Design of a Data-Driven PID Controller using Operating Data

Wakitani, Shin; Nishida, Kouji; Nakamoto, Masayoshi; Yamamoto, Toru

doi:10.3182/20130703-3-fr-4038.00077

Cited by 34 publications

(15 citation statements)

References 8 publications

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“…Note that in this study, it was assumed that the PID gains were optimized in advance by using the DD-FRIT method, 16,17 which is an offline PID gain updating method. Further explanation about the offline updating method is given in Section 2.4.…”

Section: 𝝓(T Imentioning

confidence: 99%

“…To solve this problem, Wakitani et al proposed an offline updating algorithm for PID gains in a database by using data that constructs an initial database (see Figure 1). This method is called database‐driven fictitious reference iterative tuning (DD‐FRIT) 16,17 . Hence, the DD‐PID controller with an offline updated database has become a practical approach to DD‐PID controller implementation.…”

Section: Introductionmentioning

confidence: 99%

“…This method is called database-driven fictitious reference iterative tuning (DD-FRIT). 16,17 Hence, the DD-PID controller with an offline updated database has become a practical approach to DD-PID controller implementation. However, the DD-PID controller fundamentally requires a database that consumes a large amount of memory, and it also requires a controller with a high-performance CPU to calculate PID gains within each sampling time.…”

Section: Introductionmentioning

confidence: 99%

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Design and application of a sparse GMDH‐PID controller

Wakitani

Yamamoto

2022

Adaptive Control & Signal

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The database-driven PID (DD-PID) controller is an effective nonlinear PID controller design method; however, some electrical control units in industrial systems cannot implement its algorithm because the DD-PID control scheme requires a large memory capacity and a high-performance processor. In this article, to solve this problem, a group method of data handling PID (GMDH-PID) controller is proposed that approximately expresses the behavior of the DD-PID controller by combinations of simple nonlinear functions. Although this method can reduce the amount of memory capacity and computing power required, the coefficients in each nonlinear function calculated by the least squares method (LSM) may not be stable because of the multicollinearity of the input signals. In this study, instead of LSM, the least absolute shrinkage and selection operator, which is a sparse modeling method, was employed as a coefficient calculation method of the GMDH, in which some coefficients can be calculated as zero in order to avoid the influence of multicollinearity. The effectiveness of the proposed method was evaluated by simulation and experimental testing. K E Y W O R D Sdatabase-driven control, group method of data handling (GMDH), just-in-time modeling, least absolute shrinkage and selection operator (LASSO), PID control, sparse modeling 1

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Section: 𝝓(T Imentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design and application of a sparse GMDH‐PID controller

Wakitani

Yamamoto

2022

Adaptive Control & Signal

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“…Since many complex nonlinear systems exist in industrial systems, a data-driven control system that realizes nonlinear control is required. There are applications to nonlinear systems, such as DD-PID [23] and DD-FRIT [24,25], FRIT (using feedback linearization) [26], and VRFT (for LPV systems) [27][28][29]. The DD-PID and DD-FRIT have relatively high storage capacity and computational cost when a controller is implemented.…”

Section: Introductionmentioning

confidence: 99%

Direct tuning method of gain‐scheduled controllers with the sparse polynomials function

Yahagi

Kajiwara

2021

Asian Journal of Control

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In industry, gain‐scheduled proportional‐integral‐derivative (PID) control is performed for nonlinear systems using a look‐up table (LUT) that is easy to understand. Compared with the fixed PID, there are many more parameters of the scheduler, and it takes a lot of time to tune them. Also, the ROM storage area increases. To address these problems, in this paper, we propose a gain‐scheduled control law using the sparse polynomial functions and a direct parameter tuning method without system identification. The polynomial functions are used instead of LUT to reduce the ROM area. For direct tuning, data‐driven control is formulated so that it can be applied to the gain‐scheduled control, and the optimal parameters are obtained by the LASSO regression, with which the small contributing parameters of the scheduler become zero, and a sparse controller is obtained. The effectiveness of this method was examined by simulation for two types of nonlinear systems. As a result, it was revealed that a sparse controller with a low calculation cost and a reduced ROM area can be directly obtained without knowing the characteristics of the controlled object for a large number of control parameters of the gain scheduler.

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“…Furthermore, the control parameters are manipulated in a local bounded neighbor data which is extracted from the database by a certain rule. Successful implementations of the data-driven approach have been presented in the technical literatures [10,11,12,13]. However, the stability of the data-driven approach applied in those mentioned literatures has not been investigated.…”

Section: Introductionmentioning

confidence: 99%

Design of a Data-Driven Adaptive Control based on OFSP for Discrete Nonlinear Systems

Guan

Wakitani

Mizumoto

et al. 2018

Stochastic Systems Theory and its Applications (SSS)

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This paper presents the design of a data-driven adaptive control system based on output feedback strictly passive (OFSP) with a feedforward input for nonlinear systems in discrete-time domain. It is well recognized that an adaptive control system based on OFSP conditions can achieve asymptotic stability via a static output feedback. However, these conditions are very restrictive on most realistic systems. Therefore, a parallel feedforward compensator (PFC) is introduced to alleviate such restrictions, but remains a steady state error which is removed by utilizing the feedforward input. Besides, the data-driven approach is one of effective control strategies for nonlinear systems in the process control, and the control parameters can be updated rapidly at each equilibrium point. However, there is no research work that had been done in terms of applying the data-driven approach in a adaptive control system based on OFSP. Therefore, once the robust stability of the adaptive control system based on OFSP can be guaranteed, the adaptive gains can be optimized by the data-driven approach such that the output performance is able to be improved. The proposed scheme is verified through an numerical example, the results of which demonstrate the effectiveness.

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Design of a Data-Driven PID Controller using Operating Data

Cited by 34 publications

References 8 publications

Design and application of a sparse GMDH‐PID controller

Design and application of a sparse GMDH‐PID controller

Direct tuning method of gain‐scheduled controllers with the sparse polynomials function

Design of a Data-Driven Adaptive Control based on OFSP for Discrete Nonlinear Systems

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