Improved Particle Swarm Optimization And Its Application In Tuning Of Controller Parameters

Particle Swarm Optimization (PSO) is a kind of group intelligent heuristic global evolutional algorithm that was proposed in the recent decade, its basic idea was originated from birds foraging behavior simulation. PSO is applied to solve non-linear and non-differentiable complex optimization problem, also it can be used to solve combinatorial optimization problem too. With the advantages such as its principle is simple, it has fast convergence rate and it is easy to be implemented, PSO has become an important branch in artificial intelligence research areas in recent years. However, PSO has some inherent defects such as following: premature convergence, easily turning into local optimization and poor search result. In response to these defects, taking steps to improve PSO algorithm and expand application fields will have important theoretical value and practical significance.First of all, on the base of systematic analysis on basic theory and general improved principles of PSO algorithm, this paper introduces genetic algorithm selection, crossover and mutation operator in the iterative process of computing and adapts non-linear reduce mechanisms for inertia factor, thus puts forward a type of improved PSO (GAPSO). Four standards test functions are used to test the improved optimization in order to verify the performance, the experimental results show that the improved algorithm effectively overcomes the inherent PSO defect, avoids premature convergence and improves search result accuracy.Then, the improved PSO (GAPSO) is applied in tuning process of PID controller and Auto-Disturbance Rejection Controller (ADRC) parameters. PID controller is a type of control strategy that uses deviation to eliminate the deviation. The theory of PID is simple and easy to be implemented but has high reliability. However, the tuning of parameters is the core issue of PID design and applications. In order to achieve excellent control results, the improved PSO is applied in PID controller parameters tuning process and the experimental results show that this parameters optimization algorithm has fine control effect. ADRC is a new type of advanced nonlinear control strategy, it is independents with the charged object model. ADRC has strong ability to resist interference, but the parameters that need for setting are too more. Nevertheless, to a large extent, the effect of control is related to parameters that settled. In terms of the difficulties and lack of experience in tuning of ADRC parameters, this paper introduces the improved PSO in optimize process, and adopts a fitness function that rejects overshoot. The experimental results show that the parameters that tuned by this algorithm have excellent control quality.