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Structurally Optimized Neural Fuzzy Modeling for Model Predictive Control
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DOI：10.1109/TII.2021.3133893.png)
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摘要
En 中文
This article investigates the local linear model tree (LOLIMOT), a typical neural fuzzy model, in the multiple-input-multiple-output model predictive control (MPC). In the conventional LOLIMOT, the structural parameters including centers and variances of its Gaussian kernels are set based on equally dividing the input data space. In this article, after the structural parameters are initially obtained from the input space partition, they are optimized by the gradient descent search, from which the space partitions are further adjusted. This makes it better for the model structure to fit the input data statistics, leading to improved modeling performance with a small model size. The MPC based on the proposed structurally optimized LOLIMOT is then implemented and verified with both numerical and diesel engine plants. Validation results show that the proposed MPC has significantly a better controlling performance than the MPC based on the conventional LOLIMOT, making it an attractive solution in practice.
Keyword:
Neurons
Kernel
Data models
Predictive models
Numerical models
MIMO communication
Training data
Local linear model tree (LOLIMOT)
model predictive control (MPC)
multiple-input-multiple-output (MIMO) nonlinear system
neural fuzzy network
期刊
IF:
9.9
论文数:
8.5K
被引数:
6.0W
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