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Adaptive Control of Nonlinear Time-Varying Processes Using Selective Recursive Kernel Learning Method
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DOI：10.1021/ie100634k.png)
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A selective recursive kernel learning-based (SRKL) adaptive predictive controller is proposed for nonlinear time-varying processes. First, a SRKL identification model is presented with an efficient sparsification strategy which makes a trade-off between the tracking precision and the controller's complexity. The SRKL model can be updated efficiently by introducing and/or deleting a sample via recursive learning algorithms. Consequently, the model can adjust its structure adaptively to capture the process dynamics and time-varying characteristics. On the basis of the SRKL model, a predictive controller with an adaptive modification item is designed. The novel controller can achieve better performance since the SRKL model can trace the process characteristics online. The obtained results on a laboratory-scale liquid-level process and a continuous bioreactor with time-varying parameters show that the proposed controller is superior to the traditional proportional-integral-derivative (PID) controller and related controller with an offline KL model without online updating.
Keyword:
MODEL-PREDICTIVE CONTROL
SUPPORT VECTOR MACHINE
QUALITY ESTIMATION
NEURAL-NETWORKS
SVM
REGRESSION
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期刊
I
IF:
3.9
论文数:
4.0W
被引数:
9.6W
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