The PID and 2DOF control of the integral system - influence of the 2DOF parameters and practical implementation

Guráš, Radek; Strambersky, Radek; Mahdal, Miroslav

doi:10.1177/00202940221076961

Cited by 9 publications

(6 citation statements)

References 29 publications

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“…The PID control law is formulated as a weighted sum of the system’s classical state error, error-integral, and error-derivative. 24 The proportional term rejects the instantaneous error, the derivative term speeds up the transient response and predicts the system’s future state variations, and the error-integral term optimizes the system’s reference-tracking behavior and damps the steady-state fluctuations. The conventional integer-order PID control law formulation for BG regulation is expressed below.…”

Section: Glucose-insulin Regulation Mechanismmentioning

confidence: 99%

“…The aforementioned controllers were applied to the same set of three patient models, as described in Table 1, to carry out the simulations A and B in Delavari et al 10 The reader is referred to Delavari et al 10 for visualizing the simulation results of the aforementioned controllers. In this article, the performance comparison is done on the basis of the IAE metric expressed in Guras et al 24 . The summary of performance comparison is quantified in Table 4.…”

Section: Simulations and Analysismentioning

confidence: 99%

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Complex-order PID controller design for enhanced blood-glucose regulation in Type-I diabetes patients

Saleem

Iqbal

2023

Measurement and Control

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Type-I Diabetes (TID) is a chronic autoimmune disease that elevates the glucose levels in the patient’s bloodstream. This paper formulates a fractional complex-order Proportional-Integral-Derivative (PID) control strategy for robust Blood Glucose (BG) regulation in TID patients. The glucose-insulin dynamics in blood plasma are modeled via the Bergman-Minimal-Model. The proposed control procedure employs the ubiquitous fractional order PID controller as the baseline BG regulator. The design flexibility of the baseline regulator to effectively normalize the BG levels is enhanced by assigning complex orders to the integral and differential operators instead. The resulting Complex Order PID (CO-PID) regulator strengthens the controller’s robustness against abrupt variations in the patient’s BG levels caused by meal disturbances or sensor noise. The controller parameters are numerically optimized offline. The aforesaid propositions are justified by performing credible simulations in which the proposed controller is tasked to effectively track a set point value of 80 mg/dL from an initial state of hyperglycemia under various disturbance factors. As compared to the FO-PID controller, the CO-PID controller improves the reference tracking-error, transient recovery-time, and control expenditure by 13.1%, 33.4%, and 28.1%, respectively. The simulation results validate the superior reference-tracking accuracy of the proposed CO-PID controller for BG regulation.

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Section: Glucose-insulin Regulation Mechanismmentioning

confidence: 99%

Section: Simulations and Analysismentioning

confidence: 99%

Complex-order PID controller design for enhanced blood-glucose regulation in Type-I diabetes patients

Saleem

Iqbal

2023

Measurement and Control

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“…At present, commonly used control algorithms include the PI control, Sliding mode control (SMC) and Automatic disturbance rejection control (ADRC). The PI 7 control is simple, flexible and easy to adjust. However, when the controlled object becomes complex, the PI control cannot consider both good flexibility and toughness, and the ideal control result is tough to acquire.…”

Section: Introductionmentioning

confidence: 99%

Sensorless control of a PMSM based on an RBF neural network-optimized ADRC and SGHCKF-STF algorithm

Li,

Zhang,

Shi

et al. 2023

Measurement and Control

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For the problem of the rotor position estimation and control accuracy of permanent magnet synchronous motor (PMSM), this paper proposes a PMSM sensorless based on radial basis function (RBF) neural network optimized Automatic disturbance rejection control (RBF-ADRC) and strong tracking filter (STF) improved square root generalized fifth-order cubature Kalman filter (SGHCKF-STF). The Automatic disturbance rejection control (ADRC) has strong robustness, but there are many parameters and difficult to adjust. Now we use RBF neural network to adjust the parameters in ADRC online so as to improve the robustness and anti-disturbance ability. In order to improve the estimation accuracy of rotor position and speed, the orthogonal triangle (QR) decomposition and STF are introduced on the basis of the generalized fifth-order cubature Kalman filter (GHCKF) to design the SGHCKF-STF algorithm that not only ensure the non-positive nature of the covariance matrix but also improve the ability to cope with sudden changes in state during the filtering process. Experimental results show that the combination of RBF-ADRC and SGHCKF-STF improve the sensorless control effect of the PMSM to some extent.

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“…During the production process, the uncertain disturbance and random noise may reduce the control performance of the controller [7]. The output measurement noise and input disturbance need to be filtered and suppressed [8]. Therefore, the PID control strategy based on two degrees of freedom and filter technology will be used to achieve precise control of the engraving machine system in this paper.…”

Section: Introductionmentioning

confidence: 99%

Two-Degrees-of-Freedom PID Control with Kalman Filter for Engraving Machine System

Dong,

Hao,

Shao

et al. 2023

Actuators

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For an engraving machine system with input dynamic disturbance and output random measurement noise, a two-degrees-of-freedom proportional integral derivative (2-DOF PID) control method based on the Kalman filter is firstly proposed in this paper, which can effectively reject the input disturbance and ensure the set point tracking performance of the controller. The 2-DOF controller consists of a disturbance rejection controller and a set point tracking controller. The disturbance rejection controller is composed of a PID controller based on a disturbance observer and expectation model. The parameters of the set point tracking controller are tuned using a differential evolution algorithm (DE), and the cumulative absolute error value (IAE) is used as the fitness function of the DE algorithm, which can improve the rationality of intelligent parameter tuning. In addition, the Kalman filter is also applied to deal with the output noise to suppress the influence of the output measurement uncertainty. Finally, compared with existing algorithms, the feasibility and superiority of the proposed algorithm are verified using numerical simulation and an experimental test.

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The PID and 2DOF control of the integral system - influence of the 2DOF parameters and practical implementation

Cited by 9 publications

References 29 publications

Complex-order PID controller design for enhanced blood-glucose regulation in Type-I diabetes patients

Complex-order PID controller design for enhanced blood-glucose regulation in Type-I diabetes patients

Sensorless control of a PMSM based on an RBF neural network-optimized ADRC and SGHCKF-STF algorithm

Two-Degrees-of-Freedom PID Control with Kalman Filter for Engraving Machine System

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