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

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 pa...

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Bibliographic Details
Published in:World journal of engineering 2017-12, Vol.14 (6), p.509-521
Main Authors: Merabti, Halim, Belarbi, Khaled
Format: Article
Language:English
Subjects:
Algorithms
Computing time
Control methods
Linear programming
Mathematical models
Nonlinear control
Obstacle avoidance
Particle swarm optimization
Predictive control
Robot control
Robots
Tracking control
Trajectories
Unmanned vehicles
Velocity
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Summary: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 AµPSO-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 AµPSO 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.
ISSN:1708-5284
2515-8082
DOI:10.1108/WJE-01-2017-0004