A stochastic iterative learning control algorithm with application to an induction motor

Saab, Samer S.

doi:10.1080/00207170310001646282

Cited by 57 publications

(27 citation statements)

References 35 publications

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“…On the other hand, if G is a full row rank matrix for all k and t C [0, nt], w(t, k) = 0, and there exists a positive constant cx0 such that x (0, k + 1)-x(O, k) < cxO, then there exist positive constants c1p and cr,, such that~( t, k + 1)-x(t, k) < c<k k i(t+,u,k) l2 < CV) and lim 0(t+p,k)= 0 k k-o where where the output error 309q(-,k) A Cq()[Xd() x(, k)] with 1 < q < m. U Proof The proof follows similar arguments that are presented in [14], thus, omitted. Remark 2.…”

Section: Problem Formulationmentioning

confidence: 83%

“…The proposed suboptimal algorithm is partially motivated by the optimal theory presented in the previous section, in particular, Equations (14), (18) V. NUMERICAL EXAMPLE In this example, three different ILC algorithms are applied to the same example considered in [5], where performance of the algorithms are compared. The ILC algorithms under examination are:…”

Section: Suboptimal Second-order Ilcmentioning

confidence: 99%

“…The system considered (similar to [1 1]- [14]) is a discretetime-varying linear system described by the following dif-…”

Section: Problem Formulationmentioning

confidence: 99%

“…Remark 2. A suboptimal recursive algorithm, presented in [14], is based on unknown system dynamics and unknown disturbance statistics. The robustness and convergence is shown to possess similar characteristics to the ones of the optimal recursive algorithm summarized in Theorem 2 of this manuscript.…”

Section: Problem Formulationmentioning

confidence: 99%

“…1) The first-order suboptimal ILC algorithm presented in [14], denoted by ILC1, where the recursive update of the learning gain is given by…”

Section: Suboptimal Second-order Ilcmentioning

confidence: 99%

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On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise

Saab

Proceedings of the 44th IEEE Conference on Decision and Control

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Higher-Order Iterative Learning Control (HO-ILC) algorithms use past system control information from more than one past iterative cycle. This class of ILC algorithms have been proposed aiming at improving the learning efficiency and performance. This paper addresses the optimality of HO-ILC in the sense of minimizing the control error covariance matrix in the presence of measurement noise. It is shown that the optimal weighting matrices corresponding to the control information associated with more than one cycle preceding the current cycle are zero. Consequently, an optimal HO-ILC is automatically reduced to an optimal first-order ILC. The system under consideration is a linear discrete-time varying systems with different relative degree between the input and each output. Furthermore, a suboptimal second-order ILC is proposed for a class of nonlinear systems. Based on a numerical example, it is shown that a compatible suboptimal first-order ILC yields better performance than the proposed suboptimal second-order ILC algorithm.

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Section: Problem Formulationmentioning

confidence: 83%

Section: Suboptimal Second-order Ilcmentioning

confidence: 99%

“…The system considered (similar to [1 1]- [14]) is a discretetime-varying linear system described by the following dif-…”

Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

“…1) The first-order suboptimal ILC algorithm presented in [14], denoted by ILC1, where the recursive update of the learning gain is given by…”

Section: Suboptimal Second-order Ilcmentioning

confidence: 99%

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On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise

Saab

Proceedings of the 44th IEEE Conference on Decision and Control

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Linearly monotonic convergence of nonlinear parameter‐optimal iterative learning control to linear discrete‐time‐invariant systems

Liu

Ruan

2021

Intl J Robust & Nonlinear

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In this paper, a parameter‐optimal iterative learning control (POILC) scheme is developed for linear discrete‐time‐invariant systems with Markov parameters available. For the scheme the iteration‐time‐variable learning‐gain vector is assigned as the argument while the sequential performance index is consisting of the quadratic tracking error energy plus quadratic learning effort intensity weighed by the iteration‐varying tuning factor. By making use of matrix theory, the learning‐gain vector and the convergence rate are explicated which conveys that the POILC is a nonlinear scheme and is linearly monotonously convergent with the smaller tuning factors delivering the faster convergence. In particular, the linearly monotone convergences are respectively exploited for the extreme case when the tuning‐factor sequence is identically nullity and for the optimal performance index. Further, a quasi POILC is established which considers additive systematic parameters uncertainties in composing of the learning‐gain vector. Rigorous analysis indicates that the quasi POILC is linearly monotonically convergent within a wider uncertainty range which conveys that the POILC is robust to the system parameters uncertainties. Numerical simulations testify the validity and the effectiveness.

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Immune Inspired Collaborative Learning Controllers

Ding

Chen

2017

Studies in Systems, Decision and Control

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A stochastic iterative learning control algorithm with application to an induction motor

Cited by 57 publications

References 35 publications

On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise

On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise

Linearly monotonic convergence of nonlinear parameter‐optimal iterative learning control to linear discrete‐time‐invariant systems

Immune Inspired Collaborative Learning Controllers

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