一类含参数不确定性混沌系统的控制

给出一类含参数不确定性混沌系统的控制器设计方法 .用该方法设计的控制器 ,能够使系统消除混沌并鲁棒渐近稳定到任何期望的光滑轨道 .设计过程无需预先知道未知参数的估计值和精确值 .直接构造混沌控制器 ,方法简单 .仿真验证了其有效性     

交直流并联输电系统的非线性鲁棒控制

针对含有常参数不确定性的交直流输电系统,使用自适应逆推方法设计了直流系统的非线性鲁棒控制器.基于该方法的设计,无需对原系统进行反馈线性化,并能保证闭环误差系统渐近稳定.设计过程表明逆推方法设计的控制器拥有更优越的性能.     

多电机驱动系统的保性能鲁棒Funnel控制

本文针对含参数不确定性的多电机驱动系统,提出一种基于最优保性能鲁棒的Funnel控制方法实现系统的规定跟踪性能.该控制方法通过构造Funnel函数对误差系统进行变换,并设计自适应反步控制器保证变换后系统的稳定性即可使跟踪误差的瞬态和稳态响应均被限制在给定的Funnel边界内.然而由于系统中存在的参数不确定性会影响系统的规定控制性能,本文在Funnel控制基础上又设计了最优保性能鲁棒控制器.它是通过将参数不确定性系统的保性能鲁棒控制问题转化为标称系统的最优控制问题,并求解新的黎卡提方程而得到的.因此所设计的控制器不但消除了参数不确定性对系统的影响并且能够使系统的性能指标达到一确定的上界.最后,对四电机驱动系统进行了仿真和实验验证,说明所提出控制方法的有效性.     

关联模糊大系统的保性能控制器设计: LM I 方法

考虑具有参数不确定性的连续时间关联模糊大系统的保性能控制问题.基于LMI方法,给出了该不确定性模糊大系统分散状态反馈保性能控制器的设计方案.所设计的分散状态反馈保性能控制器,不但使得该不确定性模糊大系统闭环渐近稳定,而且使其二次型性能指标的上界最小.     

不确定离散时滞系统的时滞相关非脆弱H_∞控制

讨论不确定离散时滞系统非脆弱控制器的设计问题.利用Lyapunov-Krasovskii稳定性理论和有限和不等式方法,获得了不确定时滞系统在非脆弱控制器作用下不仅内部渐近稳定,而且具有给定的H∞扰动抑制水平y的时滞相关有界实条件.采用迭代算法分别给出了控制器具有加法不确定性和乘法不确定性两种情况的非脆弱控制器参数的设计方法,借助于Matlab的LMI工具箱可以方便求解.数值仿真实例表明了该方法的有效性.     

具有传感器故障的非线性关联大系统的可靠控制

考虑一类非线性关联大系统的可靠控制器设计问题,该系统具有常时滞、参数不确定性、非线性扰动和传感器故障.其中参数不确定性满足匹配条件,非线性扰动满足范数有界条件,传感器具有部分失效的模型.本文的目的是设计无记忆分散控制器来镇定该系统.通过解线性矩阵不等式获得此控制器,使得当传感器器发生故障时被控系统能够保持渐近稳定.最后通过仿真的例子,验证该状态反馈控制器的可行性.     

一类不确定非线性系统的鲁棒自适应控制

针对一类具有一般不确定性和未知参数的非线性系统,设计出一种适用于输出跟踪 的鲁棒自适应控制器.该控制器对系统的参数和状态的不确定性具有鲁棒性,能保证闭环系 统的全局稳定性,并解决了ε-跟踪问题.仿真实例表明,所设计的鲁棒自适应控制器具有良好 的跟踪性能.     

范数有界型参数不确定性系统的概率鲁棒H∞控制方法

利用确定性鲁棒控制方法对参数摄动的最坏情况进行研究,设计出的控制器具有较大的保守性和较高的控制成本.针对这一问题,建立范数有界型参数不确定性系统模型,分析系统性能的置信概率与参数不确定性随机向量的范数边界之间的关系,提出一种基于概率估计的鲁棒H∞控制方法,该方法能以有限的迭代步数给出一个与系统性能置信概率相关的鲁棒H∞控制器的可行解.最后通过仿真实例验证了所提出方法的有效性.     

考虑输入约束的半主动悬架非线性自适应控制

针对具有输入约束及参数不确定性问题的汽车半主动悬架系统,提出一种考虑输入饱和的非线性自适应Backstepping控制器.该方法引入一个辅助系统,通过设计新的误差变量,实现对控制饱和的补偿,解决控制输入的幅值约束问题.同时,考虑到悬架系统的参数不确定性问题,采用映射自适应算法设计自适应律,通过构造适当的Lyapunov函数,保证悬架系统的稳定性.仿真结果表明,所设计的控制器具有良好的隔振性能,而且能够有效降低输入约束和不确定参数对系统性能的影响.     

基于神经网络干扰观测器的一类不确定非线性MIMO系统H_∞跟踪控制

针对一类具有未知外部干扰及内部不确定性的非线性MIMO系统,提出了基于神经网络干扰观测器的鲁棒跟踪控制方法,用于降低控制器对干扰的要求.设计了基于神经网络的干扰观测器,以逼近由外部干扰、内部不确定性和子系统的交叉耦合组成的复合干扰.根据Lyapunov稳定性理论的参数更新律及所设计的控制器,保证了系统中所有信号的最终一致有界性,并获得了给定的跟踪性能指标.仿真结果证明了该方法的有效性.     

Dynamic observer-based controllers for linear uncertain systems

This paper addresses robust controller design for uncertain linear systems via a dynamic observer-based controller. A dynamic observer is an alternative structure for a classical observer which can be regarded as a general form of a usual observer and has additional degrees of freedom in the observer structure. Using this new observer structure, a new observer-based controller for linear systems is proposed. Some strict linear matrix inequalities (LMIs) have been given to find the dynamic observer parameters and controller gain. It is shown that dynamic observer can be used effectively for tackling the drawbacks of the classical observer-based robust controller design methods. As an advantage, LMIs are derived even in the presence of uncertainties in system, input and output matrices simultaneously, whereas by using the traditional observer, bilinear matrix inequalities (BMIs) are given in the presence of such uncertainties. Moreover, the proposed LMIs do not imply the equality constraint. Simulation results are used to illustrate the effectiveness of the proposed design technique.     

一类2-D不确定离散系统的弹性保成本控制

当被控系统的数学模型存在不确定性时,需要设计鲁棒控制器才能使得受控系统稳定,然而,如果控制器本身也存在不确定性时,系统就会变得复杂难以控制,使用传统的鲁棒控制方法很难达到期望的控制目标,甚至不能保证受控系统的稳定性.本研究就是针对当系统模型和控制器同时存在不确定性时,给出了设计稳定控制器的简便方法.通过将控制器的不确定性分别表示为加法式和乘法式摄动,研究了以上两种系统的弹性保成本控制问题,并给出了相应控制器的设计方法.在主要结果推导过程中,巧妙运用了各种矩阵不等式放缩和等价参数变换等数学方法,最终将主要结果表示为线性矩阵不等式(LMI),利用Matlab的LMI工具箱,可以很方便地设计所需要的控制器.最后,对同一个受控系统,分别施加利用本文结果和已有结果设计的控制器,发现前者可以很好地控制系统,而后者却不能使受控系统稳定,从而验证了所得结果的有效性.     

Adaptive terminal sliding mode control for rigid robotic manipulators

In order to apply the terminal sliding mode control to robot manipulators, prior knowledge of the exact upper bound of parameter uncertainties, and external disturbances is necessary. However, this bound will not be easily determined because of the complexity and unpredictability of the structure of uncertainties in the dynamics of the robot. To resolve this problem in robot control, we propose a new robust adaptive terminal sliding mode control for tracking problems in robotic manipulators. By applying this adaptive controller, prior knowledge is not required because the controller is able to estimate the upper bound of uncertainties and disturbances. Also, the proposed controller can eliminate the chattering effect without losing the robustness property. The stability of the control algorithm can be easily verified by using Lyapunov theory. The proposed controller is tested in simulation on a two-degree-of-freedom robot to prove its effectiveness.     

A synthesis framework for robust gain-scheduling controllers

We present a general framework for the systematic synthesis of robust gain-scheduling controllers by convex optimization techniques and for uncertain dynamical systems described by standard linear fractional representations. We distinguish between linear time-varying parameters, which are assumed to be available online as scheduling parameters for the controller, and genuine uncertainties, not necessarily time-varying, parametric or linear, that are not available online. Under the rough hypothesis that the control channel is not affected by the unmeasurable uncertainties and that the properties of the uncertainties and scheduling variables are captured by suitable families of integral quadratic constraints, this paper reveals how controller synthesis can be turned into a genuine semi-definite program. The design framework is shown to encompass a rich class of concrete scenarios.     

Robust adaptive control for a nonholonomic mobile robot with unknown parameters

A robust adaptive controller for a nonholonomic mobile robot with unknown kinematic and dynamic parameters is proposed. A kinematic controller whose output is the input of the relevant dynamic controller is provided by using the concept of backstepping. An adaptive algorithm is developed in the kinematic controller to approximate the unknown kinematic parameters, and a simple single-layer neural network is used to express the highly nonlinear robot dynamics in terms of the known and unknown parameters. In order to attenuate the effects of the uncertainties and disturbances on tracking performance, a sliding mode control term is added to the dynamic controller. In the deterministic design of feedback controllers for the uncertain dynamic systems, upper bounds on the norm of the uncertainties are an important clue to guarantee the stability of the closed-loop system. However, sometimes these upper bounds may not be easily obtained because of the complexity of the structure of the uncertainties. Thereby, simple adaptation laws are proposed to approximate upper bounds on the norm of the uncertainties to address this problem. The stability of the proposed control system is shown through the Lyapunov method. Lastly, a design example for a mobile robot with two actuated wheels is provided and the feasibility of the controller is demonstrated by numerical simulations.     

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.     

Adaptive multiple-surface sliding control for non-autonomous systems with mismatched uncertainties

In this paper, we propose a sliding mode-based controller for a class of single-input single-output nonlinear systems with mismatched uncertainties whose variation bounds are not given. The concept of multiple-surface sliding control is used to cope with the uncertainty mismatch problem, and the function approximation technique is introduced to transform the uncertainties into a finite combination of orthonormal basis functions. An adaptive controller can thus be designed using the Lyapunov approach to achieve output error convergence and boundedness of all signals. Simulation results of a benchmark problem have verified the performance and feasibility of the proposed control strategy.     

A Robust Tracking Controller for Electrically Driven Robot Manipulators: Stability Analysis and Experiment

 In this paper, a robust controller for electrically driven robotic systems is developed. The controller is designed in a backstepping manner. The main features of the controller are: 1) Control strategy is developed at the voltage level and can deal with both mechanical and electrical uncertainties. 2) The proposed control law removes the restriction of previous robust methods on the upper bound of system uncertainties. 3) It also benefits from global asymptotic stability in the Lyapunov sense. It is worth to mention that the proposed controller can be utilized for constrained and nonconstrained robotic systems. The effectiveness of the proposed controller is verified by simulations for a two link robot manipulator and a four-bar linkage. In addition to simulation results, experimental results on a two link serial manipulator are included to demonstrate the performance of the proposed controller in tracking a given trajectory.     

A framework for brain learning-based control of smart structures

A novel framework for intelligent structural control is proposed using reinforcement learning. In this approach, a deep neural network learns how to improve structural responses using feedback control. The effectiveness of the framework is demonstrated in a case study for a moment frame subjected to earthquake excitations. The performance of the learning method was improved by proposing a state-selector function that prevented the neural network from forgetting key states. Results show that the controller significantly improves structural responses not only to earthquake records on which it was trained but also to earthquake records new to the controller. The controller also has stable performance under environmental uncertainties. This capability distinguishes the proposed approach and makes it more appropriate for the situations in which it is likely that the controller will be exposed to unpredictable external excitations and high degrees of uncertainties.     

Design of minimax robust LQG controllers under parameter and noise uncertainties

The problem of design of minimax robust LQG controllers for linear systems with parameter and noise uncertainties is considered in this paper. Necessary and sufficient conditions for converting this problem to a two-person, zero-sum continuous game problem are presented. A simple procedure for design of a suboptimal minimax robust LQG controller, i.e., the LQG controller for least-favourable model, is proposed. Necessary and sufficient conditions for the existence of a saddle point are established. Under these conditions, the controller obtained is exactly the minimax LQG controller. When there does not exist a saddle point, the worst-case error between the controller obtained and the minimax robust LQG controllers under described uncertainties is bounded.