A neural network approach to nonlinear model predictive control

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
IECON Proceedings (Industrial Electronics Conference), 2011, pp. 2305 - 2310
Issue Date:
2011-12-01
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This paper proposes a neural network approach to nonlinear model predictive control (NMPC). The NMPC problem is formulated as a convex programming problem via Jacobain linearization. The unknown high-order term associated with the linearization is estimated by using a feedforward neural network via supervised learning. The convex optimization problem involved in MPC is solved by using a recurrent neural network. Simulation results are provided to demonstrate the performance of the approach. © 2011 IEEE.
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