Data-Driven Control Based on the Behavioral Approach: From Theory to Applications in Power Systems

Markovsky, Ivan; Huang, Linbin; Dörfler, Florian

doi:10.1109/mcs.2023.3291638

Cited by 29 publications

(2 citation statements)

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“…In general, experimental data is corrupted by measurement noise. Although the noise may affect the performance of data-driven control, many studies have examined strategies for addressing noise, such as regularization [36], total variation denoising [53], discrete Fourier transform via periodization [54] , and reformulation of the optimization problem using slack variables [57]. In the future, the noise tolerance of the proposed method will be evaluated and an appropriate handling method for noise will be explored.…”

Section: Discussionmentioning

confidence: 99%

“…Data-driven control methods can be classified as indirect and direct methods [28]. Unlike the indirect method, which first explicitly identifies the plant model using the data, the direct method avoids such system identification from the data and directly designs the control policy from the data [36]. Various direct data-driven controller tuning methods for solving the model-reference control problem have been examined, such as iterative feedback tuning (IFT) [37], correlation-based approach [38], virtual reference feedback tuning (VRFT) [39,40] , and fictitious reference iterative feedback (FRIT) [41].…”

Section: Related Workmentioning

confidence: 99%

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Direct tuning of the data-driven controller considering closed-loop stability based on a fictitious reference signal

Yahagi

Kajiwara

2021

Measurement and Control

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The direct tuning of controller parameters, which is based on data-driven control, has been attracting considerable attention because of the ease of its control system design. In practical use, it is important to consider the stability of the closed-loop system and model matching with few design parameters. In this study, we propose a direct tuning method based on a fictitious reference signal that considers the bounded-input bounded-output (BIBO) and model matching without repeating experiments. The proposed method includes two steps. In the first step, the BIBO stability is satisfied. The pole information is lost in the cost function of the conventional method using a fictitious reference signal. Then, we derive a new cost function that can prevent the loss of the pole information. This provides controller parameters that can stabilize the closed-loop system. The model matching between the reference model and the closed-loop system is considered in the second step. When model matching is achieved, the characteristics of the reference model almost match those of the closed-loop system, including the gain and phase margins. The parameters of the reference model are automatically tuned to realize model matching. Using the two-step method, we can obtain parameters considering BIBO stability and the model matching. In addition, there are no design parameters apart from the dealing noise. Two simulations and an experiment were performed on a system with dead time to verify the effectiveness of the proposed two-step method. The results showed that the proposed method provides BIBO stability and model-matched control parameters from the measured data through a one-time experiment without trial and error.

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