Adaptive Neural Network Based Predictive Control of Nonlinear Systems With Slow Dynamics
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Abstract In this paper a data-driven approach for model-free control of nonlinear systems with slow dynamics is proposed. The system behavior is described using a local model respectively a neural network. The network is updated online based on a Kalman filter. By predicting the system behavior two control approaches are discussed. One is obtained by calculating a control input from the one step ahead prediction equation using least squares, the other is obtained by solving a standard linear model predictive control problem. The approaches are tested on a constrained nonlinear MIMO system with slow dynamics.
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2018 ◽
Vol 29
(12)
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pp. 6227-6241
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2015 ◽
Vol 10
(5)
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2017 ◽
Vol 47
(8)
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pp. 2351-2362
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Keyword(s):
2019 ◽
Vol 139
(10)
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pp. 1167-1174