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Parameter identification using adaptive differential evolution algorithm applied to robust control of uncertain nonlinear systems
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DOI：10.1016/j.asoc.2018.07.015.png)
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Abstract
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This paper investigates the dynamic parametric identification of the uncertain inverted pendulum system perturbed with friction by using an adaptive differential evolution (ADE) algorithm. In ADE approach, the initiation is realized in the mutant step with a new mutation scheme, namely adaptive mutant structure, contained multi-mutant vectors including 'best/1' and 'best/2' or 'rand/1' and 'rand/2' for selecting target vectors in population. The modification that aims to equalize between global exploration and local exploitation capacities which helps to effectively search global potential optimum solutions. The performance of ADE algorithm is compared with those of standard differential evolution (DE), particle swarm optimization (PSO) and genetic algorithm (GA). Furthermore, the identification results are applied to design a swing-up and balancing controller for the inverted pendulum system perturbed with friction. The sliding mode controller is used to swing up the inverted pendulum system to the top equilibrium position, and the LQR controller is initially applied for balancing and control the position of the first link of the inverted Pendulum in the downright position. The experimental results demonstrate that the proposed approach can accurately identify and robust control such nonlinear dynamic systems. (c) 2018 Elsevier B.V. All rights reserved.
Keywords:
Adaptive differential evolution (ADE) algorithm
Inverted pendulum system
Dynamic parametric estimation
Swing-up and balancing controller
Parametric-model based LQR controla
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