线性时变不确定时滞系统的鲁棒H∞ 控制

本文主要研究了状态和控制同时存在滞后的线性时变不确定时滞系统的鲁棒H∞控制．问题，给出了对所有容许不确定性，被控对象可二次镇定和满足从于扰输入到控制输出的H∞范数界约束的无记忆状态反馈鲁棒H∞控制分析结果，得到了确保鲁棒H∞控制器存在的充分条件．文中进一步把不确定系统的鲁棒H∞控制器设计问题等价为线性时不变系统的状态反馈标准H∞控制问题，并由此得到鲁棒H∞控制器综合设计方法．     

时延网络控制系统的模型预测控制

针对有输入约束的不确定时延网络控制系统,提出鲁棒模型预测控制方法;其中,将不确定时延建模为范数有界的输入矩阵的不确定性．给出了鲁棒性能指标的上界和系统渐近稳定的充分条件,通过在线求解LMI凸优化问题得到状态反馈控制律．仿真例子验证了该方法的有效性．     

基于观测器的不确定动态时滞系统鲁棒镇定的Riccati方程方法

本文给出了一类不确定动态时滞系统基于观测器的鲁棒镇定方法，该类系统的状态、输人和输出矩阵均含有不确定性，且不确定性需满足给定的匹配条件，系统不仅有状态时滞，还有控制时滞，该方法通过求解两个代数Riccati方程实现，本文比较全面地解决了满足匹配条件的不确定动态时滞系统的鲁棒镇定问题．     

一类不确定非线性系统的指数鲁棒输出跟踪

研究一类不确定非线性系统的鲁棒输出跟踪控制问题。应用输入／输出反馈线性化法和李亚普诺夫方法,提出一种基于不确定项上界的连续型鲁棒输出跟踪控制器设计方法。该控制器不仅可确保闭环系统的状态一致最终有界,使系统输出按指数规律跟踪期望输出,而且计算简单,更易实现。仿真结果证明了该方法的可行性与有效性。     

状态时滞时变不确定系统的鲁棒H∞ 输出反馈控制器设计

主要研究了存在状态滞后的线性时变不确定时滞系统的鲁棒∞控制分析和综合问题,给出了对所有容许不确定性,被控对象可二次镇定和满足从干扰输入到控制输出的Ｈ∞范数界约束的动态输入出反馈鲁棒Ｈ∞控制分析结果,将不确定时滞系统的鲁棒Ｈ∞输出反馈控制器设问题等价对两个线性时不变系统的状态反馈标准Ｈ∞控制问题,并由此得到反馈阵和观测阵,了终得到鲁棒Ｈ∞控制器综合设计方法。     

一类不确定离散系统的严格正实分析和设计

考虑了一类不确定离散多变量系统严格正实分析和控制问题,其中不确定参数具有非负性.分析了系统鲁棒稳定且严格正实的条件,讨论了状态反馈、输出反馈使闭环系统鲁棒稳定且严格正实的问题.将不确定系统的鲁棒严格正实分析和设计转化为确定系统的严格正实分析和控制,得到了系统鲁棒稳定且严格正实的充分必要条件.最后给出了鲁棒严格正实控制问题的可解条件及控制器的综合方法.     

时变时滞不确定系统的鲁棒输出反馈控制

研究了时变时滞不确定系统基于状态观测器的动态输出反馈实现鲁棒镇定的分 析和综合问题.所研究的系统不仅同时包含时变状态时滞和时变控制时滞,而且包含时变未 知且有界不确定参数.提出了确保该系统可通过输出反馈鲁棒镇定的充分条件,并将该充分 条件转化为线性矩阵不等式(LMI)问题,最终通过求解两个LMI来构造输出反馈控制律.     

具有自适应噪声边界的Tube可达集鲁棒预测控制

针对过程噪声设定边界与真实噪声边界失配的有界干扰离散线性不确定系统,提出一种具有自适应噪声边界的Tube可达集鲁棒模型预测控制方法.首先,该算法引入基于MIT规则的自适应集员滤波在线估计系统状态和噪声边界.其次,基于估计值,通过迭代自适应集员滤波的时间更新部分计算出预测时域内闭环不确定系统状态的可达集.最后,用可达集代替不变集并根据Tube鲁棒模型预测控制策略,给出了实际不确定系统的控制律,确保系统状态鲁棒渐近稳定,并收敛于终端干扰不变集.仿真结果验证了该控制方法的有效性.     

移动机器人的鲁棒输出跟踪

本文讨论了一类不确定非完整系统的鲁棒输出跟踪问题。首先给出了在适当条件下受限系统的降阶状态实现及有关性质；进而给出了三轮移动机器人在纯滚动与非打滑条件下的简化模型，并结合变结构控制方法对该模型给出了具体的鲁棒输出跟踪控制规律。     

独立摄动参数不确定线性系统的正实控制

考虑了一类范数有界参数不确定线性系统的鲁棒正实性分析和设计问题，其中参数不确定性是独立摄动的．通过构造增广系统将不确定系统的鲁棒正实分析和控制问题转化为确定系统的情形，给出了鲁棒正实分析问题的线性矩阵不等式解法，导出了输出反馈控制器的存在条件．所得结论将范数有界参数不确定系统的鲁棒正实分析和控制的现有结果推进了一步．     

Synthesis of Mixed Objective Output Feedback Robust Model Predictive Control

Aiming at the constrained polytopic uncertain system with energy‐bounded disturbance and unmeasurable states, a novel synthesis scheme to design the output feedback robust model predictive control(MPC)is put forward by using mixed H₂/H_∞ design approach. The proposed scheme involves an offline design of a robust state observer using linear matrix inequalities(LMIs)and an online output feedback robust MPC algorithm using the estimated states in which the desired mixed objective robust output feedback controllers are cast into efficiently tractable LMI‐based convex optimization problems. In addition, the closed‐loop stability and the recursive feasibility of the proposed robust MPC are guaranteed through an appropriate reformulation of the estimation error bound (EEB). A numerical example subject to input constraints illustrates the effectiveness of the proposed controller.     

An optimal approach to output‐feedback robust model predictive control of LPV systems with disturbances

An observer‐based output feedback predictive control approach is proposed for linear parameter varying systems with norm‐bounded external disturbances. Sufficient and necessary robust positively invariant set conditions of the state estimation error are developed to determine the minimal ellipsoidal robust positively invariant set and observer gain through offline computation. The quadratic upper bound of state estimation error is updated and included in an ‐type cost function of predictive control to optimize transient output feedback control performance. Recursive feasibility of the dynamic convex optimization problem is guaranteed in the proposed predictive control strategy. With the input‐to‐state stable observer, the closed‐loop control system states are steered into a bounded set. Simulation results are given to demonstrate the effectiveness of the proposed control strategy. Copyright © 2016 John Wiley & Sons, Ltd.     

Markov跳变系统的输出反馈鲁棒预测控制

对离散时间Markov跳变系统, 当系统状态不完全可测时, 研究了一类基于输出反馈的鲁棒模型预测控制问题. 所研究系统为准线性参数时变的, 考虑在当前时刻系统的时变参数是已知的, 将来时刻未知的情况. 综合考虑系统存在多胞不确定性和有界噪声等因素, 通过运用线性矩阵不等式方法及变量变换思想, 将无穷时域性能指标的最小最大鲁棒预测控制问题转化为具有线性矩阵不等式约束的凸优化问题, 得到了系统的输出反馈控制律. 引入二次有界概念, 在满足输入输出约束的情况下, 保证闭环系统的随机稳定性. 数值算例验证了方法的有效性.     

A gradient flow approach to on-line robust pole assignment for synthesizing output feedback control systems

Pole assignment is a basic design method for synthesis of feedback control systems. In this paper, a gradient flow approach is presented for robust pole assignment in synthesizing output feedback control systems. The proposed approach is shown to be capable of synthesizing linear output feedback control systems via on-line robust pole assignment. Convergence of the gradient flow can be guaranteed. Moreover, with appropriate design parameters the gradient flow converges exponentially to an optimal solution to the robust pole assignment problem and the closed-loop control system based on the gradient flow is globally exponentially stable. These desired properties make it possible to apply the proposed approach to slowly time-varying linear control systems. Simulation results are shown to demonstrate the effectiveness and advantages of the proposed approach.     

Output feedback model predictive control for LPV systems based on quasi-min–max algorithm

This paper proposes a robust output feedback model predictive control (MPC) scheme for linear parameter varying (LPV) systems based on a quasi-min–max algorithm. This approach involves an off-line design of a robust state observer for LPV systems using linear matrix inequality (LMI) and an on-line robust output feedback MPC algorithm using the estimated state. The proposed MPC method for LPV systems is applicable for a variety of systems with constraints and guarantees the robust stability of the output feedback systems. A numerical example for an LPV system subject to input constraints is given to demonstrate its effectiveness.     

基于啤酒发酵过程的时变轨迹输出反馈鲁棒模糊预测控制

考虑到实际生产中状态不易测量和设定值变化的情况以及系统本身的非线性特性,针对啤酒发酵过程温度控制系统提出了一种时变轨迹下输出反馈鲁棒模糊预测控制方法。在啤酒发酵罐温度系统的机理模型的基础上,建立包括不确定性和未知干扰的状态空间模型;通过设计模糊集,建立为具有加权系数的T-S模糊状态空间模型;并在状态变量的中引入输出跟踪误差,建立新型多自由度状态空间模型;并运用鲁棒模型预测控制方法优化参数不确定性问题,结合李雅普诺夫稳定性理论推导出线性矩阵不等式形式的稳定性条件,通过求解线性矩阵不等式中参数来计算对应子模型控制律,并对所设计的输出反馈控制器给定权值。通过仿真结果验证了提出方法的有效性和可行性。     

Output tracking of constrained nonlinear processes with offset-free input-to-state stable fuzzy predictive control

This paper develops an efficient offset-free output feedback predictive control approach to nonlinear processes based on their approximate fuzzy models as well as an integrating disturbance model. The estimated disturbance signals account for all the plant-model mismatch and unmodeled plant disturbances. An augmented piecewise observer, constructed by solving some linear matrix inequalities, is used to estimate the system states and the lumped disturbances. Based on the reference from an online constrained target generator, the fuzzy model predictive control law can be easily obtained by solving a convex semi-definite programming optimization problem subject to several linear matrix inequalities. The resulting closed-loop system is guaranteed to be input-to-state stable even in the presence of observer estimation error. The zero offset output tracking property of the proposed control approach is proved, and subsequently demonstrated by the simulation results on a strongly nonlinear benchmark plant.     

Adaptive neural network output feedback control of stochastic nonlinear systems with dynamical uncertainties

In this paper, a robust adaptive neural network (NN) backstepping output feedback control approach is proposed for a class of uncertain stochastic nonlinear systems with unknown nonlinear functions, unmodeled dynamics, dynamical uncertainties and without requiring the measurements of the states. The NNs are used to approximate the unknown nonlinear functions, and a filter observer is designed for estimating the unmeasured states. To solve the problem of the dynamical uncertainties, the changing supply function is incorporated into the backstepping recursive design technique, and a new robust adaptive NN output feedback control approach is constructed. It is mathematically proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are semi-globally uniformly ultimately bounded in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by choosing design parameters appropriately. The simulation example and comparison results further justify the effectiveness of the proposed approach.     

Output feedback robust MPC with one free control move for the linear polytopic uncertain system with bounded disturbance

In this paper a previous approach, for the robust model predictive control (MPC) for a linear polytopic uncertain system, is extended to the case with bounded disturbance and unmeasurable state. The controller on-line optimizes a free control move followed by an output feedback control law based on the pre-specified state estimator. A key technique for this controller is an appropriate formulation of the estimation error bound which accounts for recursive feasibility of the optimization problem. The quadratic boundedness (QB) of the augmented state is guaranteed by the proposed approach. A numerical example is given to illustrate the effectiveness of the proposed controller.