鲁棒辨识问题评述

本文综述了一类新的辨识问题－鲁棒辨识问题的研究进展,首先介绍了鲁棒辨识问题产生的背景,然后对各种不同的辨识方法进行了评述,并指出了各自的特点,最后总结了今后的研究方向。     

Modelling and control of non-minimal non-linear realisations of tensegrity systems

The dynamics of tensegrity systems have their simplest mathematical form when the realisation is non-minimal. In this article, the simplicity of this structure is exploited to design controls. A Lyapunov-based approach creates a non-linear feedback control law exploring the tools of linear algebra due to the linearity of the model on a new set of transformed control variables. Explicit solutions are given for different choices of norm minimisation of the instantaneous control. The theory is illustrated through numerical simulations of simple tensegrity structures.     

机器人力传感器研究概况

介绍了用于机器人力控制和主动式顺应控制的几种力传感器,着重分析了几种典型的腕力传感器的弹性体结构和它们的优缺点。     

有界扰动系统高效鲁棒预测控制器设计

具有有界扰动的有约束线性系统是一类常见的不确定系统. 针对此类系统, 本文借鉴扰动不变集方法, 通过离线设计两个椭圆不变集以降低以往设计的保守性, 进而提出一种有界扰动系统的高效鲁棒预测控制器(SD–ERPC)的设计方法. 该方法能够较好地处理扰动对系统的影响, 在减小控制器在线计算量的同时, 扩大原ERPC设计的初始可行域, 且具有较好的控制性能. 文中给出了SD–ERPC控制器可行性和鲁棒稳定性的理论证明,并通过仿真算例验证了该控制器的有效性.     

广义预测控制的鲁棒化改进

利用内模控制结构分析了广义预测控制在未建模动态鲁棒性方面的缺陷,提出采用失配滤波器以增强系统的鲁棒性。针对广义预测控制的特点,提出了次优失配滤波器的简单设计方法。     

机器人鲁棒模型跟踪控制

本文根据非线性系统模型参考控制理论,提出了一种连续的机器人控制方法．这种方法可以保证跟踪误差的有界性,消除不连续控制中的高频颤动现象,同时继承了不连续控制中的鲁棒特性．     

基于鲁棒控制理论的多模型分层切换控制

A new hierarchical switching control system of multiple models based on robust control theory is designed for some plant with large uncertainties. The model set and controller set are designed by robust control theory and the characteristics of robust control system are taken into account. A new kind of switching index function by estimating uncertainty is designed. Furthermore,stability of the closed system is analyzed by the small gain theorem in the sense of exponentially weighted L2 norm. And simulation is done on a plant with both parameter uncertainty and unmodeled dynamics. Both theoretical analysis and simulation results show that this new hierarchical switching control system can control the plant with large uncertainties effectively and has good performance of tracking and stability.     

具有降阶模型的多模型鲁棒自适应控制

传统鲁棒自适应控制由于考虑了实际系统存在的不确定性,在一定程度上扩大了常规自适应控制的应用范围,但是传统鲁棒自适应控制大多只是从系统全局稳定性的角度出发来设计控制器而忽略系统动态和稳态性能,导致其无法在工况多变的实际被控系统中取得令人满意的效果。针对传统鲁棒自适应控制的不足,本文对由ARMA模型描述并包含未建模动态的系统设计了多模型鲁棒自适应控制器。首先采用正则化技术将系统未建模动态转化为系统有界扰动,并在系统降阶模型的基础上根据系统工况的变化设计了多个固定控制器和2个鲁棒自适应控制器,并根据性能指标函数选择最佳控制器作为当前系统控制器以提高系统性能。仿真实验说明当系统存在未建模动态以及系统工况发生变化时,本文设计的控制器能获得较好的控制效果。     

Feedback min–max model predictive control using robust one-step sets

A solution to the infinite-horizon min–max model predictive control (MPC) problem of constrained polytopic systems has recently been defined in terms of a sequence of free control moves over a fixed horizon and a state feedback law in the terminal region using a time-varying terminal cost. The advantage of this formulation is the enlargement of the admissible set of initial states without sacrificing local optimality, but this comes at the expense of higher computational complexity. This article, by means of a counterexample, shows that the robust feasibility and stability properties of such algorithms are not, in general, guaranteed when more than one control move is adopted. For this reason, this work presents a novel formulation of min–max MPC based on the concept of within-horizon feedback and robust contractive set theory that ensures robust stability for any choice of the control horizon. A parameter-dependent feedback extension is also proposed and analysed. The effectiveness of the algorithms is demonstrated with two numerical examples.     

模型不确定系统的鲁棒预测控制

针对一类脉冲传递函数描述的模型不确定系统 ,基于预测控制和鲁棒控制相结合的策略 ,提出一种鲁棒预测控制器的设计方法 ,并讨论了该方法允许的模型摄动鲁棒界 .仿真结果表明文中方法的有效性和理论分析的正确性 .     

广义预测鲁棒自适应控制

本文指出自校正控制中广泛采用的CARMA模型会导致控制系统对确定性扰动和非平稳随机扰动缺乏鲁棒性;在CARMA模型中引入相应于上述扰动的不可控模态,给出了一种广义的CARMA模型;基于内模原理,提出一种新的广义预测鲁棒自适应控制器;并从理论上揭示其鲁棒性机理;给出了数字仿真。理论分析和数字仿真结果表明:这种广义预测自适应控制器对各类扰动和对象参数变化具有很强的鲁棒性。     

具有大不确定性对象的分层切换控制方法

针对大模型不确定性对象的控制问题,提出了一种基于鲁棒控制理论的多模型分层切换控制方法.为减少覆盖不确定性需要的模型数量,采用多个乘性不确定模型来描述对象,并应用LMI方法设计控制器集合.考虑鲁棒控制中常用系统增益来度量不确定性,设计了一种基于不确定性增益估计的切换指标函数,并据此将控制器集合中合适的控制器连接到反馈回路中.理论分析表明系统BIBO稳定,且具有一定的扰动抑制能力.仿真实验结果验证了控制方法的有效性.     

ON THE LYAPUNOV APPROACH TO ROBUST STABILIZATION OF UNCERTAIN NONLINEAR SYSTEMS

In the Lyapunov approach employed in this paper, known in the literature as Lyapunov control, or min-max control, robust, global uniform asymptotic stability is achieved by a discontinuous control law which ensures that the Lyapunov derivative is negative despite bounded uncertainty. For that, it is assumed that the uncertainties satisfy certain matching conditions, and that a Lyapunov function for the nominal plant is available. To obtain lower control magnitudes, this paper develops control laws which counter the uncertainties on a component-wise basis, rather than the usual normic one. Both the basic discontinuous control law, which is proved to provide robust global uniform asymptotic stability, and a continuous app roximation, which is proved to ensure global uniform ultimate boundedness, are derived. Application to model following is given. We adapt recent results on robust quadratic stabilization of nominally linear time-invariant plants subjected to nonlinear, bo unded and unmatched uncertain perturbations, to extend our results to this important class of systems; this is illustrated by two examples.     

Dynamic output feedback robust MPC with convex optimisation for system with polytopic uncertainty

This paper proposes a dynamic output feedback robust model predictive control for system with polytopic model uncertainty. Earlier researches on this topic utilise iterative methods to solve the non-convex optimisation problem which are computationally demanding. In order to reduce the computational burden, we explore a new approach in this paper, where, by utilising some proper matrix transformations, a computationally more efficient but conservative convex optimisation problem is formulated which can be solved in terms of linear matrix inequalities. Furthermore, we try to reduce the conservativeness by introducing a nonsingular matrix as a degree of freedom. The recursive feasibility and the convergence of the augmented state to the equilibrium point are guaranteed. The effectiveness of the proposed approach is illustrated by two numerical examples.