Hybrid Predictive Control Design based on Particle Swarm Optimization and Genetic Algorithm

摘要：

This paper discusses a model predictive control approach to hybrid systems with continuous and discrete inputs. The algorithm, which takes into account a model of a hybrid system, described as Hybrid Automaton. However, to avoid computational complexity and computation time, the nonlinear optimization problem is solved by evolutionary algorithms (EA) such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). We have applied both GA and PSO algorithms for nonlinear optimization in Hybrid Predictive Control (HPC) for the start-up of a Continuous Stirred-Tank Reactor (CSTR). The simulation results show the good performance of approaches and their capability to use in online application.

关键词： component Hybrid Systems Mixed Integer Programming Particle Swarm Optimization Genetic Algorithm

作者: Yaser Mohammad Nezhad Mehdi Shahbazian

作者单位: Department of Instrumentation and Automation Petroleum University of Technology Ahwaz, Iran

会议类型: 国际会议

会议名称: 2011 3rd IEEE International Conference on Computer Research and Development(ICCRD 2011)(2011第三届计算机研究与发展国际会议)

会议地点: 上海

会议语种:英文

页码: 129-134

在线出版日期: 2011-03-11（万方平台首次上网日期，不代表论文的发表时间）