多智能体粒子群优化的SVR 模型预测控制

参数的优化选择对支持向量回归机的预测精度和泛化能力影响显著,鉴于此,提出一种多智能体粒子群算法(MAPSO)寻优其参数的方法,并建立MAPSO支持向量回归模型,用于非线性系统的模型预测控制,推导出最优控制率.采用该算法对非线性系统进行仿真,并与基于粒子群算法、基于遗传算法优化支持向量回归机的模型预测控制方法和RBF神经网络的预测控制方法进行比较,结果表明,所提出的算法具有更好的控制性能,可以有效应用于非线性系统控制中.

Model predictive control based on SVR optimized by multi-agent particle swarm optimization algorithm

The prediction accuracy and generalization ability of the support vector regression(SVR) model depend on a proper setting of its parameters to a great extent. An optimal selection approach of SVR parameters is proposed based on the multi-agent particle swarm optimization(MAPSO) algorithm. On this basis, a model predictive control method based on the MAPSO-SVR is proposed and applied to the nonlinear predictive control scheme to select the optimal control inputs. For the nonlinear system, the simulation results show that the proposed method is effective and has an excellent adaptive ability and robustness. Compared with the model predictive controllers based on SVR optimized by particle swarm optimization algorithm(PSO-SVR), SVR optimized genetic algorithm(GA-SVR), and RBF neural network algorithm, the proposed method is superior to other methods.