Convergence characteristics of proportional-type iterative learning control in the sense of Lebesgue-
            <i>p</i>
            norm

Ruan, Xiaoe; Bien, Zeungnam; Wang, Q.

doi:10.1049/iet-cta.2010.0388

Cited by 38 publications

(17 citation statements)

References 15 publications

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“…Recently, iterative learning control problems of P-type, D-type, I-type, or their combination schemes have been widely applied to various types of repetitive or batch dynamical systems (see, e.g., [32][33][34][35][36][37][38]). The problem on designing an ILC for uncertain plants with time-delays has not been fully investigated, and only a limited number of the results are available so far (see, e.g., [39][40][41]).…”

Section: Introductionmentioning

confidence: 99%

Analysis of a Class of Fractional Nonlinear Multidelay Differential Systems

Gao

Wang

Zhou

2017

Discrete Dynamics in Nature and Society

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We address existence and Ulam-Hyers and Ulam-Hyers-Mittag-Leffler stability of fractional nonlinear multiple time-delays systems with respect to two parameters' weighted norm, which provides a foundation to study iterative learning control problem for this system. Secondly, we design PID-type learning laws to generate sequences of output trajectories to tracking the desired trajectory. Two numerical examples are used to illustrate the theoretical results.

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Section: Introductionmentioning

confidence: 99%

Analysis of a Class of Fractional Nonlinear Multidelay Differential Systems

Gao

Wang

Zhou

2017

Discrete Dynamics in Nature and Society

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“…Numerous ILC contributions have come forth over the past three decades scoping from theoretical investigations to practical applications. As one of the important theoretical issues, the convergence analysis has been discussed by Amann et al [2], Xu [3], and Meng et al (2-D analysis approach) [5] and Ruan et al [6,7]. In addition, the robustness has been discussed from many aspects, such as stochastic noise in [8], iteration-varying disturbances in [9], model uncertainty in [10] and time-delay uncertainty in [11], etc., stability and initial state learning have been researched in [12] and [13], respectively.…”

Section: Introductionmentioning

confidence: 99%

Quasi-Newton-type optimized iterative learning control for discrete linear time invariant systems

Geng

Ruan

2015

Control Theory Technol.

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In this paper, a quasi-Newton-type optimized iterative learning control (ILC) algorithm is investigated for a class of discrete linear time-invariant systems. The proposed learning algorithm is to update the learning gain matrix by a quasi-Newton-type matrix instead of the inversion of the plant. By means of the mathematical inductive method, the monotone convergence of the proposed algorithm is analyzed, which shows that the tracking error monotonously converges to zero after a finite number of iterations. Compared with the existing optimized ILC algorithms, due to the superlinear convergence of quasi-Newton method, the proposed learning law operates with a faster convergent rate and is robust to the ill-condition of the system model, and thus owns a wide range of applications. Numerical simulations demonstrate the validity and effectiveness.

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“…One single-link robotic arm was simulated by ILC [10]. Ruan et al [11] proposed the first and second-order P-type ILC schemes for linear time-invariant (LTI) system. Parameter optimization based iterative learning control (POILC) was proposed to tackle the ILC issue when the original plant is a discrete time LTI system [12].…”

Section: Introductionmentioning

confidence: 99%

Trajectory tracking control for a nonholonomic mobile robot using an improved ILC

Xiao

2014

2014 IEEE International Conference on Information and Automation (ICIA)

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This paper develops a kinematic path-tracking algorithm for a nonholonomic mobile robot using an improved iterative learning control (I-ILC) technique. The proposed algorithm produces a velocity command to the wheeled robot, in addition, the state disturbances and measurement noises are taken into consideration. In the learning rule, an adjustable parameter is defined and by adjusting this parameter we can obtain the best learning rule. From the simulation results we find that the system outputs and control inputs are bounded to converge to the desired values. The MATLAB software is used for simulation to verify the feasibility and validity of the proposed learning algorithm.

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Convergence characteristics of proportional-type iterative learning control in the sense of Lebesgue- p norm

Cited by 38 publications

References 15 publications

Analysis of a Class of Fractional Nonlinear Multidelay Differential Systems

Analysis of a Class of Fractional Nonlinear Multidelay Differential Systems

Quasi-Newton-type optimized iterative learning control for discrete linear time invariant systems

Trajectory tracking control for a nonholonomic mobile robot using an improved ILC

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