Nonlinear stabilizing control based on particle swarm optimization with controlled mutation

A. Ishigame
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Abstract

In this paper, a new approach based on Particle Swarm Optimization (PSO) and Lyapunov method is presented to construct nonlinear stabilizing controller using a neural network. The procedure to learn the value of neural network is formulated as min-max problem. And the problem is solved by the co-evolutionary PSO with a controlled mutation that is newly proposed. The PSO is able to generate an optimal set of parameters for neural controller. Then, the proposed neural controller can be satisfied the Lyapunov stability condition and is validated through numerical simulations of stabilizing control problem.
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基于可控突变粒子群优化的非线性稳定控制
本文提出了一种基于粒子群算法和李雅普诺夫方法的神经网络非线性稳定控制器的构造方法。将学习神经网络值的过程表述为最小-最大问题。提出了一种具有可控突变的协同进化粒子群算法。该算法能够为神经控制器生成最优的参数集。然后,所提出的神经控制器能够满足Lyapunov稳定条件,并通过稳定控制问题的数值仿真进行了验证。
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