Abstract:

A constrained model predictive control algorithm is proposed based on linearization and neural network approach. The nonlinear system must be continuously differentiable when using the Taylor series expansion linearization method. In order to break through this restriction, we introduce Stirling’s interpolation formula method. In the interest of improving the precision of the model, we estimate the high-order terms associated with the linearization using a radial basis function (RBF) neural network. For the sake of reducing the complexity of computation, we reformulate the control performance index to a quadratic optimization problem, and obtain the optimization control sequences by solving the quadratic optimization problem. The constraint conditions are considered during the control process to simulate actual industrial production processes. The simulation results demonstrate the effectiveness of the proposed model predictive control scheme.