Nonlinear Model Predictive Control Using State-space Recurrent Multi-dimensional Taylor Network
Pages 46 - 52
Abstract
In this paper, we propose a discrete-time infinite horizon nonlinear model predictive control (QIH-NMPC) based on state-space recurrent multi-dimensional Taylor network (RMTN). The purpose of this paper is to construct a state-space RMTN to be used as internal predictive model for QIH-NMPC and train this network by backpropagation through time (BPTT) algorithm. The multi-dimensional Taylor network (MTN) differs from the existing neural network (NN) on its structure and dynamic performance. RMTN gains advantages over recurrent neural network (RNN) on its training efficiency and ease of use, thus it reduces the on-line nonlinear optimization burden and enhances the efficiency of computation. The stability of closed loop system is guaranteed via Lyapunov stability theory. Finally, a numeric example is given to illustrate the effectiveness of the proposed design approach.
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Index Terms
- Nonlinear Model Predictive Control Using State-space Recurrent Multi-dimensional Taylor Network
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Published In
October 2018
198 pages
ISBN:9781450365635
DOI:10.1145/3284516
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- SFedU: Southern Federal University
- University of Alberta: University of Alberta
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Association for Computing Machinery
New York, NY, United States
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Published: 12 October 2018
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ICCMA 2018
ICCMA 2018: 2018 The 6th International Conference on Control, Mechatronics and Automation
October 12 - 14, 2018
Tokyo, Japan
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