A Discrete-Time Adaptive ILC for Systems with Random Initial Condition and Iteration-Varying Trajectory

Chi, Ronghu; Sui, Shulin; Yu, Lei; Hou, Zhongsheng; Xu, Jian‐Xin

doi:10.3182/20080706-5-kr-1001.02445

Cited by 8 publications

(9 citation statements)

References 11 publications

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“…The key feature of ILC is to use control information of the preceding execution to improve the present execution. In the past three decades, much progress has been made in the field of ILC (Ahn, Chen, & Moore, 2007;Chi, Hou, & Xu, 2008;Lee, Lee, & Kim, 2000;Liu, Xu, & Wu, 2009;Ouyang, Zhang, & Gupta, 2006;Sun & Wang, 2003;Wang, 2000;Xu & Qu, 1998;Zhang, Ouyang, & Sun, 2010). In Xu and Qu (1998), a robust ILC is developed to handle a class of non-linear uncertain systems.…”

Section: Introductionmentioning

confidence: 99%

“…In Ahn et al (2007), detailed classifications of ILC categories are given, and in Zhang et al (2010), a generic principle to construct different ILC schemes is presented. In Chi et al (2008), a discrete-time adaptive ILC is * Corresponding author. Email: zhshhou@bjtu.edu.cn introduced to deal with iteration-varying desired trajectory and random initial condition.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive iterative learning control for a class of non-linearly parameterised systems with input saturations

Zhang

Hou

et al. 2014

International Journal of Systems Science

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In this paper, an adaptive iterative learning control scheme is proposed for a class of non-linearly parameterised systems with unknown time-varying parameters and input saturations. By incorporating a saturation function, a new iterative learning control mechanism is presented which includes a feedback term and a parameter updating term. Through the use of parameter separation technique, the non-linear parameters are separated from the non-linear function and then a saturated difference updating law is designed in iteration domain by combining the unknown parametric term of the local Lipschitz continuous function and the unknown time-varying gain into an unknown time-varying function. The analysis of convergence is based on a time-weighted Lyapunov-Krasovskii-like composite energy function which consists of time-weighted input, state and parameter estimation information. The proposed learning control mechanism warrants a L 2 [0,T ] convergence of the tracking error sequence along the iteration axis. Simulation results are provided to illustrate the effectiveness of the adaptive iterative learning control scheme.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive iterative learning control for a class of non-linearly parameterised systems with input saturations

Zhang

Hou

et al. 2014

International Journal of Systems Science

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“…Analogous to adaptive control, the robustness of AILC is an important issue. Iteration-domain deadzone (Xu and Badrinath 2000) or projection (Chi et al 2008) are possible solutions for robustification of AILC. AILC with output feedback or observer is to be explored for nonlinear systems.…”

Section: Remarkmentioning

confidence: 99%

“…The extension of AILC to systems in parameter-pure-feedback form with time-varying parameters remain to be explored. In discrete-time AILC (Chi et al 2008), the nonlinear function f (x) is limited to GLC, whereas the controller is LLC, as shown in (34)-(36). On the other hand, discrete-time adaptive control has been extended to LLC systems (Kanellakopoulos 1994).…”

Section: Remarkmentioning

confidence: 99%

A survey on iterative learning control for nonlinear systems

2011

International Journal of Control

374

173

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“…The key feature of this technique is to use information from the previous (and/or current) operation (or iteration) in order to enable the controlled system to perform better progressively from operation to operation [1][2][3][4][5][6] . On the other hand, networked control systems (NCSs) are also the focus of many research studies over the last few decades [7][8][9][10] .…”

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confidence: 99%

Effect analysis of data dropout on iterative learning control

Hou

Chi

2013

2013 25th Chinese Control and Decision Conference (CCDC)

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Iterative learning control (ILC) is an attractive technique to deal with systems that execute the same task repeatedly over a finite time-interval. The key feature of this technique is to use information from the previous (and/or current) operation (or iteration) in order to enable the controlled system to perform better progressively from operation to operation [1][2][3][4][5][6] . On the other hand, networked control systems (NCSs) are also the focus of many research studies over the last few decades [7][8][9][10] . Compared to the conventional point-to-point system connection, NCSs have the advantages of easy installation and reduced setup, wiring, and maintenance costs. In NCSs, data can travel through the communication channels from the sensors to the controller and from the controller to the actuators. Data packet dropout (a kind of uncertainty) is a common problem in networked control systems and could happen due to node failures or network congestion. Because of random dropout, conventional methods for estimation and control cannot be used directly.The data dropout problem in the context of ILC has been studied in [11][12][13][14][15][16], and such an ILC system is call intermittent ILC system. In [11] and [12], an optimal learning gain matrix is given for intermittent ILC systems. They considered the problem where each component in the multivariable output vector of the plant is subject to a dependent or independent dropout respectively. In [13], an averaging ILC algorithm is proposed to overcome the random data dropout, and it is shown that such an ILC algorithm can perform well and achieve asymptotic convergence in ensemble average along the iteration axis. In [14], the convergence of first-order and high-order ILC for linear intermittent ILC systems is considered. Using the super-vector technique, such an ILC system can be modeled as an asynchronous dynamical system with rate constraints on events in the iteration domain, and then the convergence condition can be provided by solve a binary linear matrix inequality. It is worth noting that the binary linear matrix inequality is difficult to be solved, especially for the high order ILC scheme. To avoid the problem, another convergence condition in the expectation sense is given in [15]. In [16], the issue of intermittent ILC is considered for a class of nonlinear systems. Key conclusions of these works are that the ILC systems can still guarantee convergence in the face of data dropout as long as there is not 100% dropout. However, the effect of data dropout on ILC has not been studied. Intuitively, data dropout implies less information can be used, and it should be affect some performances of the system. This observation motivates the present study.In this paper, we analyze the effect of data dropout for intermittent ILC with asymptotic stability and monotonic convergence. Using the super-vector formulation, the relationship between convergence speed and data dropout rate can be presented. The remainder of this paper is organized as follows: In section...

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A Discrete-Time Adaptive ILC for Systems with Random Initial Condition and Iteration-Varying Trajectory

Cited by 8 publications

References 11 publications

Adaptive iterative learning control for a class of non-linearly parameterised systems with input saturations

Adaptive iterative learning control for a class of non-linearly parameterised systems with input saturations

A survey on iterative learning control for nonlinear systems

Effect analysis of data dropout on iterative learning control

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