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Optimal tuning of sliding mode controller parameters using LQR input trend

Azad, R. K ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. DOI: 10.1109/IS.2012.6335151
  3. Publisher: 2012
  4. Abstract:
  5. This paper presents a novel method fortuning the parameters of an sliding mode (SM) controller to obtain near-optimal performance. In order to do so the Linear Quadratic Regulator (LQR) was implemented on a linearized system. The input history of the LQR was used as a reference to obtain an optimal space for sliding mode controller parameters. Afterwards, the optimal space boundaries were dedicated to Genetic Algorithm (GA) to search for the optimal parameter for the nonlinear model. Also, the center of the obtained optimal space was used as an initial guess to the Particle Swarm Optimization (PSO) Algorithm. The proposed algorithm was implemented to regulate SM controller for the attitude control of a virtual satellite with uncertainly on inertia matrix. The proposed method also eliminates the heavy burden of trial and error and promises to deliver near-optimal performance that is considered as an important merit of the present study
  6. Keywords:
  7. Genetic Algorithm ; Optimal parameter tuning ; Particle swarm optimization ; Inertia matrix ; Initial guess ; Linear quadratic regulator ; Linearized systems ; Near-optimal performance ; Non-linear model ; Optimal parameter ; Optimal space ; Optimal tuning ; Particle swarm optimization algorithm ; Sliding mode controller ; Sliding modes ; Trial and error ; Virtual satellite ; Attitude control ; Intelligent systems ; Particle swarm optimization (PSO) ; Sliding mode control ; Genetic algorithms
  8. Source: IS'2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings ; 2012 , Pages 297-303 ; 9781467327824 (ISBN)
  9. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6335151