A novel parameter estimation method based on PSOSQP optimization

The particle swam optimization (PSO) technique is an effective global convergence method, but its local search speed is slow. The sequential quadratic programming (SQP) method can solve a nonlinear programming problem quickly, but may be trapped into a local minimum. In this paper, a novel optimization method PSO-SQP by combining the PSO and SQP is proposed. In this method, PSO is employed for a global search. SQP is employed for a local minimum search in each PSO main loop to get the best group fitness particle. Then the PSO-SQP method is used in closed-loop parameter estimation. Several examples are simulated to illustrate effectiveness of the PSO-SQP method used in the parameter estimation. The results of simulations have demonstrated the effectiveness of the algorithms.