Particle-swarm optimization algorithm for model predictive control of MIMO with constraints

The constraints of output variables, input variables and intermediate variables exist widely in chemical process control. The inconsistency in different constraints may make constrained model predictive controller have no feasible solutions, which will bring harmful effect to practical production. To ensure the implementation of model predictive control, using its global optimization performance and constraint handling mechanism, a new particle-swarm optimization algorithm with the function of constraint handling, was proposed in this article. Taking into account the form of constraints and the constraints characteristics of MIMO (multi-input multi-output) predictive control system, this thesis, based on convex polyhedron geometry, discuss the feasibility of constrained model predictive control. Combined with duality theorem, the output constraints of system are transformed into constraints of input. After that, the constraints form which meets the requirements of control algorithm is obtained. Finally, particle swarm optimization algorithm is used to conduct the optimization of predictive control system. The simulation results of MIMO model with constraints showed the advantages and effectiveness of this algorithm.