An auto-tuning PD controller for DC servo position control system

Simhachalam, Dharmana; Dey, Chanchal; Mudi, Rajani K.

doi:10.1109/icpces.2012.6508121

Cited by 13 publications

(6 citation statements)

References 9 publications

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“…e deliberate application of delays to stabilize systems is a latent topic in the literature, one of the most important contributions in this aspect is found in [290] that drove a whole stream of research in what is known as time-delayed feedback control (TDFC). e technique is to deliberately introduce delays into the control scheme to σ-stabilize a system with or without inherent delays [291][292][293][294][295][296][297][298][299][300][301][302][303][304][305][306][307][308]. is is due to the advantages that they have in applications on experimental platforms, such as noise attenuation, nonimplementation of estimators, observers and speed sensors, avoiding filters, as well as its easy implementation.…”

Section: Numerical Methods (Applications)mentioning

confidence: 99%

A Panoramic Sketch about the Robust Stability of Time-Delay Systems and Its Applications

et al. 2020

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This paper presents a brief review on the current applications and perspectives on the stability of complex dynamical systems, with an emphasis on three main classes of systems such as delay-free systems, time-delay systems, and systems with uncertainties in its parameters, which lead to some criteria with necessary and/or sufficient conditions to determine stability and/or stabilization in the domains of frequency and time. Besides, criteria on robust stability and stability of nonlinear time-delay systems are presented, including some numerical approaches.

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Section: Numerical Methods (Applications)mentioning

confidence: 99%

A Panoramic Sketch about the Robust Stability of Time-Delay Systems and Its Applications

et al. 2020

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“…1 The typical approach for the feedback control is using the PD controller. [2][3][4] There is no permanent control deviation for linear integral systems such as pneumatic, electric, or hydraulic systems unless there are non-linearities like static friction. PID control can lead to removing the permanent control deviation in such systems.…”

Section: Introductionmentioning

confidence: 99%

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

Guráš

Strambersky

Mahdal

2022

Measurement and Control

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The article deals with the issue of using the Two Degree of Freedom (2DOF) PID controller to control an integral system and investigates by the simulation and experimental measurement what influence it has on the course of the control process compared to standard PID controller. The controlled plant is represented by the DC electric motor with worm gear and its output shaft rotation angle. The article studies the effect of the added parameters of the 2DOF controller on the dynamics of the closed-loop control. The influence of these parameters is then evaluated using the quality of feedback control criteria ITAE. The paper studies how the overshoot of the controlled variable during the setpoint step is eliminated using 2DOF control theory. The overshoot is caused due to an aggressive tuning of the controller to eliminate the disturbance effect on the controlled variable of the integral plants with dead zones.

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“…There have been a lot of approaches to tune the parameters for the motor PID controller. For example, parameters were tuned by using an basis adaptive rule and a speed error under various conditions [3]. A parameter modifier was proposed for the PD position controller based on a position error [4].…”

Section: Introductionmentioning

confidence: 99%

Auto-Tuning Controller Using MLPSO With K-Means Clustering and Adaptive Learning Strategy for PMSM Drives

et al. 2022

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This paper proposes a new online auto-tuning method to improve the accuracy and reduce the tuning time of permanent magnet synchronous motor (PMSM) drives. Under varying loads, the ability to tune the controllers of PMSM drives using optimal tuning time is crucial. However, direct tuning of controller parameters using estimated parameters or conventional particle swarm optimization (PSO) methods do not satisfy the performance criteria. To solve this problem, the new method combining mechanical parameter estimation (MPE) and multi-layer particle swarm optimization (MLPSO) with Kmeans clustering (KMC) and an adaptive learning strategy (ALS) is proposed. First, the combination of an MPE method with a lookup table (LUT) for initial parameter selection is introduced to reduce the iteration time. Then, the MLPSO-KMCALS method is proposed as an improvement over the conventional PSO method by increasing the number of layers, grouping the swarm into several subswarms, and using the ALS for each particle to increase the population diversity and optimize the controller parameters within the shortest possible amount of time. Finally, a disturbance load torque observer is applied to compensate for the effect of external disturbances after tuning. The effectiveness of the proposed method is validated through experiments conducted under practical conditions.INDEX TERMS PMSM drives, auto-tuning, parameter estimation, particle swarm optimization (PSO), adaptive multi-layer search.

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An auto-tuning PD controller for DC servo position control system

Cited by 13 publications

References 9 publications

A Panoramic Sketch about the Robust Stability of Time-Delay Systems and Its Applications

A Panoramic Sketch about the Robust Stability of Time-Delay Systems and Its Applications

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

Auto-Tuning Controller Using MLPSO With K-Means Clustering and Adaptive Learning Strategy for PMSM Drives

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