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10 August 2023 Nonlinear model predictive control based on improved growing and merging neural network
Dong Deng, Qibing Jin, Yang Zhang
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Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 127590L (2023) https://doi.org/10.1117/12.2686464
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China
Abstract
In this paper, a novel self-organizing radial basis function neural network (RBFNN)-based nonlinear model predictive control (RBF-NMPC) scheme is proposed. First, the RBFNN is initialized on the training data using clustering and extreme learning machine (ELM) algorithms, and it serves as a dynamic predictor of an unknown plant. In addition, an adaptive growing and merging strategy is utilized in the neural network so that the RBFNN can automatically adjust its structure. Second, an improved Levenberg-Marquardt (LM) algorithm with a fixed time window is used to increase convergence speed while tuning network parameters. Then, the optimal control signal is calculated by gradient method. Finally, the validity of the developed method is demonstrated by a simulation of continuous stirred tank reactor system.
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Dong Deng, Qibing Jin, and Yang Zhang "Nonlinear model predictive control based on improved growing and merging neural network", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 127590L (10 August 2023); https://doi.org/10.1117/12.2686464
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KEYWORDS
Nonlinear control
Neural networks
Artificial neural networks
Control systems
Modeling
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