Intelligent PID Temperature Control Using Output Recurrent Fuzzy Broad Learning System for Nonlinear Time-Delay Dynamic Systems

Rospawan, Ali; Tsai, Ching‐Chih; Tai, Feng‐Chun

doi:10.1109/icsse55923.2022.9948234

Cited by 5 publications

(10 citation statements)

References 15 publications

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“…Considering this case, one can draw the conclusion in the form of 2 − 𝜂𝜆 𝑖 > 0 for any eigenvalue [39]. Hence, it follows that…”

Section: B Convergent Analysis Of Mimo Orfbls Identifiermentioning

confidence: 89%

“…Notice that the form of the Lyapunov function between the SISO [39] and MIMO terms is different where, in MIMO, the induced quadratic form of the error vector is used. Thus, the time difference or alteration in the Lyapunov function gives:…”

Section: B Convergent Analysis Of Mimo Orfbls Identifiermentioning

confidence: 99%

“…A yy PP (39) In the end, by defining the Lyapunov function (27) and finding its time difference form is negative definite, i.e., ∆𝐿 𝑚 (𝑘) < 0, this requires that 𝜆 𝑖 (2 − 𝜂𝜆 𝑖 ) > 0 for any 𝑖 if the sufficient condition ( 26) is met. Accordingly, the proof procedure of Theorem 1 is completed.…”

Section: B Convergent Analysis Of Mimo Orfbls Identifiermentioning

confidence: 99%

“…This aforementioned method, abbreviated as RFBLS-APPID in short, has been done by feeding back the fuzzy subsystem output back to the fuzzy rules. On the other hand, the studies in [24]- [27] proposed another structural improvement with output recurrent FBLS which has been done by feeding back the FBLS system output back to the fuzzy rules and back to each node in the enhancement layer. Such a method was shown to give better controller adaptability over the nonlinear system compared with the RFBLS-APPID controller, yet both methods proved applicable and effective in handling nonlinear processes.…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by [24]- [27], the objectives of this paper are to theoretically present a novel MIMO ORFBLS-APPID controller by first constructing a MIMO ORFBLS identifier to online learn the input-output dynamic behavior generated by a class of nonlinear digital dynamic systems, and then automatically tuning PID gain matrices over time by employing the Jacobian transformations of the MIMO ORFBLS identifier, and to validate the proposed MIMO ORFBLS-APPID controller via three comparative simulations and one experimental study on a real MIMO extrusion barrel. By comparing to existing methods and the state of the art, the main contributions of the paper are summarized as follows.…”

Section: Introductionmentioning

confidence: 99%

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Output Recurrent Fuzzy Broad Learning Systems for Adaptive MIMO PID Control: Theory, Simulations, and Application

Rospawan,

Tsai,

Hung

2024

IEEE Access

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This paper proposes a novel adaptive predictive Proportional-Integral-Derivative (PID) controller utilizing an output recurrent fuzzy broad learning systems (ORFBLS) for Multiple-Input Multiple-Output (MIMO) digital control systems, aiming to effectively adapt to changing setpoints and dynamic environments. The proposed controller, MIMO ORFBLS-APPID controller in short, is proposed to extend the application of ORFBLS as an adaptive adjustment mechanism for PID gains parameters, where the controller gain matrices are automatically tuned over time by employing the Jacobian transformations of the MIMO ORFBLS identifier. Three theorems are established to ensure proper usage and successful applications of the proposed controller. The setpoints tracking control performance and disturbance rejection abilities are firmly illustrated by performing three simulations to the multivariable nonlinear dynamic systems. Moreover, one experimental study to the laboratory-built extrusion barrel in a plastic injection molding machine is done to validate the effectiveness and practicality of the proposed control method. Through comparative simulations and experimental results, the proposed controller has been shown to outperform two existing control methods in terms of control performance indexes.INDEX TERMS Adaptive control, auto-tuning, intelligent control, output recurrent fuzzy broad learning systems (ORFBLS), parameter adjustment algorithm, PID controller.

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“…Considering this case, one can draw the conclusion in the form of 2 − 𝜂𝜆 𝑖 > 0 for any eigenvalue [39]. Hence, it follows that…”

Section: B Convergent Analysis Of Mimo Orfbls Identifiermentioning

confidence: 89%

Section: B Convergent Analysis Of Mimo Orfbls Identifiermentioning

confidence: 99%