares algorithm is often only slightly inferior to that of the Kalman filter-based smoother.     

控制增益符号未知的MIMO时滞系统自适应控制

针对一类带有死区模型并具有未知函数控制增益的不确定MIMO非线性时滞系统,基于滑模控制原理和Nussbaum函数的性质,提出了一种稳定的自适应神经网络控制方案.该方案放宽了对函数控制增益上界为未知常数的假设,并通过使用Lyapunov-Krasovskii泛函抵消了因未知时变时滞带来的系统不确定性.理论分析证明,闭环系统是半全局一致终结有界.仿真结果表明了该方法的有效性.     

Robust sliding mode control of general time-varying delay stochastic systems with structural uncertainties

This paper presents a new robust sliding mode control （SMC） method with well-developed theoretical proof for general uncertain time-varying delay stochastic systems with structural uncertainties and the Brownian noise （Wiener process）. The key features of the proposed method are to apply singular value decomposition （SVD） to all structural uncertainties and to introduce adjustable parameters for control design along with the SMC method. It leads to a less-conservative condition for robust stability and a new robust controller for the general uncertain stochastic systems via linear matrix inequality （LMI） forms. The system states are able to reach the SMC switching surface as guaranteed in probability 1. Furthermore, it is theoretically proved that the proposed method with the SVD and adjustable parameters is less conservatism than the method without the SVD. The paper is mainly to provide all strict theoretical proofs for the method and results.     

Variable structure control with sliding mode prediction for discrete-time nonlinear systems

A new variable structure control algorithm based on sliding mode prediction for a class of discrete-time nonlinear systems is presented. By employing a special model to predict future sliding mode value, and combining feedback correction and receding horizon optimization methods which are extensively applied on predictive control strategy, a discrete-time variable structure control law is constructed. The closed-loop systems are proved to have robustness to uncertainties with unspecified boundaries. Numerical simulation and pendulum experiment results illustrate that the closed-loop systems possess desired performance, such as strong robustness, fast convergence and chattering elimination.     

Set-based adaptive estimation for a class of nonlinear systems with time-varying parameters

In this paper, an adaptive estimation technique is proposed for the estimation of time-varying parameters for a class of continuous-time nonlinear system. A set-based adaptive estimation is used to estimate the time-varying parameters along with an uncertainty set. The proposed method is such that the uncertainty set update is guaranteed to contain the true value of the parameters. Unlike existing techniques that rely on the use of polynomial approximations of the time-varying behaviour of the parameters, the proposed technique does require a functional representation of the time-varying behaviour of the parameter estimates. A simulation example and a building systems estimation example are considered to illustrate the developed procedure and ascertain the theoretical results.     

一种新的机器人轨迹跟踪滑模变结构控制

该文在传统变结构控制趋近律的基础上,提出一种快速平滑趋近律,并作仿真比较;同时对其滑动模态作改进,引入非线性项,提出一种快速收敛滑动模态。在选择滑动模态的切换方式后,将新的变结构控制器应用于机器人的轨迹跟踪控制中,在对系统的抗干扰能力进行分析后,作了相应的仿真研究。     

线性多变量离散系统全程滑模变结构控制

讨论线性多变量离散不确定系统滑模变结构控制问题。利用全程滑动模态的思想，通过构造切换函数，使系统的轨线一开始便落在切换面上，缩短了到达时间，增强了系统的鲁棒性；利用系统的摄动、干扰与采样时间的关系，改进了控制律的设计，使结论条件更符合物理意义。仿真实例说明了该方法的有效性。     

Adaptive sliding mode control with moving sliding surface

A continuous sliding mode control with moving sliding surface for nonlinear systems of arbitrary order is presented in this paper. The sliding surface is moved repetitively toward the target sliding surface in order to ensure that the system trajectory is close to the actual surface during the whole control process. The parameters of sliding mode control are tuned by a fuzzy logic. The proposed procedure reduces the time when the system operates in the approaching phase during which the control performance is deteriorated since the system is more susceptible to external disturbances and model uncertainties. The effectiveness of the presented approach is demonstrated on a control of a flexible robot manipulator arm.     

一类由偏微分方程描述时滞随机系统的变结构控制

研究了一类由偏微分方程描述的Ito型时变时滞随机系统的变结构控制问题．首先构造了系统的滑动流形,设计了变结构控制律;然后证明T系统的滑动模具有次可达性,并且利用Halanay不等式的方法给出了系统滑动模运动为均方稳定运动的一个充分条件．     

Co-states for sliding mode design in linear systems

A control design for linear systems with uncertainties is presented where a particular time-varying sliding mode manifold is specified utilizing co-states of the plant. We introduce design methods for both continuous time and sampled data systems. It is shown that robustness of the closed-loop systems with continuous time control degrades when an equivalent sampled data control is used.     

Adaptive inverse compensation for actuator backlash with piecewise time-varying parameters

Existing adaptive inverse compensation methods for cancelling actuator backlash nonlinearity are all restricted to handle constant backlash parameters. In other words, when discontinuity and time variation as both ubiquitous phenomena in practical actuators exist, such inverse compensation methods are no longer applicable theoretically. So far, no result has been reported in addressing such an issue, regardless of its importance in practice. In this paper, we solve this problem by developing a new piecewise Lyapunov function analysis and using parameter projection adaptation mechanism. Based on such approaches, an adaptive inverse compensation control scheme is designed to compensate for piecewise time-varying actuator backlash nonlinearity. It is proved that all signals of closed-loop system are ensured bounded. Moreover, the steady-state error is bounded by an adjustable scalar approaching to zero arbitrarily. Simulation also illustrates the obtained theoretical results.     

Backstepping design for time-varying nonlinear systems with unknown parameters

A simple backstepping design procedure is proposed and sufficient conditions for global partial state- and dynamic- feedback stabilization for a class of triangular systems with unknown time-varying parameters are derived.     

Subspace identification of Bilinear and LPV systems for open- and closed-loop data

In this paper we present a novel algorithm to identify LPV systems with affine parameter dependence operating under open- and closed-loop conditions. A factorization is introduced which makes it possible to form a predictor that predicts the output, which is based on past inputs, outputs, and scheduling data. The predictor contains the LPV equivalent of the Markov parameters. Using this predictor, ideas from closed-loop LTI identification are developed to estimate the state sequence from which the LPV system matrices can be constructed. A numerically efficient implementation is presented using the kernel method. It turns out that if structure is present in the scheduling sequence the computational complexity reduces even more.     

基于混合遗传算法的时滞和参数的在线辨识

针对遗传算法应用于时变时滞和参数在线辨识时，存在无法兼顾收敛速度与辨识精度的缺点，提出一种遗传算法和单神经元有机结合的混合遗传算法。利用GA的全局最优性在整个空间搜索可能的极值，而用单神经元的误差梯度下降特性在极值点附近快速搜索，从而达到全局最优与快速搜索的有机结合，提高了收敛速度和辨识精度。并对混合遗传算法进行了改进，使之更适用于在线辨识。仿真结果表明，改进的混合遗传算法用于在线辨识有效且实用。     

多变量系统变结构控制的一个滑模到达条件

为了削弱变结构控制系统的抖振,提出一个新的滑动模态到达条件.分析了该到达条件参数对系统动态性能的影响,讨论了参数的物理实现,并给出了到达时间的简化计算公式.仿真结果表明,该到达条件能有效地抑制抖振.     

外激励参数未知系统的同步控制及其参数识别

考虑外激励的振幅和频率未知的混沌系统.把系统未知参数扩张成系统的新的状态量,构造新的驱动系统.用参数自适应控制方法构造一个响应系统,使之与驱动系统的结构相同.基于级联系统的稳定性理论,通过两个步骤来设计控制器和参数自适应律,使得驱动—响应系统能够达到完全同步.当驱动—响应系统达到完全同步时,就可以识别出系统的未知参数振...     

A comment on ‘A time-varying sliding surface for fast and robust tracking control of second-order uncertain systems’

In a recent paper, Seung-Bok Choi, Dong-Won Park and Suhada Jayasuriya (Automatica, 30, 899–904, 1994) have presented a stepwise time-varying sliding surface for variable structure control of a class of second-order uncertain systems. As the surface does not truly guarantee insensitivity of the systems to parameter variations and external disturbances, in this comment we propose a continuously time-varying surface that allows faster tracking and really guarantees robust behaviour of the systems.     

一类线性时变系统组合自适应迭代学习辨识

针对一类在有限时间区间上可重复运行的既含时变参数又含时不变参数的高阶线性时变系统,提出了一种模型参考组合自适应迭代学习参数辨识算法.应用Lyapunov方法,给出了时不变参数的时域自适应学习律和时变参数的迭代域自适应学习律,分析了参数估计和模型状态跟踪误差的有界性与收敛性.该算法适于时变和时不变参数并存的线性系统的参数辨识,可加快参数估计的收敛速度.仿真例子验证了所提出的辨识算法的有效性.