Abstract
For processes with strong nonlinearity and fast response, a Nonlinear Model Predictive Control (NMPC) algorithm is proposed based on Laguerre functions and Radial Basis Function-based AutoRegressive model with eXogenous variable (RBF-ARX-LMPC), which is built to capture the nonlinear dynamics of such process. Firstly, the RBF-ARX model is transformed into an extended Non-Minimal State-Space (NMSS) model in which integral action and set-point information are naturally contained to decrease steady-state error. Then, to reduce computational burden, the control variables of the NMPC is parameterised by Laguerre polynomials for dimensionality reduction. The proposed control strategy is applied to a maglev ball system and is compared with Proportional–Integral–Derivative (PID) controller, the RBF-ARX model-based Linear Quadratic Regulator (RBF-ARX-LQR), and the RBF-ARX model-based Model Predictive Control (RBF-ARX-MPC). The experimental results show that the proposed control strategy improves transient performance and computational efficiency.
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