离线鲁棒预测控制器综合方法的改进方案

对离线鲁棒预测控制器综合方法进行改进.离线鲁棒预测控制算法离线确定一个控制律序列,对应一组吸引域;在线根据当前状态位于哪个吸引域内部(或哪两个吸引域之间)选择相应的一个控制律(或相邻的两个控制律的线性插值).引入参数Lyapunov函数得到多包椭圆型吸引域和多包二次稳定的离线控制律,改进了控制器的可行性和最优性.改进的控制器保持了稳定性以及控制律关于系统状态的连续性.     

基于多面体不变集的离线鲁棒预测控制器综合

提出一种基于多面体不变集的离线鲁棒预测控制器综合算法.该算法离线确定一组反馈控制律及其对应的不变集,在线控制时根据当前状态所处的位置选择相应的控制律,不仅扩大了初始可行域,还能在一定程度上改善控制性能.仿真结果表明了采用多面体不变集的优越性.     

基于多面体不变集的离线鲁棒预测控制器综合

提出一种基于多面体不变集的离线鲁棒预测控制器综合算法.该算法离线确定一组反馈控制律及其对应的不变集,在线控制时根据当前状态所处的位置选择相应的控制律,不仅扩大了初始可行域,还能在一定程度上改善控制性能.仿真结果表明了采用多面体不变集的优越性.

     

约束非线性系统构造性模型预测控制

研究了连续时间约束非线性系统模型预测控制设计.利用控制Lyapunov函数离线构造单变量可调预测控制器,再根据性能指标在线优化可调参数,其中该参数近似于闭环系统的"衰减率".同时,控制Lyapunov函数保证了算法的可行性和闭环系统的稳定性.最后通过数值仿真验证了该算法的有效性.     

动态输出反馈鲁棒模型预测控制离线算法

研究具有多包不确定性和有界噪声系统的动态输出反馈鲁棒模型预测控制(Robust model predictive control, RMPC)的离线方法. 先前的在线方法中, 在估计状态和估计误差集合已知的情况下, 在每一采样时刻通过近似最优算法求解控制器参数. 本文采用先前的方法计算离线控制器参数和吸引域. 首先, 选定一系列估计状态, 其中,每个估计状态对应同样一组嵌套的估计误差集合. 然后,针对每一估计状态和每一估计误差集合的组合,离线计算唯一的控制器参数和对应的吸引域. 这些控制器参数和对应的吸引域存储在表中. 如果离线确定的吸引域包含实时的扩展状态, 则该离线控制器参数是实时可行的. 在线时, 根据实时估计状态和选取实时估计误差集合, 在表中搜索包含实时扩展状态且优化性能指标最小的吸引域所对应的控制器参数. 通过连续搅拌釜式反应器控制系统验证了该方法的有效性.     

变约束高效预测控制

为改善离线鲁棒预测控制算法的最优性引入了变终端约束集的思想,即在原离线方法基础上通过在线优化方法获得构建当前反馈矩阵的凸组合系数,然后再产生当前控制量.与原方法相比增加了在线求解的自由度,改善了系统的最优性.在此基础上,又提出了变约束预测控制(VC-MPC)算法,根据状态所处的不同稳定椭圆域确定其对应的控制约束,在取得...     

约束非线性系统构造性模型预测控制

研究了连续时间约束非线性系统模型预测控制设计. 利用控制 Lyapunov函数离线构造单变量可调预测控制器,再根据性能指标在线优化可调参数,其中该参数近似于闭环系统的,衰减率,同时,控制 Lyapunov函数保证了算法的可行性和闭环系统的稳定性 .最后通过数值仿真验证了该算法的有效性.

     

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.     

基于多步控制集的鲁棒预测控制器设计

针对有约束多胞不确定系统, 本文提出多步控制集的概念, 并将其作为终端集进而设计鲁棒预测控制器. 由于设计了一系列可变的反馈律, 鲁棒预测控制器可以得到更好的控制性能和更大的初始可行域. 另外, 利用多步控制集的特性, 本文提出了一种将预测控制器的在线计算量转移到离线完成的算法. 通过该算法, 可以有效地平衡鲁棒预测控制器的控制性能、在线计算量和初始可行域. 仿真算例验证了这些算法的有效性.     

多输入预报自校正前馈控制器及其在高频焊管焊接过程控制中的应用

本文针对高频焊管焊接过程的控制,提出了一种适用于具有多种扰动对象的多输入预报自校正前馈控制器,其特点是综合了广义最小方差控制的最优性、极点配置控制的鲁棒性和多输入前馈控制的抗扰性等优点。该控制器采用间接算法实现,在线选择二次型性能指标中加权项。它不仅可以消除可测干扰的影响,消除稳态跟踪误差,而且即使用于非最小相位系统也具有全局收敛特性。理论分析和仿真实验表明,提出的控制器具有良好的控制性能,可以适应焊接过程环境的变化,从而获得满意的控温效果。     

Optimizing the end-point state-weighting matrix in model-based predictive control

In this paper a linear model-based predictive control (MPC) algorithm is presented, for which nominal closed-loop stability is guaranteed. The input is obtained by minimizing a quadratic performance index over a finite horizon plus an end-point state (EPS) penalty, subject to input, state and output constraints. Under certain conditions, the weighting matrix in the EPS penalty enables one to specify an invariant ellipsoid in which the input, state and output constraints are satisfied. In existing MPC algorithms this weighting matrix is calculated off-line. The main contribution of this paper is to incorporate the calculation of the EPS-weighting matrix into the on-line optimization problem of the controller. The main advantage of this approach is that a natural and automatic trade-off between feasibility and optimality is obtained. This is demonstrated in a simulation example.     

Move blocked model predictive control with improved optimality using semi-explicit approach for applying time-varying blocking structure

Move blocking is an input parameterization scheme that fixes the decision variables over arbitrary time intervals, commonly referred to as blocks, and it is widely implemented in model predictive control (MPC) to reduce the computational load during on-line optimization. Since the blocking position acts as the search direction in the solution space, selection of the blocking structure has a significant effect on the optimality of moved blocked MPC. However, existing move blocked MPC schemes apply arbitrary time-invariant blocking structures without considering the optimality of the blocking structure due to the difficulty in deriving a proper time-varying blocking structure on-line. Thus, we propose a semi-explicit approach for move blocked MPC that solves a multiparametric program for the blocking position set off-line and a simplified on-line optimization problem. This approach allows for a proper time-varying blocking structure for the current state on-line. The proposed approach can efficiently improve the optimality performance of move blocked MPC with only a little additional computational cost for critical region search while guaranteeing the recursive feasibility and closed-loop stability.     

Indirect adaptive techniques for fixed controller performance enhancement

This paper develops the adaptive disturbance estimate feedback schemes of a companion paper for enhancing the performance of controllers designed by off-line techniques. The developments are based on the parametrization theory for the class of all stabilizing controllers for a nominal plant, and the dual class of plants stabilized by a nominal controller. Such parametrization allows us conveniently to parametrize plant uncertainties for on-line identification and control purposes, minimizing the effects of unmodelled dynamics. Based on these parametrizations, along with prefiltering which minimizes the effect of unmodelled dynamics, standard adaptive stabilization, adaptive pole assignment, or adaptive linear quadratic schemes are shown to achieve controller enhancement. The idea is to exploit a priori information about a plant and design objectives in an off-line design, and yet exploit the power of adaptive techniques to learn and tune on-line. Attention is focused on techniques for fixed but uncertain plants.     

Nash-optimization enhanced distributed model predictive control applied to the Shell benchmark problem

This paper presents an efficient distributed model predictive control scheme based on Nash optimality, in which the on-line optimization of the whole system is decomposed into that of several small co-operative agents in distributed structures, thus it can significantly reduce computational complexity in model predictive control of large-scale systems. The relevant nominal stability and the performance on single-step horizon under the communication failure are investigated. The Shell heavy oil fractionator benchmark control problem is illustrated to verify the effectiveness of the proposed control algorithm.     

Design of robust model-based controllers via parametric programming

In this paper a method is presented for deriving the explicit robust model-based optimal control law for constrained linear dynamic systems. The controller is derived off-line via parametric programming before any actual process implementation takes place. The proposed control scheme guarantees feasible operation in the presence of bounded input uncertainties by (i) explicitly incorporating in the controller design stage a set of feasibility constraints and (ii) minimizing the nominal performance, or the expectation of the performance over the uncertainty space. An extension of the method to problems involving target point tracking in the presence of persistent disturbances is also discussed. The general concept is illustrated with two examples.