Multi-output least squares support vector regression modeling based adaptive nonlinear predictive control and its application

This paper proposes a novel data-driven adaptive nonlinear predictive control method, which can effectively improve the control performance and computing efficiency of the conventional predictive control methods. First, in order to improve the accuracy of least squares support vector regression (LS-SVR) modeling for multi-output nonlinear systems, and considering the coupling relationship among multiple outputs, multi-output LS-SVR (M-LS-SVR) prediction modeling is proposed in this paper by adding the whole sample fitting error term to the optimization objective function. And the particle swarm optimization (PSO) algorithm is used to optimize the model parameters. Then, in view of the model mismatch of dynamic process modeling, and considering that the complexity of the M-LS-SVR model may lead to the slow optimization speed of predictive control with traditional intelligent algorithms, a novel adaptive nonlinear predictive control scheme is proposed, which includes two phases of nonlinear optimization. The first phase is to adopt the gradient descent algorithm to optimize the error between the model outputs and the actual ones in real time, so as to adjust the model parameters. And the second one is to use sequential quadratic programming (SQP) algorithm with global convergence and superlinear convergence speed to design the nonlinear predictive controller, so as to accelerate the speed of predictive control solving. Benchmark simulation and data experiment in a blast furnace ironmaking process show that the proposed method has fast computing speed, and good performances of setpoint tracking, disturbance rejection and robustness. © 2019, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.