Adaptive Fault-Tolerant Boundary Control for a Flexible String With Unknown Dead Zone and Actuator Fault

Ren, Yong; Puchen, Zhu; Zhao, Zhijia; Yang, Jingfeng; Zou, Tao

doi:10.1109/tcyb.2020.3044144

Cited by 50 publications

(14 citation statements)

References 59 publications

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“…Practical systems are often constrained by the limited capacity of physical components. Among these constraint phenomena [ 31 – 34 ], input saturation (IS) is a very common one, which usually destroys the control performance of systems and even results in instability. Since 1950s, control problems of systems with IS has attracted considerable attention and plenty of studies have been reported [ 35 – 38 ].…”

Section: Introductionmentioning

confidence: 99%

Finite-time stabilization and H∞ control of Port-controlled Hamiltonian systems with disturbances and saturation

Wang

2021

PLoS ONE

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The finite-time stabilization and finite-time H∞ control problems of Port-controlled Hamiltonian (PCH) systems with disturbances and input saturation (IS) are studied in this paper. First, by designing an appropriate output feedback, a strictly dissipative PCH system is obtained and finite-time stabilization result for nominal system is given. Second, with the help of the Hamilton function method and truncation inequality technique, a novel output feedback controller is developed to make the PCH system finite-time stable when IS occurs. Further, a finite-time H∞ controller is designed to attenuate disturbances for PCH systems with IS, and sufficient conditions are presented. Finally, a numerical example and a circuit example are given to reveal the feasibility of the obtained theoretical results.

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Section: Introductionmentioning

confidence: 99%

Finite-time stabilization and H∞ control of Port-controlled Hamiltonian systems with disturbances and saturation

Wang

2021

PLoS ONE

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“…Before starting to schedule a computing task v i [ V, it must be ensured that all its predecessors have been scheduled. Assuming that v i is scheduled for local execution, the ready time of the task for local execution is defined as (Zhan, 2020;Yang et al, 2019b;Qiao et al, 2014;Ren et al, 2021;Li et al, 2020;Liu and Qiao, 2014;Peng et al, 2020;Huang et al, 2020;Yang et al, 2019c):…”

Section: Numerical Optimization Of Multi-sensor Fusion Based On Edge Computingmentioning

confidence: 99%

Collaborative processing and data optimization of environmental perception technologies for autonomous vehicles

Song

Zhou

Chang³

et al. 2021

Self Cite

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Purpose Autonomous driving depends on the collection, processing and analysis of environmental information and vehicle information. Environmental perception and processing are important prerequisite for the safety of self-driving of vehicles; it involves road boundary detection, vehicle detection, pedestrian detection using sensors such as laser rangefinder, video camera, vehicle borne radar, etc. Design/methodology/approach Subjected to various environmental factors, the data clock information is often out of sync because of different data acquisition frequency, which leads to the difficulty in data fusion. In this study, according to practical requirements, a multi-sensor environmental perception collaborative method was first proposed; then, based on the principle of target priority, large-scale priority, moving target priority and difference priority, a multi-sensor data fusion optimization algorithm based on convolutional neural network was proposed. Findings The average unload scheduling delay of the algorithm for test data before and after optimization under different network transmission rates. It can be seen that with the improvement of network transmission rate and processing capacity, the unload scheduling delay decreased after optimization and the performance of the test results is the closest to the optimal solution indicating the excellent performance of the optimization algorithm and its adaptivity to different environments. Originality/value In this paper, the results showed that the proposed method significantly improved the redundancy and fault tolerance of the system thus ensuring fast and correct decision-making during driving.

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“…Lakhekar et al 28 combined fuzzy control with sliding mode control to reduce the influence of state switching through the empirical characteristics of fuzzy rules, thus reducing chattering of sliding mode control. Ren et al 29 developed an adaptive faulttolerant BC by utilizing strict formula derivations to compensate for unknown composite disturbance, dead zone, and actuator fault in the FS system. Huang et al 30 proposed a method based on the combination of RBF neural network and sliding mode control, and used the approximation ability of neural network function to load the switching state quantity into the neural network function in the form of input quantity, thus achieving the purpose of eliminating chattering.…”

Section: Introductionmentioning

confidence: 99%

The adaptive neural network sliding mode control for angle/force tracking of the dexterous hand

Qin

Shi²,

Gao³

et al. 2021

Advances in Mechanical Engineering

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In order to achieve high precision control of the dexterous hand, an adaptive neural network sliding mode control algorithm based on the U-K (Udwadia-Kalaba) equation is proposed. Firstly, based on the U-K equation and considering the ideal and non-ideal constrained force at each link of the dexterous hand, the detailed dynamic equation is derived. Secondly, considering the uncertainty of the non-ideal constrained force (mainly the friction force on each link of the dexterous hand) and the chattering phenomenon when using sliding mode control alone, the adaptive neural network and the sliding mode control algorithm are combined to realize the high-precision tracking and estimation of each link angle trajectory and the non-ideal constrained force. Finally, in order to verify the correctness and rationality of the proposed algorithm, the 3-DOF spatial dexterous hand is taken as the simulated object. The simulation results show that the tracking and estimation errors of each link angle and the non-ideal constrained force are 10−2 order of magnitude.

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Adaptive Fault-Tolerant Boundary Control for a Flexible String With Unknown Dead Zone and Actuator Fault

Cited by 50 publications

References 59 publications

Finite-time stabilization and H∞ control of Port-controlled Hamiltonian systems with disturbances and saturation

Finite-time stabilization and H∞ control of Port-controlled Hamiltonian systems with disturbances and saturation

Collaborative processing and data optimization of environmental perception technologies for autonomous vehicles

The adaptive neural network sliding mode control for angle/force tracking of the dexterous hand

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