scholarly journals Integrating Particle Swarm Optimization with Analytical Nonlinear Model Predictive Control for Nonlinear Hybrid Systems

2015 ◽  
Author(s):  
Jean Thomas
Keyword(s):  
Particle Swarm Optimization ◽  
Model Predictive Control ◽  
Hybrid Systems ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Particle Swarm ◽  
Nonlinear Model Predictive Control ◽  
Swarm Optimization ◽  
Nonlinear Hybrid

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

2017 ◽  
Vol 14 (6) ◽  
pp. 509-521 ◽  
Author(s):  
Halim Merabti ◽  
Khaled Belarbi
Keyword(s):  
Particle Swarm Optimization ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Computation Time ◽  
Unmanned Vehicles ◽  
Nonlinear Model Predictive Control ◽  
Swarm Optimization ◽  
Content Type ◽  
Solution Methods

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.

scholarly journals Model Predictive Control of Duplex Inlet and Outlet Ball Mill System Based on Parameter Adaptive Particle Swarm Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Leihua Feng ◽  
Feng Yang ◽  
Wei Zhang ◽  
Hong Tian
Keyword(s):  
Particle Swarm Optimization ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Optimization Algorithm ◽  
Ball Mill ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Better Regulation ◽  
Parameter Adaptive

The direct-fired system with duplex inlet and outlet ball mill has strong hysteresis and nonlinearity. The original control system is difficult to meet the requirements. Model predictive control (MPC) method is designed for delay problems, but, as the most commonly used rolling optimization method, particle swarm optimization (PSO) has the defects of easy to fall into local minimum and non-adjustable parameters. Firstly, a LS-SVM model of mill output is established and is verified by simulation in this paper. Then, a particle similarity function is proposed, and based on this function a parameter adaptive particle swarm optimization algorithm (HPAPSO) is proposed. In this new method, the weights and acceleration coefficients of PSO are dynamically adjusted. It is verified by two common test functions through Matlab software that its convergence speed is faster and convergence accuracy is higher than standard PSO. Finally, this new optimization algorithm is combined with MPC for solving control problem of mill system. The MPC based on HPAPSO (HPAPSO-MPC) algorithms is compared with MPC based on PAPSO (PAPSO-MPC) and PID control method through simulation experiments. The results show that HPAPSO-MPC method is more accurate and can achieve better regulation performance than PAPSO-MPC and PID method.

Automatic Tuning of Model Predictive Control Using Particle Swarm Optimization

2007 ◽  
Author(s):  
Ryohei Suzuki ◽  
Fukiko Kawai ◽  
Hideyuki Ito ◽  
Chikashi Nakazawa ◽  
Yoshikazu Fukuyama ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Particle Swarm ◽  
Automatic Tuning ◽  
Swarm Optimization
Sign in / Sign up

Export Citation Format

Share Document