Survey on stochastic iterative learning control

Intl J Robust & Nonlinear

Zhang

2019

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Summary This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.

Section: Introductionmentioning

confidence: 99%

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

Intl J Robust & Nonlinear

Zhang

2019

Self Cite

“…In this paper, we are interested in iterative learning control (ILC), which was first proposed by Arimoto in 1980s for tracking problem in robotics . After developments of over 3 decades, it had been wildly used to deal with process control of artificial intelligence systems . However, most existing literature concentrates on ordinary system, while only very a few papers consider ILC for fractional‐order systems, showing that much blank exists for further attention.…”

Section: Introductionmentioning

confidence: 99%

Learning formation control for fractional‐order multiagent systems

Luo

Wang

Math Methods in App Sciences

2018

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In this paper, we use 2 iterative learning control schemes (P‐type and PI‐type) with an initial learning rule to achieve the formation control of linear fractional‐order multiagent systems. To realize the finite‐time consensus, we assume repeatable operation environments as well as a fixed but directed communication topology for the fractional‐order multiagent systems. Both P‐type and PI‐type update laws are applied to generate the control commands for each agent. It is strictly proved that all agents are driven to achieve an asymptotical consensus as the iteration number increases. Two examples are simulated to verify the effectiveness of the proposed algorithms.

“…Iterative learning control (ILC) is a novel type of intelligent control methodology, which was first proposed by Uchiyama and then formally defined by Arimoto. After developments of over three decades, it has been widely applied to solve the precise tracking problem for various systems such as robotics, process control, and biological systems . The essential concept of ILC is to generate control signal for the current trial by using the input and tracking information from previous trials so that the tracking performance can be gradually improved along the trial axis.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning control for noninstantaneous impulsive fractional‐order systems with varying trial lengths

Liu

Wang

Intl J Robust & Nonlinear

et al. 2018

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Summary In this paper, we propose five iterative learning control (ILC) schemes for noninstantaneous impulsive fractional‐order systems with randomly varying trial lengths. We introduce a domain alignment operator to establish a rigorous convergence analysis for nonlinear fractional‐order systems. This operator guarantees that the input, state, output, and tracking error are constrained in a function space that is designed in advance. Moreover, with the help of this operator, we extend the conventional ILC scheme from discrete systems to continuous and algebraic systems with incorporating redundant tracking information. In addition, nonlinear ILC schemes are also presented with a geometric analysis concept. All proposed schemes are shown to be convergent to the desired tracking trajectory. Two illustrative examples are provided to verify the theoretical results.