Abstract
Aiming at the problem of parameter adjustment in nonlinear system controller, an improved particle swarm optimization (PSO) algorithm is used to optimize parameters. Aiming at the defect that PSO algorithm will fall into local optimum in the early iteration, a nonlinear dynamic inertia weight method based on quadratic exponential is proposed. And the distance between the particle and the global optimum is introduced, which make the inertia weight not only changes with the number of iterations, but also the inertial weight of each particle is related to its distance from the global optimum. In order to comprehensively consider fuel consumption and dynamic performance, an improved design of the fitness function introduced the fuel consumption factor is proposed. The effectiveness of the improved PSO algorithm is verified by simulation analysis.
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