Case studies of filtering techniques in multirate iterative learning control

Zhang, Bin; Ye, Yongqiang; Zhou, Keliang; Wang, Danwei

doi:10.1016/j.conengprac.2014.01.018

Cited by 13 publications

(3 citation statements)

References 28 publications

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“…In this section, the rationality and effectiveness of the proposed algorithm are verified by a joint moving in the horizontal plane of the industrial SCARA robot SEIKOTT3000 in Zhang et al (2014). Controller-to-actuator and actuator-to-controller data are transmitted over a network with communication bandwidth constraints.…”

Section: Simulationmentioning

confidence: 99%

Quantized iterative learning control of communication-constrained systems with encoding and decoding mechanism

Tao,

Zhuang

et al. 2024

Transactions of the Institute of Measurement and Control

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In practical applications, due to the limited communication bandwidth, the network control systems (NCSs) are prone to data dropouts when the load is high. In this paper, the problem of quantized iterative learning control (ILC) based on encoding and decoding mechanism for such communication-constrained systems is studied. By combining the encoding and decoding mechanism with the uniform quantizer, the network burden and the impact of quantization error on the tracking performance of the systems are significantly mitigated. Meanwhile, data dropouts are represented as the Bernoulli random variable model, and an ILC law based on gradient is designed. When data dropouts occur, the signals maintain the value of the previous trial, otherwise the signals are updated. For this kind of learning framework, the asymptotic zero-error tracking performance has been rigorously proven for the uniform quantizer. To validate the proposed design, a joint motion of an industrial robot in the horizontal plane is simulated as an example.

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Section: Simulationmentioning

confidence: 99%

Quantized iterative learning control of communication-constrained systems with encoding and decoding mechanism

Tao,

Zhuang

et al. 2024

Transactions of the Institute of Measurement and Control

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“…In this subsection, we verify the theoretical results by an industrial SEIKO D-TRAN 3000 Series robot. 33,34 The SEIKO D-TRAN 3000 Series robot is a four-axis closed-loop DC servo selectively compliance assembly robot arm (SCARA), where each of the four axes provides a different motion and contributes to one degree of freedom of the robot. The main characteristics of the SCARA are its high precision, repeatability, and speed.…”

Section: Numerical Experiments For Discrete-time Siso Systemmentioning

confidence: 99%

A novel iterative learning control scheme based on Broyden‐class optimization method

Qian,

Shen

2023

Intl J Robust & Nonlinear

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SummaryThis study proposes a novel iterative learning control scheme for discrete‐time linear systems based on the Broyden‐class optimization method. To overcome the difficulty of lacking system information, a cost function is introduced for the performance index by constructing a positive‐definite matrix with little system information. An optimization‐based learning control algorithm is proposed using a Hessian matrix approximation and the generated input sequence is demonstrated to exhibit a superlinear convergence rate. The proposed scheme is extended to address the point‐to‐point tracking problem. Numerical simulations are provided to verify the effectiveness of the proposed approach.

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“…The objective of our work is to apply ILC to improve the performance of sampled-data systems that perform repetitive tasks in the multirate framework. In [26], [27], a pseudo downsampled technique was derived for a P-type multirate ILC; however, this approach was limited by the fact that reducing the sampling rate of the error input may result in the loss of some frequency information. Alternatively, a multirate ILC method aiming to suppress intersample oscillation was proposed in [28], in which fast output measurements were obtained for an ILC with a slow-rate design.…”

Section: Introductionmentioning

confidence: 99%

Fast-Update Iterative Learning Control for Performance Enhancement With Application to Motion Systems

Wang

Hsiao

2022

IEEE Access

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Iterative learning control (ILC) algorithms are typically used to improve the performance of repetitive processes. Numerous successful applications of ILC, such as computer numerical control (CNC) machining processes, robot manipulation, and lithography processes, have been reported. However, ILC often exhibits less than satisfactory performance since the control unit operates at a limited sampling rate in consideration of cost. In this study, the multiloop, multirate structure of servo motor control systems is taken advantage of and a fast-update multirate ILC (FILC) scheme is proposed for high-accuracy trajectory tracking, where compensation is updated at a rate faster than that of the original position loop without the need for redesigning the feedback controller. The key difficulty related to the design of the FILC lies in the extremely complex and time-varying dynamics inherent in multirate systems. To address this problem, a novel equivalent single-rate parametric model description of the multirate system is derived, which enables the use of the efficient norm optimal ILC algorithm. Consequently, a computationally efficient FILC is obtained to improve the performance. Subsequently, the proposed FILC is applied to the position control of the CNC motion stage. Simulation and experimental results are used to verify the effectiveness of the proposed method.INDEX TERMS Iterative learning control, multirate system, norm optimal, linear peridoic time varying, precise motion control

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Case studies of filtering techniques in multirate iterative learning control

Cited by 13 publications

References 28 publications

Quantized iterative learning control of communication-constrained systems with encoding and decoding mechanism

Quantized iterative learning control of communication-constrained systems with encoding and decoding mechanism

A novel iterative learning control scheme based on Broyden‐class optimization method

Fast-Update Iterative Learning Control for Performance Enhancement With Application to Motion Systems

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