Non-Iterative Data-Driven Tuning of Model-Free Control Based on an Ultra-Local Model

Non-Iterative Data-Driven Tuning of Model-Free Control Based on an Ultra-Local Model

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In this paper, we present a data-driven tuning method for model-free control based on an ultra-local model (MFC-ULM), which is also called intelligent proportional-integral-derivative control.In industries, the control design must be easy, and it is important that the control law can be applied to nonlinear systems.The MFC-ULM has most of these features.However, in practice, trial-and-error tuning of MFC-ULM design parameters is necessary.

To address this problem, we adopt a data-driven Collagen tuning approach.In the proposed method, the MFC-ULM design parameters can be tuned from single-experiment data without requiring system identification, and optimal parameters for the MFC-ULM are obtained using the least-squares method.Additionally, we adopt $L_{2}$ -norm BLACK WALNUT HULLS regularization to avoid overlearning.The effectiveness of this method was examined using simulations of two nonlinear systems.

The results revealed that the MFC-ULM design parameters can be obtained directly without knowing the characteristics of the controlled object.