Improving Boundary Level Calculation in Quantized Iterative Learning Control With Encoding and Decoding Mechanism

Huo, Niu; Shen, Dong

doi:10.1109/access.2019.2918186

Cited by 8 publications

(6 citation statements)

References 28 publications

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“…The proposed scheme achieves zero‐error tracking performance. Whereafter, the tight upper bounds of quantization level are established using the lifting method for a linear system, 45 where the computed upper bounds are considerably close to the actual maximum input value. This is superior for the practical selection of the finite‐level uniform quantizer and vitally improves the existing results 30,40,44 …”

Section: Introductionmentioning

confidence: 99%

“…This study is a deeper investigation of the mechanism and algorithm design of NCSs based on previous studies 30,39,40,44,45 . Specifically, the problems of quantized iterative learning control (QILC) for a generally networked structure, 30 and QILC combined with an encoding–decoding mechanism for an unreliable communication network at the measurement side 44 have been considered.…”

Section: Introductionmentioning

confidence: 99%

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Finite‐level uniformly quantized learning control with random data dropouts

Huo

Jiang

Shen

et al. 2022

Intl J Robust & Nonlinear

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In this study, a quantized iterative learning control method with an encoding-decoding mechanism is investigated for networked control systems with constrained transmission bandwidths and random data dropouts at both the measurement and actuator sides. The intermittent update principle is used to address the problem of data asynchronism caused by two-sided data dropouts.Then, a new "process" concept is introduced for the convergence analysis. The input sequence is guaranteed to achieve asymptotic zero-error convergence to the unknown desired input for the given reference through an appropriate selection of scaling sequences. Furthermore, the quantization errors are shown bounded, where the upper bounds of the quantization level are precisely determined for selecting a finite-level uniform quantizer. To verify the proposed scheme, an example of a permanent magnet linear motor is simulated.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finite‐level uniformly quantized learning control with random data dropouts

Huo

Jiang

Shen

et al. 2022

Intl J Robust & Nonlinear

Self Cite

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show abstract

“…In the repeated operation process, based on previous information, iterative learning control (ILC) adopts the iterative method to make the change process of the control quantity gradually approach the expected change process [3]- [5]. ILC has been applied in many different control fields [1], [6]- [9]. The conventional iterative learning control strategy was adopted to control the piezoelectric actuators in [1], results showed that it is superior to pure proportional-integral (PI) controller.…”

Section: Introductionmentioning

confidence: 99%

Predictive Iterative Learning Speed Control With On-Line Identification for Ultrasonic Motor

Jingzhuo

Huang

2020

IEEE Access

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Aiming at the needs of ultrasonic motor motion control, a new two-dimensional (2D) predictive control objective function is proposed. Different from the existing methods, the objective function consists of three terms, including the product of the control quantity and the error of the previous control process. Based on the objective function, the predictive iterative learning control (ILC) law is derived by using the design method of generalized predictive control (GPC) without specifying ILC law form in advance. An on-line identification method for model parameters is given to realize effective identification under a small amount of data circumstances, and therefore, the parameters of controller are adjusted adaptively according to the identification results. The proposed control method is validated both in simulation and experiment. The experimental results show that the proposed predictive iterative learning control strategy can obtain better control effect than GPC, and has more obvious characteristics of iterative learning control. It can maintain the expected performance under the condition of intermittent loading and replacing the motor. It presents strong robustness. INDEX TERMS Ultrasonic motor, iterative learning control, generalized predictive control, on-line identification.

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“…On the other hand, the design and convergence analysis of an ILC with data quantization is also significant and several achievements have been made using quantizers to quantify the output data, input data, and tracking error, respectively (Bu et al, 2015; Huo and Shen 2019a; Shen and Xu, 2016, 2017; Xiong et al, 2017; Zhang and Li, 2017). In Bu et al (2015), the convergence of ILC with output quantization is proved for a linear system.…”

Section: Introductionmentioning

confidence: 99%

“…The consensus tracking problem of quantized iterative learning controllers for digital networks with time-varying topologies is discussed for linear systems in Xiong et al (2017). Both a probability quantizer and a finite uniform quantizer-based ILC method have been proposed for linear systems, respectively, in Shen and Xu (2017) and Huo and Shen (2019a) for the zero-error tracking problem. An extended result for linear discrete-time systems has been proposed in Huo and Shen (2019b) by combining random data dropouts.…”

Section: Introductionmentioning

confidence: 99%

Multi-lagged-input information enhancing quantized iterative learning control

Zhang

Chi

2020

Transactions of the Institute of Measurement and Control

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Quantization is a significant technique in network control to save limited bandwidth. In this work, two new multi-lagged-input-based quantized iterative learning control (MLI-QILC) methods are proposed by using output quantization and error quantization, respectively. The multi-lagged-input iterative dynamic linearization method (MLI-IDL) is introduced to build a linear data model of nonlinear systems using additional control inputs in lagged time instants and multiple parameters where the condition of nonzero input change is not required any longer. The MLI-QILC is proposed by designing two new objective functions utilizing the quantized data of the system outputs and tracking errors, respectively. With rigorous analysis, it is shown that the proposed MLI-QILC with output quantization guarantees that the tracking error converges to a bound which is related to the quantization density and the bound of the desired trajectory. Furthermore, an asymptotic convergence can be achieved for the proposed MLI-QILC method with error quantization. The theoretical results are verified by simulations.

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Improving Boundary Level Calculation in Quantized Iterative Learning Control With Encoding and Decoding Mechanism

Cited by 8 publications

References 28 publications

Finite‐level uniformly quantized learning control with random data dropouts

Finite‐level uniformly quantized learning control with random data dropouts

Predictive Iterative Learning Speed Control With On-Line Identification for Ultrasonic Motor

Multi-lagged-input information enhancing quantized iterative learning control

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