Iterative Learning Control of Iteration-Varying Systems via Robust Update Laws with Experimental Implementation

Altın, Berk; Willems, Jeroen; Oomen, Tae Tom; Barton, Kira

doi:10.1016/j.conengprac.2017.02.005

Cited by 40 publications

(29 citation statements)

References 33 publications

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“…Theorem 2: Suppose that the linear system (7) satisfies assumption (8). If there exist symmetric positive definite matrices…”

Section: Hoim-based Ilc Designmentioning

confidence: 99%

“…Theorem 3: Suppose that the linear system (7) satisfies Assumptions (A1)-(A2), i.e. the relationships (13) and (8). If there exist symmetric positive definite matrices W 1 ∈ ℝ (n + m α q) × (n + m α q) , W 2 ∈ ℝ m β q × m β q , W ≜ W 1 ⊕ W 2 , and matrix R ∈ ℝ p × (n + m α q + m β q) such that the following LMI:…”

Section: It Follows Thatmentioning

confidence: 99%

“…For instance, the convergence of ILC for iteration-varying uncertainties was discussed in [7]. In [8], a robust ILC was designed for a linear discrete-time system with iterationvarying input-output operator. For an industrial flatbed inkjet printer with iteration-varying position reference, an ILC with a rational feedforward parameterisation was proposed in [9,10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dual IM‐based ILC scheme for linear discrete‐time systems with iteration‐varying reference

Zhu

2018

IET Control Theory & Applications

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“…Theorem 2: Suppose that the linear system (7) satisfies assumption (8). If there exist symmetric positive definite matrices…”

Section: Hoim-based Ilc Designmentioning

confidence: 99%

Section: It Follows Thatmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dual IM‐based ILC scheme for linear discrete‐time systems with iteration‐varying reference

Zhu

2018

IET Control Theory & Applications

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“…However, the disturbances were assumed to be repetitive, and the controller was synthesized under fixed gains. Robust ILC schemes were proposed for discrete-time linear systems by Altin et al 16 and Meng and Moore; 17 therefore, these controllers cannot deal with uncertain nonlinear systems. In the work by Bouakrif and Zasadzinski, 18 a model-based ILC was developed under the resetting condition and an assumption of knowing the exact inertia matrix of a robot manipulator.…”

Section: Introductionmentioning

confidence: 99%

Adaptive robust iterative learning control with application to a Delta robot

Boudjedir

Boukhetala

2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, an adaptive robust iterative learning control is developed to solve the trajectory tracking problem of a parallel Delta robot performing repetitive tasks and subjected to external disturbances. The proposed control scheme is composed of an adaptive proportional–derivative controller to increase the convergence rate, a proportional–derivative-type iterative learning control to enhance the tracking performances through the repetitive trajectory as well as a robust term to compensate the repetitive and nonrepetitive disturbances. The practical assumption of alignment condition is introduced instead of the classical assumption of resetting conditions. The asymptotic convergence is proved using Lyaponuv analysis, and it is shown that the tracking error decreases through the iterations. Simulation and experiments are performed on a Delta robot to demonstrate the effectiveness and the superiority of the proposed controller over the traditional iterative learning control.

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“…Iterative learning control has important significance for situations with nonlinearity, strong coupling, challenging modeling, and the requirement of high-accuracy trajectory control [16]. The existing studies on the theory of iterative learning control concentrate mainly on proofs of convergence for the learning algorithm, the learning velocity, studies of the structural form for the learning law, the robustness of, and analysis method, for the iterative learning process, the initial values, and assorted engineering applications [17][18][19][20][21][22][23]. There have been few studies on iterative learning control of the servo system for the permanent magnet synchronous motor.…”

Section: Introductionmentioning

confidence: 99%

Accelerated Iterative Learning Control of Speed Ripple Suppression for a Seeker Servo Motor

Lin

2018

Algorithms

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To suppress the speed ripple of a permanent magnet synchronous motor in a seeker servo system, we propose an accelerated iterative learning control with an adjustable learning interval. First, according to the error of current iterative learning for the system, we determine the next iterative learning interval and conduct real-time correction on the learning gain. For the learning interval, as the number of iterations increases, the actual interval that needs correction constantly shortens, accelerating the convergence speed. Second, we analyze the specific structure of the controller while applying reasonable assumptions pertaining to its condition. Using the λ-norm, we analyze and apply our mathematical knowledge to obtain a strict mathematical proof on the P-type iterative learning control and obtain the condition of convergence for the controller. Finally, we apply the proposed method for periodic ripple inhibition of the torque rotation speed of the permanent magnet synchronous motor and establish the system model; we use the periodic load torque to simulate the ripple torque of the synchronous motor. The simulation and experimental results indicate the effectiveness of the method.

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Iterative Learning Control of Iteration-Varying Systems via Robust Update Laws with Experimental Implementation

Cited by 40 publications

References 33 publications

Dual IM‐based ILC scheme for linear discrete‐time systems with iteration‐varying reference

Dual IM‐based ILC scheme for linear discrete‐time systems with iteration‐varying reference

Adaptive robust iterative learning control with application to a Delta robot

Accelerated Iterative Learning Control of Speed Ripple Suppression for a Seeker Servo Motor

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