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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Noriega, J.R., Wang, H.: A Direct Adaptive Neural-Network Control for Unknown Nonlinear Systems and Its Application. IEEE Trans. Neural Networks 9, 27–34 (1998)
Yoo, S.J., Choi, Y.H., Park, J.B.: Generalized Predictive Control Based on Self-Recurrent Wavelet Neural Network for Stable Path Tracking of Mobile Robots: Adaptive Learning Rates Approach. IEEE Trans. Circuits and Systems 53, 1381–1394 (2006)
Eberhart, R.C., Kennedy, J.: A New Optimizer Using Particle Swarm Theory. In: Proceedings of the 6th International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43 (1995)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Network, Perth, Australia, pp. 1942–1948 (1995)
Plett, G.L.: Adaptive Inverse Control of Linear and Nonlinear Systems Using Dynamic Neural Networks. IEEE Trans. Neural Networks 14, 360–376 (2003)
Shi, Y., Eberhart, R.C.: Parameter Selection in Particle Swarm Optimization. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 591–600. Springer, Heidelberg (1998)
Shi, Y., Eberhart, R.: An Empirical Study of Particle Swarm Optimization. In: Proceedings of IEEE Congress on Evolutionary Computation, Washington, DC, pp. 1945–1949 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, X., Li, Y. (2007). Enhance Computational Efficiency of Neural Network Predictive Control Using PSO with Controllable Random Exploration Velocity. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_95
Download citation
DOI: https://doi.org/10.1007/978-3-540-72383-7_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72382-0
Online ISBN: 978-3-540-72383-7
eBook Packages: Computer ScienceComputer Science (R0)