Adaptive Fixed-Time Tracking Control of Cart-Pendulum Robotic Systems with Bias Actuator Dynamics

Shuo Chen; Xuansen Zhao; Xiaozheng Jin; Hai Wang

doi:10.3390/act14050245

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Adaptive Fixed-Time Tracking Control of Cart-Pendulum Robotic Systems with Bias Actuator Dynamics

Journal article

Open access

Peer reviewed

Adaptive Fixed-Time Tracking Control of Cart-Pendulum Robotic Systems with Bias Actuator Dynamics

Shuo Chen, Xuansen Zhao, Xiaozheng Jin and Hai Wang

Actuators, Vol.14(5), 245

2025

DOI: https://doi.org/10.3390/act14050245

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Abstract

Engineering

Engineering, Mechanical

Instruments & Instrumentation

Science & Technology

Technology

This research addresses the challenge of precise trajectory tracking for cart-pendulum robotic systems affected by unknown nonlinear actuator dynamics. We introduce a novel control framework that combines neural network modeling with adaptive parameter estimation to handle these complex dynamics. By characterizing state-dependent actuator behavior through custom-designed linear filters and adaptive laws, our approach identifies system parameters with high precision. We then develop an innovative fixed-time adaptive sliding mode controller that guarantees convergence within a predetermined timeframe regardless of initial conditions. Lyapunov stability analysis confirms that tracking errors converge to a small neighborhood around zero within the specified time bounds, with the size of the neighborhood determined by the design parameters. Simulation studies on a watermelon transportation robot validate our approach's practical effectiveness, demonstrating improved tracking accuracy and robustness against actuator disturbances compared with conventional methods.

Details

Title: Adaptive Fixed-Time Tracking Control of Cart-Pendulum Robotic Systems with Bias Actuator Dynamics
Authors/Creators: Shuo Chen - Qilu University of Technology
Xuansen Zhao - Taiyuan University of Technology
Xiaozheng Jin - Qilu University of Technology
Hai Wang - Murdoch University, Centre for Water, Energy and Waste
Publication Details: Actuators, Vol.14(5), 245
Publisher: MDPI
Number of pages: 19
Grant note: tsqn202211208 / Taishan Scholars Program Open research fund of Anhui Provincial Key Laboratory of Intelligent Low-Carbon Information Technology and Equipment 62173193 / National Nature Science Foundation of China; National Natural Science Foundation of China (NSFC) 2024RCKY003 / Science Education Industry Integration and Innovation Project
Identifiers: 991005779512607891
Murdoch Affiliation: School of Engineering and Energy
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 4 Electrical Engineering, Electronics & Computer Science; 4.29 Automation & Control Systems; 4.29.104 Adaptive Control
Web Of Science research areas: Engineering, Mechanical; Instruments & Instrumentation
ESI research areas: Engineering