Design and implementation of adaptive PID controller for speed control of DC motor

Sahputro, Suwandi Dwi; Fadilah, Fahmi; Wicaksono, Nanda Avianto; Yusivar, Feri

doi:10.1109/qir.2017.8168478

Cited by 30 publications

(11 citation statements)

References 4 publications

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“…The PLL receives, both the motor actual and the reference frequencies [14]. According to the phase error of these two frequencies, the PLL provides a suitable duty cycle for driving the DC motor [15]. The variations in the PLL output due to the PLL input phase difference are displayed in Figure 8.…”

Section: Experimental Results Of the Pllmentioning

confidence: 99%

Relative stability enhancement for brushed DC motor using a PLL interfaced with LabVIEW

Mahmoud

El-Biomey

Ahmed

2020

IJPEDS

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This work presents a fast response and stable computer based a brushed DC motor speed controller. The controller configured of gate drive circuits for H-Bridge accompanied with data acquisition unit DAQ-6211. These gate drive circuits include, phase comparator, current booster and wave forms cleaning circuits. An optical encoder is used for motor speed to frequency conversion. The CD4046 PLL chip compares phases of the encoder output frequency (motor speed) with a reference frequency (desired speed). The obtained phase difference (error) is used to allocate the suitable PWM duty cycles. An H-Bridge BJT switches driven by PWM is interfaced with the motor. The system hardware is provided with a simple and accurate data acquisition unit DAQ-6211 to be interfaced with the LabVIEW software Package. This allows monitoring and storing the different measured data of this platform. The system relative stability is determined and examined based on the Bode plot analysis and design. Then the relative stability criterion (Phase Margin) is measured the closed-loop stability of the system. This system considers the fast feedback response with indication of its stability state as well as the stable wide dynamic range. It compensates the changes in system parameters due to the environmental effects and other disturbances.

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Section: Experimental Results Of the Pllmentioning

confidence: 99%

Relative stability enhancement for brushed DC motor using a PLL interfaced with LabVIEW

Mahmoud

El-Biomey

Ahmed

2020

IJPEDS

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“…Which is rewritten as: The main purpose of this algorithm is to estimate the new parameters vector θ(t k ) at time instant t k by adding a correction vector to the previous parameters estimation vector θ(t k−1 ) at time instant t k−1 [19]. The estimation error in (9) is to be minimized using RLS algorithm, It, recursively and online, estimates θ(t) by applying the following equations [5]:…”

Section: Controller Designmentioning

confidence: 99%

Speed control of DC motor using conventional and adaptive PID controllers

Al-Bargothi

Qaryouti

Jaber

2019

IJEECS

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<p>Proportional Integral Derivative (PID) controllers are <a href="http://www.thesaurus.com/browse/extensively">extensively</a> used in practical industries to control the speed of DC Motors. The single weakness of PID controllers is their sensitivity to variation in parameters and operating conditions; thus, tuning the controller gains to adapt with these variations presents a practical challenge. In this paper, an adaptive mechanism that utilizes a Recursive Least Square (RLS) algorithm, with rate limiters, is implemented to perform an online self-adjusting of each of the PID gains in order to achieve Adaptive PID (APID) controller that will accommodate to system variations. MATLAB/ Simulink software is used to implement and simulate APID control of a Chopper-Fed DC motor. A conventional PID control system is also designed and simulated to obtain results that can be used to judge the performance of the APID controller. Results proved that the APID controller forced the motor speed to track the reference input with <a href="http://www.thesaurus.com/browse/insignificant">insignificant</a> tracking error, and also managed to attain the motor speed at its desired value, regardless of the load changes inflected on the motor. This enhances both transient and steady-state speed responses.</p>

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“…Many studies have opted for optimal solutions based on artificial intelligence techniques facilitated by the development of computer technology, such as genetic algorithms [10], Fuzzy Logic [11,12], Neural Networks [13,14], decoupling control, and others. They are used in several areas such as controlling electrical DC motors [15,16], automatic and robot manipulation systems [17], controlling temperature performance [18], and controlling systems in agriculture [19].…”

Section: Introductionmentioning

confidence: 99%

Optimal Neural Network PID Approach for Building Thermal Management

et al. 2023

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The process of thermal control and regulation in buildings is considered complex. Its complexity lies in the various internal and external physical phenomena impacting its control, and also in the increasingly important requirements of occupant comfort, energy optimization and efficiency, and optimization of measuring and monitoring equipment. Recently, the establishment of technical rules for optimal building thermal control has gained interest in academia and industry. These rules have focused mainly on three aspects: the use of renewable energy, optimal management, and the use of equipment and materials allowing the optimization of energy. However, optimal control has not been addressed enough. In this article, we present a PID controller based on a Neural Network approach for thermal building management and control. The proposed approach is based on two processes: an optimal identification process dedicated to the thermal building behavior prediction impacted by variable and invariable elements, measured and unmeasured factors, and a control process to ensure the desired performance with optimal energy control. The results obtained show the advantages of the adopted system in terms of energy optimization, with an important energy gain of 8% to 11%, along with better regulation and control performance, and in terms of occupant comfort with minimal temperature variations.

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Design and implementation of adaptive PID controller for speed control of DC motor

Cited by 30 publications

References 4 publications

Relative stability enhancement for brushed DC motor using a PLL interfaced with LabVIEW

Relative stability enhancement for brushed DC motor using a PLL interfaced with LabVIEW

Speed control of DC motor using conventional and adaptive PID controllers

Optimal Neural Network PID Approach for Building Thermal Management

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