粒子群算法下间歇过程的2D鲁棒约束预测输出反馈控制

2D robust constrained model predictive output feedback control for batch processes under particle swarm algorithm

中文关键词  间歇过程  时滞  粒子群算法  模型预测控制  输出反馈

英文关键词  batch processes  time delay  particle swarm optimization algorithm  model predictive control  output feedback

中文摘要

      为有效解决间歇过程存在的不确定性、时滞、输入输出受限及状态不可测的问题,本文提出了一种基于粒 子群算法(PSO)的2D鲁棒约束模型预测输出反馈控制方法.首先,引入扩展信息构建等价的维数扩展后的闭环预测 模型;其次,构造带终端约束的优化性能指标函数以研究系统的控制优化问题;进一步,设计依赖时滞上下界的Lya punov函数, 根据稳定性理论可将系统优化转换为线性矩阵不等式(LMIs)的求解问题;接着,分别讨论了在重复性及 非重复性干扰下系统鲁棒渐近稳定的充分条件,设计了相应的输出反馈控制器;然后,利用PSO对系统进行再次优 化寻找更优解;最后,通过注塑过程的仿真实验验证了所设计方法的有效性.

英文摘要

      To solve the problems of uncertainty, time delay, input and output constraints and state unpredictability of batch processes effectively, a 2D robust constrained model predictive output feedback control method based on particle swarm algorithm (PSO) is proposed in this paper. Firstly, an equivalent closed-loop prediction model with extended dimen sion is constructed by introducing extended information. Next, the optimization performance index function with terminal constraints is constructed to study the system’s control optimization problem. Moreover, the Lyapunov function depending on the upper and lower bounds of the time delay is designed, the optimization problem is then converted into the solution problem of the linear matrix inequalities (LMIs) according to the stability theory. And the sufficient conditions for robust asymptotic stability of the closed-loop system under repetitive and nonrepetitive disturbances are discussed and the corre sponding output feedback controllers are designed. Then, the PSO algorithm is used to optimize the system again to find a better solution. Finally, the simulation experiment of the injection process verifies the effectiveness of the designed method.