About this Physics and Astronomy article
Model-free Adaptive Control Optimization Using a Chaotic Particle Swarm Approach by Leandro dos Santos Coelho; Antonio Augusto Rodrigues Coelho is a Physics and Astronomy article available to read on EtoBox.
It is well known that conventional control theories are widely suited for applications where the processes can be reasonably described in advance. However, when the plant's dynamics are hard to characterize precisely or are subject to environmental uncertainties, one may encounter difficulties in applying the conventional controller design methodologies. Despite the difficulty in achieving high control performance, the fine tuning of controller parameters is a tedious task that always requires experts with knowledge in both control theory and process information. Nowadays, more and more studies have focused on the development of adaptive control algorithms that can be directly applied to complex processes whose dynamics are poorly modeled and/or have severe nonlinearities. In this context, the design of a Model-Free Learning Adaptive Control (MFLAC) based on pseudogradient concepts and optimization procedure by a Particle Swarm Optimization (PSO) approach using constriction coefficient and Hénon chaotic sequences (CPSOH) is presented in this paper. PSO is a stochastic global optimization technique inspired by social behavior of bird flocking. The PSO models the exploration of a pro
It is typically read by researchers, students, and practitioners in Physics and Astronomy.
- Author
- Leandro dos Santos Coelho; Antonio Augusto Rodrigues Coelho
- Publisher
- Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0960-0779)
- Published
- 2009
- Language
- EN
- Field
- Physics and Astronomy (Physical Sciences)