Virtual Time-response based Iterative Gain Evaluation and Redesign

Kosaka, Mato; Kosaka, Ayato; Kosaka, Manabu

doi:10.1016/j.ifacol.2020.12.2249

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…Several learning-based data-driven control approaches have also been proposed [7], [8], [9], [10], [11], but these approaches are computationally intensive and often require large amounts of experimental data. Data-driven simulation has been proposed as an alternative to data-driven control approaches [18], [19], [20], [21], [22]. In data-driven simulation, the plant response is estimated when the input and output data of the plant and the desired control inputs are given.…”

Section: Introductionmentioning

confidence: 99%

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Convolution-Based Data-Driven Simulation and Controller Design Method

Kameya,

Fujimoto,

Hosoyamada

et al. 2024

IEEE Trans. Ind. Electron.

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Some of the challenges in data-driven control are the selection of the nominal model of the closedloop system that gives the fastest response achievable and the validation of the tuning results. The virtual timeresponse-based iterative gain evaluation and redesign (V-Tiger) method is an approach to solve the problems in data-driven control, but it cannot account for the nonlinearity of the controller. The article proposes a data-driven simulation and controller design approach named the convolution-based data-driven simulation (CDDS) method. Based on time-domain convolution operations, the method enables closed-loop simulations without building a plant model. The method offers various approaches for controller design, such as directly specifying the overshoot and settling time and achieving the desired characteristics. Unlike the conventional methods, the CDDS method can explicitly handle the nonlinearity of the controller and is expected to be applicable to a wide range of control systems. The results of experiments conducted using a buck converter indicate that the CDDS method can reduce the estimation error by up to 95.0% compared with the conventional V-Tiger method. Furthermore, it can reduce the tuning error by more than 52.0% compared with the virtual reference feedback tuning and noniterative correlation-based tuning methods.

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Section: Introductionmentioning

confidence: 99%

“…Therefore, the output data from the plant are required to perform this check. Similarly, several of the proposed approaches that are not based on the state-space model assume a linear system, and hence, they cannot take into account the system nonlinearities [20], [21], [22].…”

Section: Introductionmentioning

confidence: 99%

Convolution-Based Data-Driven Simulation and Controller Design Method

Kameya,

Fujimoto,

Hosoyamada

et al. 2024

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

show abstract

“…However, the controller structures treated in these methods [1], [2], [34] are one-degree-of-freedom (1DOF) control systems (feedback control systems), and these methods have yet to be extended to 2DOF control systems. Virtual Time-response-based Iterative Gain Evaluation and Redesign (V-Tiger) [37] is similar to the references [1], [2], [34] in that the closed-loop systems are identified using input/output data, except the closed-loop identification is performed in the frequency domain. V-Tiger has been extended to 2DOF control systems [38], but both discretetime Fourier transform and inverse discrete-time Fourier transform must be iteratively performed in nonlinear optimization calculations.…”

Section: Introductionmentioning

confidence: 99%

Two-Degree-of-Freedom Controller Design Based on a Data-Driven Estimation Approach

2022

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Data-driven control design is a method to create and tune controllers directly from the initial experimental data without a mathematical model to be controlled. Tracking and disturbance suppression are necessary to control real systems. A two-degree-of-freedom (2DOF) control system is effective to simultaneously enhance the performances of both. This study proposes a direct data-driven tuning method for the controller parameters of a 2DOF control system using only one-shot initial experimental data without mathematical modeling of the controlled object. The proposed approach improves the tracking and disturbance suppression performances by utilizing an estimation method in which the sensitivity function and the closed-loop transfer function are identified after updating the parameters in the time domain. Specifically, the closed-loop response and control input are estimated after updating the parameters, realizing efficient control system design because the control performance can be evaluated prior to implementation. To validate the effectiveness of the proposed method, a simulation for a mechanical system and an experiment for motor control are performed. The proposed method can estimate the response and control input of a 2DOF control system after updating the parameters by offline computations. The optimal control parameters can be obtained to enhance the tracking and disturbance suppression performances.

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Direct Data-Driven Design for a Sparse Feedback Controller Based on VRFT and LASSO regression

Yahagi

Suzuki

2022

IFAC-PapersOnLine

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Virtual Time-response based Iterative Gain Evaluation and Redesign

Cited by 8 publications

References 7 publications

Convolution-Based Data-Driven Simulation and Controller Design Method

Convolution-Based Data-Driven Simulation and Controller Design Method

Two-Degree-of-Freedom Controller Design Based on a Data-Driven Estimation Approach

Direct Data-Driven Design for a Sparse Feedback Controller Based on VRFT and LASSO regression

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