PD-Type Iterative Learning Control with Adaptive Learning Gains for High-Performance Load Torque Tracking of Electric Dynamic Load Simulator

Dai, Ming‐Zhe; Qi, Rong; Zhao, Yuhong; Li, Yang

doi:10.3390/electronics10070811

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…Therefore, one should determine the value of α according to the amplitude of the random measurement noise, i.e. an error with a small amplitude needs a large α to suppress [ 60 ].…”

Section: Adaptive Ilc-based Feedforward Controllermentioning

confidence: 99%

A novel adaptive PD-type iterative learning control of the PMSM servo system with the friction uncertainty in low speeds

Riaz

Tutsoy

et al. 2023

PLoS ONE

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High precision demands in a large number of emerging robotic applications strengthened the role of the modern control laws in the position control of the Permanent Magnet Synchronous Motor (PMSM) servo system. This paper proposes a learning-based adaptive control approach to improve the PMSM position tracking in the presence of the friction uncertainty. In contrast to most of the reported works considering the servos operating at high speeds, this paper focuses on low speeds in which the friction stemmed deteriorations become more obvious. In this paper firstly, a servo model involving the Stribeck friction dynamics is formulated, and the unknown friction parameters are identified by a genetic algorithm from the offline data. Then, a feedforward controller is designed to inject the friction information into the loop and eliminate it before causing performance degradations. Since the friction is a kind of disturbance and leads to uncertainties having time-varying characters, an Adaptive Proportional Derivative (APD) type Iterative Learning Controller (ILC) named as the APD-ILC is designed to mitigate the friction effects. Finally, the proposed control approach is simulated in MATLAB/Simulink environment and it is compared with the conventional Proportional Integral Derivative (PID) controller, Proportional ILC (P-ILC), and Proportional Derivative ILC (PD-ILC) algorithms. The results confirm that the proposed APD-ILC significantly lessens the effects of the friction and thus noticeably improves the control performance in the low speeds of the PMSM.

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“…Therefore, one should determine the value of α according to the amplitude of the random measurement noise, i.e. an error with a small amplitude needs a large α to suppress [ 60 ].…”

Section: Adaptive Ilc-based Feedforward Controllermentioning

confidence: 99%

A novel adaptive PD-type iterative learning control of the PMSM servo system with the friction uncertainty in low speeds

Riaz

Tutsoy

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…For example, Shi and Huang 30 applied the iterative learning control method into the speed control of ultrasonic motor and achieved good control performance. Dai et al 31 used an adaptive iterative learning control method to improve the load torque tracking performance of power system. Zhang et al 32 proposed a method of combining adaptive predictive control with iterative learning control, and applied this method into ramp control and achieved good control results.…”

Section: Introductionmentioning

confidence: 99%

Robust iterative learning control for random switched singular systems in time domain

Cao

Qiao

2023

Measurement and Control

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A robust iterative learning control algorithm is proposed for continuous-time switched singular systems disturbed by random measurement noise. Firstly, the switched singular systems is transformed into a differential-algebraic equation composed of slow subsystem and fast subsystem by nonsingular transformation. Then, the control input is continuously modified by the state measurement errors and the learning gain with attenuation characteristics. The convergence of each subsystem is strictly proved in theory, and the sufficient conditions for the convergence of this algorithm are given. Theoretical analysis shows that the algorithm can effectively suppress the measurement noise and make the system state completely track the desired trajectory in a finite time interval. Finally, compared with the fixed gain iterative learning control method, the simulation results show that the tracking accuracy of the proposed algorithm is significantly improved.

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“…As the core of Active Disturbance Rejection Control [8], LESO can estimate and compensate for internal and external disturbances, improving the robustness of the system, playing a similar role to DOB. In reference [9], the influence of periodic interference and random measurement noise on the tracking accuracy of the system is suppressed by adaptive learning gain. In order to reduce the noise and other disturbances in the feedback channel, a control strategy combining iterative learning with FIR filter is proposed in reference [10], which improves the anti-interference ability of the system.…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning Control for AGV Drive Motor Based on Linear Extended State Observer

2021

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In order to solve the problems of repetitive and non-repetitive interference in the workflow of Automated Guided Vehicle (AGV), Iterative Learning Control (ILC) combined with linear extended state observer (LESO) is utilized to improve the control accuracy of AGV drive motor. Considering the working conditions of AGV, the load characteristics of the drive motor are analyzed with which the mathematical model of motor system is established. Then the third-order extended state space equations of the system approximate model is obtained, in which LESO is designed to estimate the system states and the total disturbance. For the repeatability of AGV workflow, ILC is designed to improve the control accuracy. As the goods mass transported each time is not same, the LESO is utilized to estimate the non-repetitive load disturbance in real time and compensate the disturbance of the system to improve the position precision. The convergence of the combined algorithm is also verified. Simulation and experimental results show that the proposed iterative learning control strategy based on LESO can reduce the positioning error in AGV workflow and improve the system performance.

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PD-Type Iterative Learning Control with Adaptive Learning Gains for High-Performance Load Torque Tracking of Electric Dynamic Load Simulator

Cited by 6 publications

References 29 publications

A novel adaptive PD-type iterative learning control of the PMSM servo system with the friction uncertainty in low speeds

A novel adaptive PD-type iterative learning control of the PMSM servo system with the friction uncertainty in low speeds

Robust iterative learning control for random switched singular systems in time domain

Iterative Learning Control for AGV Drive Motor Based on Linear Extended State Observer

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