Nonlinear model predictive control with relevance vector regression and particle swarm optimization

In this paper, a nonlinear model predictive control strategy which utilizes a probabilistic sparse kernel learning technique called relevance vector regression (RVR) and particle swarm optimization with controllable random exploration velocity (PSO-CREV) is applied to a catalytic continuous stirred tank reactor (CSTR) process. An accurate reliable nonlinear 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. Additional stochastic behavior in PSO-CREV is omitted for faster convergence of nonlinear optimization. An improved system performance is guaranteed by an accurate sparse predictive model and an efficient and fast optimization algorithm. To compare the performance, model predictive control (MPC) using a deterministic sparse kernel learning technique called Least squares support vector machines (LS-SVM) regression is done on a CSTR. Relevance vector regression shows improved tracking performance with very less computation time which is much essential for real time control.