【摘要】 针对工业过程中普遍存在的非线性被控对象,提出一种基于最小二乘支持向量机建模的预测控制算法.首先,用具有RBF核函数的LS-SVM离线建立被控对象的非线性模型;然后,在系统运行过程中,将离线模型在每一个采样周期关于当前采样点进行线性化,并用广义预测算法实现对被控系统的预测控制.仿真结果表明了该算法的有效性和优越性.更多还原

【Abstract】 A predictive control algorithm based on least squares support vector machines (LS-SVM) model for a (family) of complex systems with strong nonlinearity is presented. The nonlinear offline model of the controlled plant is built by LS-SVM with the radial basis function (RBF) kernel. In the process of system operation, the offline model is linearized at each sampling instant, and the generalized predictive control (GPC) algorithm is employed to implement the predictive control of the controlled plant. The simulation results show the effectiveness of the (presented) algorithm.更多还原