Fault-tolerant iterative learning control for batch processes with time-varying state delays and uncertainties

Chen, Hua; Xu, Rui; Tang, Yawen; Xiao, Long

doi:10.1080/00207721.2023.2230974

Cited by 9 publications

(3 citation statements)

References 46 publications

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“…Furthermore, extracting deeper information and processing data from more sources will inevitably impact its real-time performance. Therefore, we will attempt to combine various strategies like neural network adaptive dynamic surface course-keeping controller (Chen et al , 2022) and IMFAC (Chen et al , 2023a, 2023b) that focus on reducing latency to address this limitation.…”

Section: Discussionmentioning

confidence: 99%

MCFilter: feature filter based on motion-correlation for LiDAR SLAM

Sun,

Tang,

et al. 2023

RIA

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Purpose This study aims to introduce a novel noise filter module designed for LiDAR simultaneous localization and mapping (SLAM) systems. The primary objective is to enhance pose estimation accuracy and improve the overall system performance in outdoor environments. Design/methodology/approach Distinct from traditional approaches, MCFilter emphasizes enhancing point cloud data quality at the pixel level. This framework hinges on two primary elements. First, the D-Tracker, a tracking algorithm, is grounded on multiresolution three-dimensional (3D) descriptors and adeptly maintains a balance between precision and efficiency. Second, the R-Filter introduces a pixel-level attribute named motion-correlation, which effectively identifies and removes dynamic points. Furthermore, designed as a modular component, MCFilter ensures seamless integration into existing LiDAR SLAM systems. Findings Based on rigorous testing with public data sets and real-world conditions, the MCFilter reported an increase in average accuracy of 12.39% and reduced processing time by 24.18%. These outcomes emphasize the method’s effectiveness in refining the performance of current LiDAR SLAM systems. Originality/value In this study, the authors present a novel 3D descriptor tracker designed for consistent feature point matching across successive frames. The authors also propose an innovative attribute to detect and eliminate noise points. Experimental results demonstrate that integrating this method into existing LiDAR SLAM systems yields state-of-the-art performance.

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Section: Discussionmentioning

confidence: 99%

MCFilter: feature filter based on motion-correlation for LiDAR SLAM

Sun,

Tang,

et al. 2023

RIA

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“…In the past decades, consensus, which is a fundamental collective behavior of multiagent systems, has attracted enormous attention from various research field, and its related research results have been applied in some engineering fields, for example, batch processes, 1 unmanned aerial and ground vehicles, 2 surface vessels, [3][4][5] and so on. Consensus problem means that the agents reach an agreement on their states of interest by exchanging information locally in a distributed way.…”

Section: Introductionmentioning

confidence: 99%

Average-rendezvous with connectivity preservation for first-order multiagent systems with constant reference signals

Jiang,

Liu

2024

International Journal of Advanced Robotic Systems

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Average-rendezvous problem with connectivity preservation of constant reference signals is investigated for first-order multiagent systems. According to a constructive potential function, a proportional–integral average-rendezvous algorithm is proposed to make agents converge to the average value of constant reference signals as well as the network connectivity is preserved all the time. Sufficient convergence conditions are obtained for the algorithm by designing the proper Lyapunov functions. Numerical examples illustrate the correctness of theoretical results.

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“…In the subsequent development of nonholonomic control systems, more attention has been paid to systems with uncertainties. Thus, some nonlinear control approaches, such as sliding mode control [10, 11], adaptive control [12, 13], fuzzy control [14, 15], and

H_{\infty}

control [16, 17], have arisen to address the challenges of nonlinear uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Tracking controller of extended chained nonholonomic systems with matched disturbance and input saturation

Huang,

Gao,

Zhang

2023

IET Control Theory & Appl

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This paper addresses the tracking control for an extended chained nonholonomic system under input saturation and matched disturbances. Specifically, the tracking error dynamic system is split into two subsystems in a cascade form. An integral sliding mode saturated controller is designed in combination with a finite‐time observer (FTDO) to ensure the finite‐time stability of the first subsystem, thereby simplifying the design of the second subsystem. A backstepping‐based adaptive saturated tracking control method is proposed for the second subsystem by incorporating the filter technology. The stability of the resulting closed‐loop system is demonstrated, and the tracking errors can be arbitrarily minimized through the appropriate design parameter selection. The effectiveness of the proposed scheme is validated through simulation and experimental results.

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Fault-tolerant iterative learning control for batch processes with time-varying state delays and uncertainties

Cited by 9 publications

References 46 publications

MCFilter: feature filter based on motion-correlation for LiDAR SLAM

MCFilter: feature filter based on motion-correlation for LiDAR SLAM

Average-rendezvous with connectivity preservation for first-order multiagent systems with constant reference signals

Tracking controller of extended chained nonholonomic systems with matched disturbance and input saturation

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