A RBF-ARX model-based robust MPC for tracking control without steady state knowledge |
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Affiliation: | 1. Dip. Ingegneria Meccanica, Chimica e dei Materiali, via Marengo, 2 09123 Cagliari, Italy;2. Dept. of Civil and Environmental Engineering, Aalto University, P.O. Box 15200, FI-00076 Aalto, Finland;3. Department of Chemical Engineering, Federal University of Campina Grande, 58429-140 Campina Grande, Paraiba, Brazil |
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Abstract: | A RBF-ARX modeling and robust model predictive control (MPC) approach to achieving output-tracking control of the nonlinear system with unknown steady-state knowledge is proposed. On the basis of the RBF-ARX model with considering the system time delay, a local linearization state-space model is obtained to represent the current behavior of the nonlinear system, and a polytopic uncertain linear parameter varying (LPV) state-space model is built to represent the future system’s nonlinear behavior. Based on the two models, a quasi-min–max MPC algorithm with constraint is designed for output-tracking control of the nonlinear system with unknown steady state knowledge. The optimization problem of the quasi-min–max MPC algorithm is finally converted to the convex linear matrix inequalities (LMIs) optimization problem. Closed-loop stability of the MPC strategy is guaranteed by the use of parameter-dependent Lyapunov function and feasibility of the LMIs. Two examples, i.e. the modeling and control of a continuously stirred tank reactor (CSTR) and a two tank system demonstrate the effectiveness of the RBF-ARX modeling and robust MPC approach. |
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Keywords: | Model predictive control Radial basis function networks Robustness CSTR process Two tank system |
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