Model predictive control for systems with fast dynamics using inverse neural models |
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Affiliation: | 1. Department of Electronic Engineering, Technological Educational Institute of Athens, Agiou Spiridonos, Aigaleo 12243, Greece;2. School of Chemical Engineering, National Technical University of Athens, Iroon Polytechneiou 9, Zografos 15780, Greece |
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Abstract: | In this work, a novel model predictive control (MPC) scheme is introduced, by integrating direct and indirect neural control methodologies. The proposed approach makes use of a robust inverse radial basis function (RBF) model taking into account the applicability domain criterion, in order to provide a suitable initial starting point for the optimizer, thus helping to solve the optimization problem faster. The performance of the proposed controller is evaluated on the control of a highly nonlinear system with fast dynamics and compared with different control schemes. Results show that the proposed approach outperforms the rivaling schemes in terms of response; moreover, it solves the optimization problem in less than one sampling period, thus effectively rendering MPC-based controllers capable of handling systems with fast dynamics. |
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Keywords: | Applicability domain Inverse models Inverted pendulum Model predictive control Neural networks Radial basis function |
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