Title :
Research on Nonlinear Predictive Control Rolling Optimization Strategy Based on SAPSO
Author_Institution :
Sch. of Math. & Inf. Sci., Weinan Teachers Univ., Weinan, China
Abstract :
The problem in neural network model based nonlinear systems predictive control the predictive control law is difficult to get, this paper proposed using simulated annealing particle swarm optimization algorithm (SAPSO) to optimize the solution. Compared particle swarm optimization (PSO) algorithm with SAPSO algorithm in the performance of simulation, using SAPSO algorithm optimized neural network predictive control, simulation results show that the method can effectively reduce the number of iterations and improve convergence accuracy.
Keywords :
iterative methods; neurocontrollers; nonlinear control systems; particle swarm optimisation; predictive control; simulated annealing; SAPSO algorithm; iteration method; neural network model; nonlinear predictive control rolling optimization strategy; optimized neural network predictive control; predictive control law; simulated annealing particle swarm optimization algorithm; Algorithm design and analysis; Particle swarm optimization; Prediction algorithms; Predictive control; Predictive models; Simulated annealing; BP neural network; Nonlinear; Optimization; SAPSO;
Conference_Titel :
Computational Intelligence and Security (CIS), 2011 Seventh International Conference on
Conference_Location :
Hainan
Print_ISBN :
978-1-4577-2008-6
DOI :
10.1109/CIS.2011.37