Robust Control Method Based On Improved Multi-objective Particle Swarm Optimization Algorithm

For the difficulty of solving the multi-objective robust control problems, this paper presented an improved dynamic multi-objective particle swarm algorithm, which is applied to H2/H∞control andμmethod and compared with the existing design method of multi-objective robust control. The research of this paper mainly lies in the following folds:(1) Dynamic multi-objective particle swarm algorithm. The multiobjective particle swarm algorithm was improved by changing its self-adapting inertia and variation factor, and joining the dynamic variation factor. As far as the convergence and dispersion, it shows that the improved dynamic multi-objective particle swarm algorithm has obvious advantages. This paper gives several typical test functions, and the PARETO optimal bounds are obtained using the improved and basic particle swarm algorithm respectively, illustrating advantage of the dynamic multi-objective particle swarm algorithm.(2)H2/H∞control design using multi-objective particle swarm algorithm.In the robust control problem, the controller designed using the linear matrix inequality (LMI) may not be sub-optimal. Thus the paper applies the improved particle swarm in the design of H2/H∞control to get the PARETO optimal solutions. Using the dynamic multi-objective particle swarm algorithm and the LMI method respectively, the PARETO optimal solutions are obtained and analyzed, which shows the validity and advantage of the presented dynamic multi-objective particle swarm algorithm in solving the H2/H∞control problems.(3)μsynthesis using multi-objective particle swarm algorithm:In the robust control, the calculation ofμis NP difficult problem. This paper applied the dynamic multi-objective particle swarm algorithm inμsynthesis. The simulation results are compared with existing D-K method, and shows the validity and advantage ofμsynthesis based on dynamic multi-objective particle swarm algorithm.