An Approach for Setting Parameters for Two-Degree-of-Freedom PID Controllers

Wang, Xinxin; Yan, Xiaoqiang; Li, Donghai; Sun, Li

doi:10.3390/a11040048

Cited by 19 publications

(16 citation statements)

References 16 publications

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“…Where: e(t) is the error due a unit step change in the set-point command signal, u(t) is the control input from the controller, u(∞) is the optimal steadystate control input corresponding to the desired output. Also, an instinctive requirement for modern PID tuning methods is to ensure specified closed loop performance in terms of the maximum overshoot and settling time [2,3,18,28]. Therefore, the controller tuning performance is also assessed using the settling time and percentage maximum overshoot indices of the closed-loop with respect to a unit-step input.…”

Section: Algorithm 1 Sad Tuning Algorithmmentioning

confidence: 99%

“…A generalized form of the proportional integral derivative PID structure, often referred to as the two degree-of-freedom (2DOF)-PID [1][2][3][4][5][6][7] fully captures the PID controller in a statefeedback plus integral form. In this paper, we introduce a 2DOF-PID controller design method, called "optimal closed PID-loop model predictive control" (OCPID-LMPC), for the intelligent speed control of a dc motor.…”

Section: Introductionmentioning

confidence: 99%

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Speed Control of DC Motors: Optimal Closed PID-Loop Model Predictive Control

Somefun¹,

Akingbade²,

Dahunsi³

2020

ujca

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One of the lowest level control tasks, especially in robotics and other manufacturing industries, upon which other high-level controls are dependent, is the speed control of a dc motor. Usually, tuning the parameters of the proportional integral derivative (PID) controller for this task employs established conventional methods that expose the knowledge of nominal process model parameters to the control algorithm. These methods have found widespread use. Notwithstanding, a promising line of inquiry is: to search alternate possibilities of a PID being designed to automatically achieve comparable good control performance without using a formal mathematical process model approximation of the actual physical system, such as dc motor plant, in the frequency or time domain. In this paper, we propose an intelligent PID design method, "optimal closed PID-loop model predictive control", that answers this question using the characteristic settling-time (including delay-time) property of dynamic processes. The performance of this proposed method is benchmarked with popular process-model based methods. Simulation results illustrate the promise and effectiveness of the proposed tuning method, in ensuring good closed-loop performance quality for the dc motor, without using formal process models.

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Section: Algorithm 1 Sad Tuning Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Speed Control of DC Motors: Optimal Closed PID-Loop Model Predictive Control

Somefun¹,

Akingbade²,

Dahunsi³

2020

ujca

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show abstract

“…However, for many complex processes, especially with overshoot, time delay, non-minimum phase, and/or non-linear characteristics, the PID algorithm cannot achieve good and very good control performances. Also, there is not a simple unified procedure for tuning controller parameters [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Practical Unified Algorithm of P-IMC Type

Cirtoaje

2020

Processes

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The paper presents a practical algorithm of the proportional-internal model control (P-IMC) type that can be applied to control a wide class of processes: Stable proportional processes, integral processes and some unstable processes. The P-IMC algorithm is a suitable combination between the P0-IMC algorithm and the P1-IMC algorithm, which are characterized by a too weak and a too strong impact of the tuning gain on the control action, respectively. The overall controller has five parameters: A tuning parameter K, three model parameters (steady-state gain, settling time, and time delay) and a process feedback gain used only for integral or unstable processes, to turn them into a compensated process (stable and of proportional type). For a step setpoint, the initial value of the compensated process input is approximately K times its final value. Furthermore, for K = 1 , the compensated process input is close to a step shape (step control principle). These properties enable a human operator to check and adjust online the model parameters. Due to its control performance, robustness to modeling error, and capability to be easily tuned and applied for all industrial processes, the P-IMC algorithm could be a viable alternative to the known PID algorithm. Numerical simulations are given to highlight the performance and the flexibility of the algorithm.

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“…Simulation results illustrate that the proposed method could balance the tracking and disturbance rejection requirements well. A new tuning method combining the desired dynamics equation (DDE) and the generalized frequency method for a two-degree-of-freedom PID controller is developed in Wang et al (2018). The explicit expressions of the delay margin and its achievable upper bounds of PID controller for low-order delay systems are discussed in Ma and Chen (2018).…”

Section: Introductionmentioning

confidence: 99%

The proportional-integral controller design based on a Smith-like predictor for a class of high order systems

Yuan

et al. 2020

Transactions of the Institute of Measurement and Control

Self Cite

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Heat transfer and fluid flow play important roles in industrial processes, especially in chemical and thermal processes. Their dynamics are often modelled as high order systems with the type of [Formula: see text], which have slow responses to the set point and the input disturbance. To enhance the tracking and disturbance rejection performance of these systems, a control structure combining the proportional-integral (PI) controller and Smith-like predictor is proposed in this paper. The tracking and disturbance rejection performance of the proposed control structure with the order mismatch are analyzed. In addition, the precondition where the proposed control structure can obtain satisfactory disturbance rejection performance is deduced. By analyzing the influence of PI parameters on control performance, an empirical tuning rule, which can balance the control performance and robustness constraint well, is summarized and the corresponding tuning toolbox is developed. Finally, the superiority of the proposed control structure is verified by simulations and comparative experiments based on Peltier temperature control platform.

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An Approach for Setting Parameters for Two-Degree-of-Freedom PID Controllers

Cited by 19 publications

References 16 publications

Speed Control of DC Motors: Optimal Closed PID-Loop Model Predictive Control

Speed Control of DC Motors: Optimal Closed PID-Loop Model Predictive Control

A Practical Unified Algorithm of P-IMC Type

The proportional-integral controller design based on a Smith-like predictor for a class of high order systems

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