基于粒子群优化的线性/非线性约束过程广义预测控制

Zenghui Wang, Yanxia Sun
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引用次数: 9

摘要

针对具有约束的线性或非线性过程,提出了一种基于粒子群算法的智能广义预测控制器(GPC)。我们从工程的角度提出了几个约束条件,并简化了成本函数。由于粒子群算法的特点,没有使用复杂的数学运算。该方法易于用于具有线性或/或非线性约束的对象的控制。数值仿真分别证明了该控制技术对线性和非线性过程的控制效果。在第一个仿真中,控制信号是基于自适应线性模型计算的。在第二次仿真中,提出的方法是基于非线性对象的固定神经网络模型。结果表明,所提出的控制方案能够保证良好的控制性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generalized predictive control based on particle swarm optimization for linear/nonlinear process with constraints
This paper presents an intelligent generalized predictive controller (GPC) based on particle swarm optimization (PSO) for linear or nonlinear process with constraints. We propose several constraints for the plants from the engineering point of view and the cost function is also simplified. No complicated mathematics is used which originated from the characteristics of PSO. This method is easy to be used to control the plants with linear or/and nonlinear constraints. Numerical simulations are used to show the performance of this control technique for linear and nonlinear processes, respectively. In the first simulation, the control signal is computed based on an adaptive linear model. In the second simulation, the proposed method is based on a fixed neural network model for a nonlinear plant. Both of them show that the proposed control scheme can guarantee a good control performance.
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