Robust point‐to‐point iterative learning control for constrained systems: A minimum energy approach

Zhou, Changcong; Tao, Hongfeng; Chen, Yiyang; Stojanović, Vladimir; Paszke, Wojciech

doi:10.1002/rnc.6354

Cited by 103 publications

(12 citation statements)

References 24 publications

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“…The focus of the ILC is to achieve a perfect tracking task in a limited time interval by the repeated trials, and it generally refines the desired control signal by comparing the error between the system output and the desired trajectory. The ILC has been successfully applied to various systems [1][2][3][4][5]. In recent years, based on the distributed cooperative control of multi-agent systems [6][7][8], the ILC algorithm has been extended to multi-agent systems to solve the distributed tracking problems, for example, the leader-follower consensus [9,10], the formation control [11][12][13], and the finite-time consensus [14,15], to name but a few.…”

Section: Introductionmentioning

confidence: 99%

Quantized Iterative Learning Bipartite Containment Tracking Control for Unknown Nonlinear Multi-agent Systems

Zhang,

Sang,

Zhang

et al. 2024

Neural Process Lett

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This paper proposes a quantized model-free adaptive iterative learning control (MFAILC) algorithm to solve the bipartite containment tracking problem of unknown nonlinear multi-agent systems, where the interactions between agents include cooperation and antagonistic interactions. To design the controller, the agent’s dynamics is transformed into the linear data model based on the dynamic linearization method, and then a quantized MFAILC algorithm is established based on the quantized values of the relative output measurements. The designed controller only depends on the input and output data of the agent. We prove that under the quantized MFAILC algorithm, the multi-agent systems can achieve the bipartite containment, that is, the output trajectories of followers converge to the convex hull formed by the leaders’ trajectories and the leaders’ symmetric trajectories. Finally, we provide simulations to illustrate the effectiveness of our theoretical results.

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Section: Introductionmentioning

confidence: 99%

Quantized Iterative Learning Bipartite Containment Tracking Control for Unknown Nonlinear Multi-agent Systems

Zhang,

Sang,

Zhang

et al. 2024

Neural Process Lett

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“…The authors of [17] give the initial definition of ILC theory. Then, several publications have contributed to the progress and applications of ILC in various areas, such as pick-andplace tasks [18]- [24], wafer stages [25], path planning [26]- [29], process control [30]- [32], and rehabilitation [33]. A brief literature review about ILC and formation control is presented next.…”

Section: Introductionmentioning

confidence: 99%

A Unified Framework for Multi-Agent Formation with a Non-repetitive Leader Trajectory: Adaptive Control and Iterative Learning Control

Jiang¹,

Chen²,

Chen³

et al. 2023

Preprint

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<p>Formation tracking (FT) control aims at handling cooperative tasks in multi-agent systems (MASs) to achieve desired performance. In these tasks, the leader's input is generally non-zero and unknown to all followers, i.e., its trajectory can be arbitrary and non-repetitive. In this paper, the additive property of linear systems is exploited to develop a unified framework for FT tasks of MASs, consisting of adaptive observer-based control (AOC) and iterative learning control (ILC). This framework employs an AOC controller to guarantee a fixed-shape formation between the leader and followers during the whole process, which reserves the initial condition for ILC. Also, it employs ILC to improve the FT performance of certain repetitive tasks (followers rotating around the leader) over the trials. This gives rise to a fully distributed algorithm working for a directed communication graph containing a spanning tree without requiring any eigenvalue information from the Laplacian matrix of the graph, which enables its application to MASs with a large number of agents. Comparisons are provided via a numerical simulation to show that the proposed combined AOC-ILC algorithm has less FT error than pure AOC (without ILC), which validates the feasibility and efficacy of this algorithm.</p>

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“…The constraints can be robustly satisfied under the influence of external uncertainties. In existing work, Zhou et al (2022) used an iterative learning control algorithm to optimize performance beyond tracking accuracy under constrained conditions for the point‐to‐point motion task. The algorithm is robust to model uncertainty and output perturbations.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective trajectory optimization of the wall‐building robot based on RBF–NSGA‐II in an uncertain viscoelastic contact environment

Shi

Wang

et al. 2023

Journal of Field Robotics

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To solve the problem of poor masonry quality of traditional wall‐building robots in an uncertain viscoelastic contact environment while reducing energy consumption, reducing contact forces with the environment, and improving work efficiency and smoothness, a segmented multiobjective trajectory optimization method is proposed based on radial basis function (RBF) and nondominated sorting genetic algorithm II (NSGA‐II). The method divides the motion trajectory into the free motion segment and the masonry segment. In the masonry segment, the compensation variable is introduced at the brick‐stopping position, and the values of design variables are obtained by Latin hypercube sampling. The relationship between the objective functions and the design variables is established by using an RBF substitution model. The optimal design is carried out by the NSGA‐II, and the compromise solution is obtained by using the technique for order preference by similarity to an ideal solution algorithm. On this basis, a multiobjective trajectory optimization method based on seven times nonuniform B‐spline curves is proposed for the free motion segment. According to the performance indicators, such as operation efficiency, trajectory smoothness, and energy consumption, the compromise solution is again sought and obtained. Finally, the proposed trajectory optimization method is compared with the standard gate‐shaped trajectory planning method. The results show that after trajectory optimization, the masonry efficiency of the wall‐building robot is improved by 28.36%, and the energy consumption and trajectory smoothness are reduced by 28.68% and 93.81%, respectively. At the same time, the contact force with the environment is reduced by 12.26%, and the masonry error is reduced from 2.67 to 0.13 mm. These results can contribute to the construction of walls and improve the masonry quality of bricks while considering other performance indicators.

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Robust point‐to‐point iterative learning control for constrained systems: A minimum energy approach

Cited by 103 publications

References 24 publications

Quantized Iterative Learning Bipartite Containment Tracking Control for Unknown Nonlinear Multi-agent Systems

Quantized Iterative Learning Bipartite Containment Tracking Control for Unknown Nonlinear Multi-agent Systems

A Unified Framework for Multi-Agent Formation with a Non-repetitive Leader Trajectory: Adaptive Control and Iterative Learning Control

Multiobjective trajectory optimization of the wall‐building robot based on RBF–NSGA‐II in an uncertain viscoelastic contact environment

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