Support vector machine based nonlinear model multi-step-ahead optimizing predictive control
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A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-a-head optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection.
Key wordsnonlinear model predictive control support vector machine nonlinear system identification kernel function nonlinear optimization
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