Adaptive Fault-tolerant Neural Control for Large-scale Systems with Actuator Faults

Gong, Jianye; Jiang, Bin; Shen, Qikun

doi:10.1007/s12555-018-0729-y

Cited by 21 publications

(2 citation statements)

References 23 publications

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“…Robot positioning errors and fault tolerance have attracted the attention of scholars in various fields (Gong et al, 2019;Guo et al, 2019;Rasouli et al, 2020). Blas and Blanke (2011) have studied automatic baling robots, which can locate plants using binocular vision and classify the appearance of plants.…”

Section: Visual Fault Tolerancementioning

confidence: 99%

Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review

et al. 2020

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The utilization of machine vision and its associated algorithms improves the efficiency, functionality, intelligence, and remote interactivity of harvesting robots in complex agricultural environments. Machine vision and its associated emerging technology promise huge potential in advanced agricultural applications. However, machine vision and its precise positioning still have many technical difficulties, making it difficult for most harvesting robots to achieve true commercial applications. This article reports the application and research progress of harvesting robots and vision technology in fruit picking. The potential applications of vision and quantitative methods of localization, target recognition, 3D reconstruction, and fault tolerance of complex agricultural environment are focused, and fault-tolerant technology designed for utilization with machine vision and robotic systems are also explored. The two main methods used in fruit recognition and localization are reviewed, including digital image processing technology and deep learning-based algorithms. The future challenges brought about by recognition and localization success rates are identified: target recognition in the presence of illumination changes and occlusion environments; target tracking in dynamic interference-laden environments, 3D target reconstruction, and fault tolerance of the vision system for agricultural robots. In the end, several open research problems specific to recognition and localization applications for fruit harvesting robots are mentioned, and the latest development and future development trends of machine vision are described.

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Section: Visual Fault Tolerancementioning

confidence: 99%

Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review

et al. 2020

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“…Adaptive fault-tolerant control (FTC) has been used far and wide [24][25][26]. In [27], active adaptive fault-tolerant neural control was discussed for mitigating actuator fault problems in large-scale uncertain systems. By introducing a backstepping technique to fault-tolerant control, an adaptive actuator fault compensation control was studied in [28] for a class of uncertain multi-input single-out discrete-time systems with triangular forms.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Fault-Tolerant Control for the Yaw Control of UAV Helicopters with Input Saturation and Full-State Constraints

Zhang

Chen

2020

Applied Sciences

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In this paper, an adaptive neural fault-tolerant tracking control scheme is presented for the yaw control of an unmanned-aerial-vehicle helicopter. The scheme incorporates a non-affine nonlinear system that manages actuator faults, input saturation, full-state constraints, and external disturbances. Firstly, by using a Taylor series expansion technique, the non-affine nonlinear system is transformed into an affine-form expression to facilitate the desired control design. In comparison with previous techniques, the actuator efficiency is explicit. Then, a neural network is considered to approximate unknown nonlinear functions, and a time-varying barrier Lyapunov function is employed to prevent transgression of the full-state variables using a backstepping technique. Robust adaptive control laws are designed to handle parameter uncertainties and unknown bounded disturbances to cut down the number of learning parameters and simplify the computational burden. Moreover, an auxiliary system is constructed to guarantee the pitch angle of the UAV helicopter yaw control system to satisfy the input constraint. Uniform boundedness of all signals in a closed-loop system is ensured via Lyapunov theory; the tracking error converges to a small neighborhood near zero. Finally, when the numerical simulations are applied to a yaw control of helicopter, the adaptive neural controller demonstrates the effectiveness of the proposed technique.

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Adaptive Fault Tolerant Supervisory Consensus Control for Nonlinear Strict-Feedback Multiagent Systems

Gong

Jiang

2021

Lecture Notes in Electrical Engineering

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Adaptive Fault-tolerant Neural Control for Large-scale Systems with Actuator Faults

Cited by 21 publications

References 23 publications

Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review

Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review

Adaptive Neural Fault-Tolerant Control for the Yaw Control of UAV Helicopters with Input Saturation and Full-State Constraints

Adaptive Fault Tolerant Supervisory Consensus Control for Nonlinear Strict-Feedback Multiagent Systems

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