Abstract: According to the shortcomings of optimization methods of control system parameters, and the result of PSO algorithm usually falling into the partial optimum, Chaos Search and Quantum Space Search were added to the PSO algorithm, constituting the Chaos Quantum Particle Swarm Optimization algorithm, which was applied to the typical thermal control system parameters optimization. Introducing the selection of object functions of control system parameter optimization, describing the CQPSO algorithm process, the CQPSO algorithm was tested and analyzed through multiple test functions. The result shows that, compared with PSO and CPSO algorithm, CQPSO algorithm makes the particle swarm get out of the partial optimization quickly, and improves the accuracy and speed of search. Eventually, the CQPSO algorithm is applied to the PID controller parameters optimization of main steam temperature control system, which offers a credible reference for tuning control parameters and is of great significance.

Key words: control system, parameter optimization, particle swarm optimization, main steam temperature