ComputingComputer ScienceDecision Support SystemsMachine LearningProcess ControlControl SystemsControl TheoryKernel MethodsModel Predictive ControlPredictive ModelingSupport Vector MachinesControl Algorithm DevelopmentLeast Squares Support Vector MachinesNonlinear Support Vector MachinesNonlinearityPredictive ControlPredictive Control SystemsRadial Basis Function KernelsRadial Basis Function Networks
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.