时变不确定系统的变时域离线鲁棒预测控制

给出多包描述约束系统的鲁棒调节器的一种新方法.现有的离线方案离线构造一系列的状态反馈控制律,其中每一个控制律是通过将无穷时域的控制输入固定为唯一的状态反馈控制律而得到的.而本文在优化较大椭圆内的控制律时,使下个时刻的状态进入临近的更小的椭圆———在较小的椭圆内部,相应的控制律序列作为局部控制器.因此,本文新方法相当于给出了变时域的预测控制器,可给出更优的控制作用.仿真例子说明了新方法的有效性.     

基于观测器的时滞系统鲁棒控制器的设计

研究了一类不确定时滞系统基于观测器的鲁棒镇定问题,系统的不确定性时变未知且范数有界,目的是设计状态观测器和线性无记忆观测状态反馈控制器,使其能够镇定一类状态和控制输入不确定性时滞系统。基于Lyapunov稳定性理论,采用线形矩阵不等式这一有效工具,给出了系统基于观测状态反馈鲁棒镇定的充分条件,并且利用线形矩阵不等式的解构造了使得系统鲁棒稳定的基于观测状态反馈控制器,所得结果与时滞相关,从而相对减弱了控制器设计的保守性。数值算例表明了所提出的设计方法的有效性。     

约束Hammerstein系统输出反馈非线性预测控制

针对具有状态、输入和中间变量约束的Hammerstein系统,采用两步法控制策略,给出一种新的可保证闭环系统指数稳定的输出反馈非线性模型预测控制算法。基于Hammerstein系统的特殊结构,结合状态观测器给出无约束线性环节的输出反馈最优控制律,通过滚动优化一有限时域的约束优化问题计算实际控制量。给出保证闭环系统指数稳定的充分条件。以工业双环管聚丙烯装置牌号切换控制为例进行仿真,仿真结果验证该算法的有效性和实用性。     

约束Hammerstein系统输出反馈非线性预测控制

针对具有状态、输入和中间变量约束的Hammerstein系统,采用两步法控制策略,给出一种新的可保证闭环系统指数稳定的输出反馈非线性模型预测控制算法.基于Hammerstein系统的特殊结构,结合状态观测器给出无约束线性环节的输出反馈最优控制律,通过滚动优化一有限时域的约束优化问题计算实际控制量.给出保证闭环系统指数稳定的充分条件.以工业双环管聚丙烯装置牌号切换控制为例进行仿真,仿真结果验证该算法的有效性和实用性.     

基于区间二型T-S模糊模型的网络控制系统的输出反馈预测控制

针对干扰作用下的非线性网络控制系统,给出了带一个自由控制作用的输出反馈预测控制方法.首先,利用区间二型T-S模糊模型描述具有参数不确定性的非线性对象,采用马尔科夫链描述系统中的随机丢包过程,由此建立了丢包网络环境下的非线性网络控制系统的数学模型.然后,通过引入二次有界技术得到了干扰作用下网络控制系统的稳定性描述方法,并在此基础上给出了状态观测器的线性矩阵不等式条件.最后,基于估计状态,通过将无穷时域控制作用参数化为一个自由控制作用加一个线性反馈律得到了输出反馈预测控制方法.论文的特色在于构建了在线更新误差椭圆集合的基本方法,满足了约束条件下输出反馈预测控制保证稳定性的要求.仿真例子验证了所提方法的有效性.     

不确定时变时滞系统鲁棒H∞反馈控制器的设计 --LMI方法

针时一类状态和控制输入同时存在不确定性的具有时变的时滞系统，使用线性矩阵不等式方法研究了其H∞反馈控制器的分析与设计。给出系统具有H∞性能一个线性矩阵不等式（LMI）条件。并通过建立受条件约束的线性矩阵不等式的描述，给出系统的一个一次优状态反馈H∞控制律的设计方法。最后用一个算例验证该设计方法的优越性和有效性。     

基于等价输入干扰滑模观测器的磁悬浮球系统模型预测控制

本文针对系统不确定性和外部干扰引起的磁悬浮球系统控制性能下降的问题,提出了一种基于等价输入干扰滑模观测器的模型预测控制(MPC+EIDSMO)方法.首先将原系统转化为EID系统,采用等价输入干扰滑模观测器对EID系统状态变量及等价输入干扰进行估计;然后基于状态估计值设计模型预测控制器,并将等价输入干扰估计值以前馈的方式补偿后得到最终的复合控制律,实现对参考位置跟踪的快速性,准确性以及对总扰动的鲁棒性.值得注意的是,与传统EID结构中的龙伯格观测器相比,等价输入干扰滑模观测器中增加的非线性观测误差反馈项有助于提高状态估计的快速性和精确性.从理论上证明了该系统是全局一致毕竟有界的.仿真和实验结果表明,相较于基于EID观测器的模型预测控制方法和基于龙伯格观测器的积分模型预测控制方法,所提方法提高了磁悬浮球系统的跟踪性能,并且有效的抑制了系统不确定性和外部干扰.     

非一致采样状态反馈预测控制

针对输入更新不等间隔的非一致采样系统,提出一种基于状态观测器的状态反馈预测控制算法。通过提升控制信号,设计了输入输出同周期的非一致采样系统的状态观测器,并且给出了提升控制信号前后观测器系数矩阵的关系。运用线性矩阵不等式方法,解决无穷时域二次型性能指标下的优化问题,给出了状态反馈预测控制器存在的充分条件。仿真结果表明控制算法的有效性。     

基于二维混合模型和状态观测器的重复控制设计

针对一类正则线性系统, 提出一种基于状态观测器和二维混合模型的重复控制系统设计方法. 首先, 通过构造一个状态观测器来重构系统的状态, 建立基于重构状态的线性控制律. 然后, 通过独立地考虑重复控制系统的连续控制过程与离散学习行为, 给出基于状态观测器和重构状态反馈的连续/离散二维混合模型. 针对这个混合模型, 运用二维Lyapunov泛函方法, 以线性矩阵不等式(Linear matrix inequality, LMI)的形式给出重复控制系统存在重复控制器和状态观测器的充分条件, 所给条件可用Matlab工具箱方便地求解. 数值仿真验证了本文所提方法的有效性.     

输入受限系统的滚动时域预测控制

针对输入受限系统,提出滚动时域预测控制方法,用线性矩阵不等式和不变椭圆集处 理输入受限问题,给出稳定性和可行性条件,并用半正定规划求解控制律.仿真例子表明了本文 方法的优点.     

Robust output feedback model predictive control using off-line linear matrix inequalities

A fundamental question about model predictive control (MPC) is its robustness to model uncertainty. In this paper, we present a robust constrained output feedback MPC algorithm that can stabilize plants with both polytopic uncertainty and norm-bound uncertainty. The design procedure involves off-line design of a robust constrained state feedback MPC law and a state estimator using linear matrix inequalities (LMIs). Since we employ an off-line approach for the controller design which gives a sequence of explicit control laws, we are able to analyze the robust stabilizability of the combined control laws and estimator, and by adjusting the design parameters, guarantee robust stability of the closed-loop system in the presence of constraints. The algorithm is illustrated with two examples.     

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.     

An off-line robust MPC algorithm for uncertain polytopic discrete-time systems using polyhedral invariant sets

In this paper, an off-line synthesis approach to robust model predictive control (MPC) using polyhedral invariant sets is presented. Most of the computational burdens are moved off-line by computing a sequence of state feedback control laws corresponding to a sequence of polyhedral invariant sets. At each sampling time, the smallest polyhedral invariant set that the currently measured state can be embedded is determined. The corresponding state feedback control law is then implemented to the process. The controller design is illustrated with two examples. Comparisons between the proposed algorithm and an ellipsoidal off-line robust MPC algorithm have been undertaken. The proposed algorithm yields a substantial expansion of the stabilizable region. Therefore, it can achieve less conservative result as compared to an ellipsoidal off-line robust MPC algorithm.     

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.     

Enlarging the terminal region of quasi-infinite horizon NMPC based on T-S fuzzy model

The paper presents a method for enlarging the terminal region of quasi-infinity horizon nonlinear model predictive control (NMPC) for nonlinear systems with constraints. The main technique builds on the fact that terminal controllers are fictitious and never applied to the system in the quasi-infinite horizon NMPC [1]. Based on T-S fuzzy models of nonlinear systems, we show that a parameter-dependent state feedback law exists such that the corresponding value function and its level set can be served as terminal cost and terminal region. The problem of maximizing the terminal region is formulated as a convex optimization problem based on linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the proposed method.     

具有执行器饱和特性的不确定系统预测控制器设计

针对具有执行器饱和特征的不确定系统,提出了一种带有状态观测器的新型预测控制器设计方法.该方法在滚动优化的每一步,采用带有饱和特性的反馈控制结构得到一个最优控制律.使无穷时域性能指标最小.考虑在状态不完全已知的情况下,设计了带有状态观测器的预测控制器,并通过观测器参数调整使闭环系统渐近稳定.通过仿真实验验证了所设计控制器的有效性.     

$H_infty$Output Feedback Control of Discrete-Time Fuzzy Systems With Application to Chaos Control

This paper presents an observer based$H_infty$output feedback synthesis method for discrete time fuzzy dynamic systems based on a piecewise Lyapunov function. The basic idea of the approach is to design an observer based piecewise linear output feedback control law to guarantee the global stability with$H_infty$performance of the resulting closed-loop fuzzy control systems. It is shown that the controller parameters can be obtained by solving a set of linear matrix inequalities (LMIs) that are numerically feasible with commercially available software. Application to control chaotic systems is given to illustrate the effectiveness and advantages of the proposed method.     

Anti-windup via constrained regulation with observers

A method is presented for the output-feedback control of discrete-time linear systems with hard constraints on state and control variables. Prior work has shown that optimal controllers for constrained systems take the form of a nonlinear feedback law acting on a set-valued state estimate. In this paper, conventional state estimation schemes are used. A nonlinear control law is derived which views the state estimation error as a disturbance. The resulting control law is then used in conjunction with the conventional observer, rather than set-valued observer, to achieve the desired constrained regulation. The significantly reduced real-time computations come at the cost of restricting the controller structure and thereby introducing possible conservatism in the achievable performance. The results are specialized to the problem of anti-windup for systems with control saturations. A “measurement governor” scheme is introduced that alters plant measurements in such a way to improve performance in the presence of controller saturations.