Abstract
This paper demonstrates control accuracy andcomputational efficiency of nonlinear model predictive control (NMPC) strategywhich utilizes a probabilistic sparse kernel learning technique calledRelevance vector regression (RVR) and particle swarm optimization withcontrollable random exploration velocity (PSO-CREV). An accurate reliablenonlinear model is first identified by RVR with a radial basis function (RBF)kernel and then the optimization of control sequence is speeded up by PSO-CREV.An improved system performance is guaranteed by an accurate sparse predictivemodel and an efficient and fast optimization algorithm. To compare theperformance, model predictive control (MPC) using a deterministic sparse kernellearning technique called Least squares support vector machines (LS-SVM)regression is done on a highly nonlinear distillation column with severeinteracting process variables. SVR based MPC shows improved tracking performancewith very less computational effort which is much essential for real timecontrol.
