A quantum-behaved Particle Swarm Optimization approach to PID control parameters tuning

Particle Swarm Optimization (PSO) algorithm is an bio-inspired computing algorithm which can solve many complicated problems. However, it has some defects such as it can easily fall into a local optimal situation and it has too many parameters. To overcome these defects, a Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is put forward which combine standard particle swarm optimization algorithm and quantum mechanics. This new algorithm can avoid the defects which standard particle swarm optimization algorithm has. By using these two algorithms to find the best solution of a function and tuning Proportional-Integral-Derivative (PID) control parameters and comparing the results, which can demonstrate the feasibility and effectiveness of our proposed approach.