On iterative learning control with high-order internal models

Liu, Chunping; Xu, Jian‐Xin; Wu, Jun; Tan, Ying

doi:10.1109/icca.2009.5410155

Cited by 15 publications

(25 citation statements)

References 14 publications

(10 reference statements)

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“…High-order internal model (HOIM) was used in [18] to express a known variation in iteration index. In [19], an ILC algorithm was proposed for continuous-time systems tracking iterative variant reference trajectories generated by an HOIM. HOIM was then adopted in ILC for discrete-time systems in [20] to improve the permanent magnet linear motor velocity tracking performance.…”

Section: Introductionmentioning

confidence: 99%

Projection algorithm based adaptive iterative learning control for a class of discrete-time systems

Liu

Zhou

2015

The 27th Chinese Control and Decision Conference (2015 CCDC)

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In this paper, adaptive iterative learning control scheme is designed for a class of discrete-time uncertain systems with random initial state and unknown control gain. The system uncertainty is generated by a stable high-order internal model. The proposed controller incorporates a projection algorithm. Through rigorous analysis, the asymptotical learning convergence along the iteration axis in a finite time interval can be guaranteed, provided the desired trajectory is iteration-varying.

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Section: Introductionmentioning

confidence: 99%

Projection algorithm based adaptive iterative learning control for a class of discrete-time systems

Liu

Zhou

2015

The 27th Chinese Control and Decision Conference (2015 CCDC)

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“…In both cases, a bounded-input bounded-output learning estimation scheme is to be involved for stability and robustness issues. The first issue has been partially solved in [12,Remark 3] so that the corresponding result, when generalized in Section 3 to include 'high order' learning estimation schemes [17][18][19][20][21][22] through the choice of a suitable Lyapunov-like function, may be used to simultaneously solve the second challenging one. Let F.t / be a suitable measurable feedback signal, and let .t / be an uncertain T -periodic signal being related to the reference input u .t / that guarantees perfect output tracking for compatible initial conditions.…”

Section: Introductionmentioning

confidence: 99%

Linear repetitive learning controls for nonlinear systems by Padé approximants

Tomei

Verrelli

2014

Adaptive Control & Signal

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We discuss the use of OEm; m-Padé approximants in the implementation of repetitive learning controls solving the output tracking problem (via output error feedback) in the presence of uncertain periodic reference and/or disturbance signals with known common period. The aim is to address the stability issues concerning those approximants when a linear learning controller-designed through a detailed stability proof (involving the use of a suitable Lyapunov-like function) and described by a transfer function exhibiting all its poles with negative real part-is to be obtained as well as to evaluate the corresponding closed-loop performances: robustness (for instance with respect to additive disturbance noises due to unmodeled sensor dynamics) is consequently achieved with improvements in the output tracking errors appearing as the approximation order m increases. Even though the case of any relative degree may be explicitly addressed, in this paper, for the sake of clarity, we restrict our attention to the learning problem for the class of single-input, singleoutput, minimum phase, time-invariant systems with known relative degree D 2, uncertain parameters and uncertain output-dependent nonlinearities. Numerical simulation results illustrate the theoretical derivations.

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“…Further, in practice many systems states are bounded because of hardware constraints, for example, SRM states that comprise of phase currents, phase torques and velocity. In such circumstances, the tracking error can be made arbitrarily small, as shown in Liu, Xu, and Wu (2010).…”

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confidence: 98%