Hybrid predictive control design with mixed inputs based on PSO and its application for control of a Batch Reactor

Abstract

In this work, we propose a combined approach based on Particle Swarm Optimization (PSO) and Sequential Quadratic Programming (SQP) for solving the real-time optimization problem appearing in the hybrid predictive control of systems with continuous and discrete inputs. The conventional PSO for continuous problems is mixed with binary PSO for handling discrete variables, and after iterations of the algorithm have ended, SQP is applied for refining the continuous variables. This new method (PSO+SQP) is favorably compared with a conventional non-linear optimization techniques (based on Branch and Bound and Explicit Enumeration) and PSO. Also, a multi-objective approach is considered as a tuning method for the population size and number of iterations for PSO. All these algorithms are applied for the temperature control of a Batch Reactor.