粒子群优化算法在非线性模型预测控制中的研究应用

提出了一种基于粒子群优化算法(PSO)的非线性模型预测控制（NMPC）。作为NMPC重要组成的滚动优化部分对控制效果的好坏起着关键的作用，因而寻求一种可靠的优化算法十分必要。PSO算法是一种群集智能方法，通过粒子之间的合作与竞争及进化实现对多维复杂空间的高效搜索，属于一类随机全局优化技术,已成功应用于各科学和工程领域。本文在滚动优化部分应用粒子群优化算法来求解预测控制律，对非线性系统施加优化控制，此外，对常规线性递减加权因子ω策略进行了讨论，提出了非线性递减策略，可进一步缩短优化时间和优化精度。仿真实验效果良好，验证了这种优化算法的正确性和有效性。

Application of nonlinear model predictive control based on particle swarm optimization

The objective of nonlinear model predictive control (NMPC) is to select a set offuture control moves in order to minimize the function, based on a desired output trajectory over a prediction horizon. Thus it is necessary to seek a highly efficient optimization method. A new NMPC based on particle swarm optimization (PSO) is presented here. The PSO algorithm employed exhibits good optimization performance. Furthermore the effect of different kinds of inertia weight ω in the PSO is also discussed in this paper. Satisfactory experimental results are obtained and demonstrate the efficacy of the PSO algorithm employed.