Accelerated micro particle swarm optimization for the solution of nonlinear model predictive control

摘要

Purpose: Rapid solution methods are still a challenge for difficult optimization problems among them those arising in nonlinear model predictive control. The particle swarm optimization algorithm has shown its potential for the solution of some problems with an acceptable computation time. In this paper, we use an accelerated version of PSO for the solution of simple and multiobjective nonlinear MBPC for unmanned vehicles (mobile robots and quadcopter) for tracking trajectories and obstacle avoidance. The AmPSO-NMPC was applied to control a LEGO mobile robot for the tracking of a trajectory without and with obstacles avoidance one. Design/methodology/approach: The accelerated PSO and the NMPC are used to control unmanned vehicles for tracking trajectories and obstacle avoidance. Findings: The results of the experiments are very promising and show that AmPSO can be considered as an alternative to the classical solution methods. Originality/value: The computation time is less than 0.02 ms using an Intel Core i7 with 8GB of RAM.