Nonlinear stabilizing control based on particle swarm optimization with controlled mutation

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.