Enhance Computational Efficiency of Neural Network Predictive Control Using PSO with Controllable Random Exploration Velocity

Authors:
Xin Chen;Yangmin Li
Affiliations:
Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Av. Padre Tomás Pereira S.J., Taipa, Macao SAR, P.R.China;Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Av. Padre Tomás Pereira S.J., Taipa, Macao SAR, P.R.China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

Citing 3
Cited 1

Parameter Selection in Particle Swarm Optimization

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
A direct adaptive neural-network control for unknown nonlinear systems and its application

IEEE Transactions on Neural Networks
Adaptive inverse control of linear and nonlinear systems using dynamic neural networks

IEEE Transactions on Neural Networks

Particle swarm optimization with disagreements

ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

NNPC has been used widely to control nonlinear systems. However traditional gradient decent algorithm (GDA) needs a large computational cost, so that NNPC is not acceptable for systems with rapid dynamics. To apply NNPC in fast control of mobile robots, the paper proposes an improved optimization technique, particle swarm optimization with controllable random exploration velocity (PSO-CREV), to replace of GDA in NNPC. Therefore for one cycle of control, PSO-CREV needs less iterations than GDA, and less population size than conventional PSO. Hence the computational cost of NNPC is reduced by using PSO-CREV, so that NNPC using PSO-CREV is more feasible for the control of rapid processes. As an example, a test of trajectory tracking using mobile robots is chosen to compare performance of PSO-CREV with other algorithms to show its advantages, especially on the aspect of computational time.