Online Iterative Learning Compensation Method Based on Model Prediction for Trajectory Tracking Control Systems

Wang, Ze; Zhou, Ran; Hu, Chuxiong; Zhu, Yu

doi:10.1109/tii.2021.3085845

Cited by 54 publications

(10 citation statements)

References 31 publications

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“…where the superscript • (1) denotes the iteration step and κ > 0 is a constant compensation gain. Compensated by c (1) (k + N c ), the tracking error becomes…”

Section: Real-time Iterative Compensationmentioning

confidence: 99%

“…P RECISION mechatronic trajectory tracking motion systems have been universally applied in manufacturing scenarios, such as semiconductor industry, computerized numerical control (CNC) machines, nanopositioners, robotics, etc. With the urgent demand for superior tracking accuracy, disturbance rejection ability, and other tracking performances, researchers have shown an increasing interest in developing performance-oriented motion controllers [1]- [5].…”

Section: Introductionmentioning

confidence: 99%

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Comparative Study of Performance-Oriented Feedforward Compensation Strategies for Precision Mechatronic Motion Systems

Zhou

Hou

et al. 2022

IEEE Access

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To meet the progressively stringent demands for trajectory tracking performance in precision/ultra-precision industry, multiple advanced feedforward compensation strategies have been reported in recent years. However, there is no fair and comprehensive performance comparison of advantages and drawbacks of various methods under different industrial working conditions. In this paper, a comparative study of several performance-oriented feedforward strategies represented by standard iterative learning control (ILC), cascaded ILC (CILC), gated recurrent units based feedforward compensation (GRU-FFC), and real-time iterative compensation (RIC) has been systematically conducted by theoretical clarification and experimental verification. Various trajectory tracking tasks are accomplished on a precision mechatronic motion stage, and the tracking performances to be examined include (i) tracking accuracy; (ii) extrapolation capability for non-repetitive trajectories; (iii) disturbance rejection ability. Experimental results are sufficiently summarized with the combination of underlying control mechanism analysis, which can provide a comprehensive and reliable selection guidance of feedforward compensation strategies in different practical industrial scenarios.INDEX TERMS Feedforward compensation, gated recurrent units (GRU), iterative learning control (ILC), precision motion control, real-time iterative compensation (RIC), tracking performance.

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“…where the superscript • (1) denotes the iteration step and κ > 0 is a constant compensation gain. Compensated by c (1) (k + N c ), the tracking error becomes…”

Section: Real-time Iterative Compensationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Study of Performance-Oriented Feedforward Compensation Strategies for Precision Mechatronic Motion Systems

Zhou

Hou

et al. 2022

IEEE Access

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“…However, various disturbances, modeling errors [7], and time-varying system parameters [8] adversely affect the convergence of the ILC. Therefore, the control community is actively working on the ILC-based control of the PMSM servo systems working under the dynamic uncertainties [9]. Recently, researchers have combined the adaptive control and ILC to form Adaptive Iterative Learning Control (AILC) [10,11], which exploits the ILC to solve the repetitive tracking problems [12,13] and adaptive control to handle the system uncertainty problem.…”

Section: Introductionmentioning

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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“…A relatively accuracy model equalling to the inverse of the system plant is required in the model-based feedforward [10], which leads to its high dependence on both the model quality of the approximate model and the accuracy of the modelinversion. In contrary, ILC requires less prior knowledge of the system plant and outperforms the model-based feedforward in applications executing repeated tracking tasks [11].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Data-Driven Iterative Feedforward Tuning Approach Based on Fast Recursive Algorithm: With Application to a Linear Motor

Yang

Zanchetta

et al. 2023

IEEE Trans. Ind. Inf.

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The feedforward control can effectively improve the servo performance in applications with high requirements of velocity and acceleration. The iterative feedforward tuning method (IFFT) enables the possibility of both removing the need for prior knowledge of the system plant in model-based feedforward and improving the extrapolation capability for varying tasks of iterative learning control. However, most of IFFT methods require to set the number of basis functions in advance, which is inconvenient to the system design. To tackle this problem, an adaptive data-driven IFFT based on fast recursive algorithm (IFFT-FRA) is developed in this paper. Explicitly, based on FRA the proposed approach can adaptively tune the feedforward structure, which significantly increases the intelligence of the approach. Additionally, a data-based iterative tuning procedure is introduced to achieve the unbiased estimation of parameters optimization in presence of noise. Comparative experiments on a linear motor confirms the effectiveness of the proposed approach.

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Online Iterative Learning Compensation Method Based on Model Prediction for Trajectory Tracking Control Systems

Cited by 54 publications

References 31 publications

Comparative Study of Performance-Oriented Feedforward Compensation Strategies for Precision Mechatronic Motion Systems

Comparative Study of Performance-Oriented Feedforward Compensation Strategies for Precision Mechatronic Motion Systems

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

An Adaptive Data-Driven Iterative Feedforward Tuning Approach Based on Fast Recursive Algorithm: With Application to a Linear Motor

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