Neural network adaptive command filtered control of robotic manipulators with input saturation

Lin, Wei; Yang, Chunzhi

doi:10.1177/1729881419894779

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…In actual physical systems, actuators are subject to certain physical limitations, the output of the actuator cannot be arbitrarily large, so there may be an actuator oversaturation problem. The problem of actuator saturation cannot be ignored, therefore, people have already done a lot of works in this field [27][28][29][30][31]. For example, in the study of Ye et al [32], a finite time convergence auxiliary system was presented to reduce the adverse effects of saturation on the parallel robots system.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy neural network finite‐time command filtered control of n‐link robotic systems with actuator saturation

Zhang,

Jiang,

Sam Ge

2023

Asian Journal of Control

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The finite time tracking control of n‐link robotic system is studied for model uncertainties and actuator saturation. Firstly, a smooth function and adaptive fuzzy neural network online learning algorithm are designed to address the actuator saturation and dynamic model uncertainties. Secondly, a new finite‐time command filtered technique is proposed to filter the virtual control signal. The improved error compensation signal can reduce the impact of filtering errors, and the tracking errors of system quickly converge to a smaller compact set within finite time. Finally, adaptive fuzzy neural network finite‐time command filtered control achieves finite‐time stability through Lyapunov stability criterion. Simulation results verify the effectiveness of the proposed control.

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Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy neural network finite‐time command filtered control of n‐link robotic systems with actuator saturation

Zhang,

Jiang,

Sam Ge

2023

Asian Journal of Control

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Autonomous Control of Lift System based on Actor-Critic Learning for Air Cushion Vehicle

Zhou,

Wang,

et al. 2023

OCEANS 2023 - Limerick

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Model-Assisted Reduced-Order ESO Based Command Filtered Tracking Control of Flexible-Joint Manipulators with Matched and Mismatched Disturbances

et al. 2022

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Flexible-joint manipulators (FJMs) have been widely used in the fields of industry, agriculture, medical service, aerospace, etc. However, the FJMs in practical applications inevitably encounter various uncertainties including matched and mismatched disturbances. In this paper, we consider the high precision tracking control problem of FJMs in the presence of unknown lumped matched and mismatched disturbances. An efficient model-assisted composite control approach is proposed by integrating two reduced-order extended state observers (RESOs), a second-order command filtered backstepping (SCFB) technique and an error compensation dynamic system. Unlike some existing methods, the RESOs constructed with partial known model information are capable of estimating and compensating the matched and mismatched disturbances simultaneously. In addition, by employing the SCFB with an error compensation system, the proposed approach can not only overcome the problem of “explosion of complexity” inherent in backstepping, but also reduce the filtering errors arising from the command filters. The stability of the resulting control system and the convergence of error signals are guaranteed by Lyapunov stability theory. Comparative simulations are conducted for a single-link FJM with both matched and mismatched disturbances, and the results show that the proposed approach achieves a better tracking performance, i.e., compared with conventional backstepping method and adaptive fuzzy command filtered control method, the tracking accuracy is improved by 99.5% and 99.2%, respectively.

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Neural network adaptive command filtered control of robotic manipulators with input saturation

Cited by 3 publications

References 34 publications

Adaptive fuzzy neural network finite‐time command filtered control of n‐link robotic systems with actuator saturation

Adaptive fuzzy neural network finite‐time command filtered control of n‐link robotic systems with actuator saturation

Autonomous Control of Lift System based on Actor-Critic Learning for Air Cushion Vehicle

Model-Assisted Reduced-Order ESO Based Command Filtered Tracking Control of Flexible-Joint Manipulators with Matched and Mismatched Disturbances

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