High-Order Feedback Iterative Learning Control Algorithm with Forgetting Factor

Wang, Hongbin; Dong, Jian; Wang, Yueling

doi:10.1155/2015/826409

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…A2: variable forgetting factor λ j = 1 − 0 . 1 j (Wang et al, 2015) and fixed control gains, according to equation (59).…”

Section: Simulationmentioning

confidence: 99%

“…Dai et al (2014) discussed the convergence problem of ILCFF for a class of parabolic linear distributed parameter systems with uncertainty. Wang et al (2015) and Wang et al (2016) proposed high-order feedback and feedback–feedforward ILCFFs for a class of nonlinear systems considering uncertain and non-repetitive disturbances. Liu and Wang (2017) proposed a new ILCFF comprising an output equation with nonlinear input to deal with various tracking problems in the finite time intervals of fractional order systems.…”

Section: Introductionmentioning

confidence: 99%

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Iterative learning control realized using an iteration-varying forgetting factor based on optimal gains

Dai

Gong

et al. 2021

Transactions of the Institute of Measurement and Control

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Iterative learning control with forgetting factor (ILCFF) is widely used in control engineering. However, choosing the optimal parameters of ILCFF to improve system-output characteristics has been a challenging issue for controller designers. This paper proposes an iterative learning control (ILC) algorithm that involves a variable forgetting factor based on optimal gains for a class of discrete linear time-invariant systems with aperiodic disturbances. The convergence of the algorithm is analyzed, and the necessary and sufficient condition for its convergence is derived in terms of proportional–integral–derivative coefficients. A design method based on optimal gains is established to determine the algorithm coefficients and to accelerate system convergence. Furthermore, the influence of the forgetting factor on both the system-output error and the scope of the proposed algorithm is analyzed. Additionally, the most suitable system type for the application of the forgetting factor is determined. The effectiveness of the algorithm is verified by performing a theoretical analysis and a case-based simulation. The proposed iteration-varying optimal forgetting-factor-based ILC algorithm undergoes fast convergence with a small system-output error. The findings disrupt the conventional view that the use of the forgetting factor increases system-output error. In fact, in a system with small trajectory and increased disturbances, the error induced by the forgetting factor may be smaller than that of the traditional optimal ILC algorithm.

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“…A2: variable forgetting factor λ j = 1 − 0 . 1 j (Wang et al, 2015) and fixed control gains, according to equation (59).…”

Section: Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iterative learning control realized using an iteration-varying forgetting factor based on optimal gains

Dai

Gong

et al. 2021

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

show abstract

“…Some ILC algorithms have been developed for discrete-time systems, but they are restricted to linear systems [5,6]. In an ordinary way, the ILC update rules are composed of P-type [7,8], PD-type [9,10], and high-order type [11].…”

Section: Introductionmentioning

confidence: 99%

Discrete PID-Type Iterative Learning Control for Mobile Robot

Wang

Dong

Wang

2016

Journal of Control Science and Engineering

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Through studying tracking problems of the wheeled mobile robot, this paper proposed a discrete iterative learning control approach based on PID with strong adaptability, fast convergence, and small error. This algorithm used discrete PID to filter rejection and restrained the influence of interference and noise on trajectory tracking, which made it more suitable for engineering application. The PID-type iterative learning convergence condition and certification procedure are presented. The results of simulation reveal that the PID-type ILC holds the features of simplicity, strong robustness, and high repeating precision and can well meet the control requirement of nonlinear discrete system.

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A Novel Parallel Assembly Sequence Planning Method for Complex Products Based on PSOBC

Yang

Shu

et al. 2020

Mathematical Problems in Engineering

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Parallel assembly sequence planning (PASP) greatly impacts on efficiency of assembly process. In traditional methods, large scale of matrix calculation still limits efficiency of PASP for complex products. A novel PASP method is proposed to address this issue. To avoid matrix calculation, the synchronized assembly Petri net (SAPN) is firstly established to describe the precedence relationships. Associated with the SAPN model, the PASP process can be implemented via particle swarm optimization based on bacterial chemotaxis (PSOBC). Characterized by an attraction-repulsion phase, PSOBC not only prevents premature convergence to a high degree, but also keeps a more rapid convergence rate than standard particle swarm optimization (PSO) algorithm. Finally, feasibility and effectiveness of the proposed method are verified via a case study. With different assembly parallelism degrees, optimization results show that assembly efficiency of the solution calculated by PSOBC method is 9.0%, 4.2%, and 3.1% better than the standard PSO process.

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High-Order Feedback Iterative Learning Control Algorithm with Forgetting Factor

Cited by 3 publications

References 20 publications

Iterative learning control realized using an iteration-varying forgetting factor based on optimal gains

Iterative learning control realized using an iteration-varying forgetting factor based on optimal gains

Discrete PID-Type Iterative Learning Control for Mobile Robot

A Novel Parallel Assembly Sequence Planning Method for Complex Products Based on PSOBC

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