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Model-free adaptive control optimization using a chaotic particle swarm approach

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  • dos Santos Coelho, Leandro
  • Coelho, Antonio Augusto Rodrigues

Abstract

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 pseudo-gradient 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 problem space by a population of particles. Each particle in PSO has a randomized velocity associated to it, which moves through the space of the problem. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed CPSOH introduces chaos mapping which introduces some flexibility in particle movements in each iteration. The chaotic sequences allow also explorations at early stages and exploitations at later stages during the search procedure of CPSOH. Motivation for application of CPSOH approach is to overcome the limitation of the conventional MFLAC design, which cannot guarantee satisfactory control performance when the plant has different gains for the operational range when designed by trial-and-error by user. Numerical results of the MFLAC with CPSOH tuning for a nonlinear distillation column model are showed.

Suggested Citation

  • dos Santos Coelho, Leandro & Coelho, Antonio Augusto Rodrigues, 2009. "Model-free adaptive control optimization using a chaotic particle swarm approach," Chaos, Solitons & Fractals, Elsevier, vol. 41(4), pages 2001-2009.
  • Handle: RePEc:eee:chsofr:v:41:y:2009:i:4:p:2001-2009
    DOI: 10.1016/j.chaos.2008.08.004
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    References listed on IDEAS

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    1. Liu, Bo & Wang, Ling & Jin, Yi-Hui & Tang, Fang & Huang, De-Xian, 2005. "Improved particle swarm optimization combined with chaos," Chaos, Solitons & Fractals, Elsevier, vol. 25(5), pages 1261-1271.
    2. Layeghi, Hamed & Arjmand, Mehdi Tabe & Salarieh, Hassan & Alasty, Aria, 2008. "Stabilizing periodic orbits of chaotic systems using fuzzy adaptive sliding mode control," Chaos, Solitons & Fractals, Elsevier, vol. 37(4), pages 1125-1135.
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    6. Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2009. "A novel chaotic particle swarm optimization approach using Hénon map and implicit filtering local search for economic load dispatch," Chaos, Solitons & Fractals, Elsevier, vol. 39(2), pages 510-518.
    7. Bonakdar, Mohammad & Samadi, Mostafa & Salarieh, Hassan & Alasty, Aria, 2008. "Stabilizing periodic orbits of chaotic systems using fuzzy control of Poincaré map," Chaos, Solitons & Fractals, Elsevier, vol. 36(3), pages 682-693.
    8. Mohammad Shahrokhi, 2011. "Adaptive Control of Chaos," Chapters, in: Esteban Tlelo-Cuautle (ed.), Chaotic Systems, IntechOpen.
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