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

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.