A high-order internal model based iterative learning control scheme for discrete linear time-varying systems

Zhou, Wei; Yu, Miao; Huang, Deqing

doi:10.1007/s11633-015-0886-x

Cited by 43 publications

(18 citation statements)

References 34 publications

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“…To verify the effectiveness and convergence property of the proposed encoding and decoding ILC quantisation method, a permanent magnet linear motor (PMLM) model is utilised. The discretised model of PMLM is given as follows [14]:…”

Section: Simulation Examplementioning

confidence: 99%

Zero‐error convergence of iterative learning control based on uniform quantisation with encoding and decoding mechanism

Zhang

Shen

2018

IET Control Theory & Applications

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In this study, the zero-error convergence of the iterative learning control for a tracking problem is realised by incorporating a uniform quantiser with an encoding and decoding mechanism. Under this scheme, the system output is first transformed and encoded. Then, the encoded information is transmitted back for updating the input. The results are extended to a finite quantisation level situation under the same framework and a simulation using a permanent magnet linear motor is performed to demonstrate the effectiveness of the proposed scheme.

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Section: Simulation Examplementioning

confidence: 99%

Zero‐error convergence of iterative learning control based on uniform quantisation with encoding and decoding mechanism

Zhang

Shen

2018

IET Control Theory & Applications

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“…In addition, the size of memory and computation time can be reduced greatly by choosing only necessary control at certain time instants. Therefore, the PTP‐ILC and TILC approaches are not a simple extension of the traditional ILC that tracks an entire reference trajectory including both necessary and unnecessary points.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven high‐order terminal iterative learning control with a faster convergence speed

Chi

Huang

Hou³

et al. 2017

Intl J Robust & Nonlinear

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In this paper, a novel high-order optimal terminal iterative learning control (highorder OTILC) is proposed via a data-driven approach for nonlinear discrete-time systems with unknown orders in the input and output. The objective is to track the desired values at the endpoint of the operation cycle. The terminal tracking errors over more than one previous iterations are used to enhance the high-order OTILC's performance with faster convergence. From rigor of the analysis, the monotonic convergence of the terminal tracking error is proved along the iteration direction. More importantly, the condition for a high-order OTILC to outperform the low-order ones is first established by this work. The learning gain is not fixed but iteratively updated by using the input and output (I/O) data, which enhances the flexibility of the proposed controller for modifications and expansions. The proposed method is datadriven in which no explicit models are used except for the input and output data.The applications to a highly nonlinear continuous stirred tank reactor and a highly nonlinear fed-batch fermentater demonstrate the effectiveness of the proposed high-order OTILC design. KEYWORDSdata-driven approach, faster convergent speed, high-order algorithm, monotonic convergence, terminal iterative learning control

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“…The classical IMC method offers advanced control procedures that can be applied with very good performance to any process with known model, but the found particular control algorithm cannot be used for other processes. Thus, the controller complexity depends mainly on the complexity of the process model and the control system performance stated by the designer [5][6][7][8][9][10]. For these reasons, the control algorithms of IMC type are not widely used in current industrial practice.…”

Section: Introductionmentioning

confidence: 99%

A quasi-universal practical IMC algorithm

Cirtoaje

2017

Automatika

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The control algorithm incorporates a process feedback path of pure proportional type (only for integral-type or unstable processes), a process model of second order plus time delay and a realizable second-order internal controller which has not a tuning filter time constant as usual in the classical IMC design, but a tuning gain with standard value 1, that can be used by the human operator to get a strong or weak control action. The model parameters (steady-state gain, time delay and transient time) can be easily experimentally determined and can be online verified and corrected. The algorithm is practical and quasi-universal because it is easily tunable, has a unique form and can be applied to almost all process types: stable proportional processes (with or without time delay, with or without overshoot, of minimum or nonminimum phase), integral processes and even some unstable processes. The proposed algorithm is better than the proportional-integral-derivative (PID) algorithm (which is also quasi-universal and practical) due to its robustness, high control performance (especially for processes with time delay) and simple experimental procedure for determining the controller parameters. Some applications are presented to highlight the main features of the algorithm and the tuning procedure for all process types.

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A high-order internal model based iterative learning control scheme for discrete linear time-varying systems

Cited by 43 publications

References 34 publications

Zero‐error convergence of iterative learning control based on uniform quantisation with encoding and decoding mechanism

Zero‐error convergence of iterative learning control based on uniform quantisation with encoding and decoding mechanism

Data‐driven high‐order terminal iterative learning control with a faster convergence speed

A quasi-universal practical IMC algorithm

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