article · IEEE Access
Flexible robot systems face challenges with vibrations, unknown perturbations, and parametric uncertainties. This work introduces a hybrid control framework combining input shaping with fuzzy active disturbance rejection control for a flexible joint robotic manipulator. The input shaping technique reduces overshoot and increases reaction time, while an extended state observer handles unexpected perturbations and parameter variations. Additionally, fuzzy logic tunes linear feedback control gains online to enhance the dynamic capabilities of the system. Experimental evaluations on a rotary flexible joint manipulator demonstrate that this hybrid approach achieves superior trajectory tracking, vibration suppression, and disturbance rejection compared to standard active disturbance rejection controllers, fuzzy controllers, and tuned PID baselines. The system maintains robust performance despite joint nonlinearities.
Flexible robotic arms are valuable in modern industrial and academic environments, but unwanted vibrations and mechanical uncertainties make precise motion difficult. Enhancing motion tracking while dampening vibrations allows lightweight, flexible robots to perform complex tasks more accurately. By handling unexpected physical disturbances automatically, this control strategy helps robots operate reliably under shifting operational conditions without losing stability or precision.
This approach is aimed at industrial and robotic engineering applications requiring precise motion and vibration suppression in flexible manipulators. Potential users include robotics developers and automation engineers operating flexible joint systems. Having been validated through experimental testing on a rotary flexible joint manipulator, the technology represents applied and experimentally tested research, although the abstract does not provide a timeline or pathway for commercial deployment.
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Flexible robot systems are important in a variety of academic and industrial settings. Introducing innovative ideas for improving their performance is always valuable due to their wide range of practical applications. Compared to traditional methods, the utilization of soft computing techniques can improve the overall performance by predicting and optimizing the outcomes. This research proposes a hybrid control method that combines input shaping with fuzzy active disturbance rejection control for a flexible joint robotic manipulator with unknown perturbations and parametric uncertainties. The suggested algorithm’s control objective is to adapt and learn in different real-world scenarios to accurately follow the required trajectories while dampening the system’s vibrations. Overshoot is reduced, and reaction time is increased by employing an input shaping approach, while the extended state observer is built to handle unexpected perturbations and uncertainties in parameters. Additionally, fuzzy logic adjusts the linear feedback control law gains online to boost the control system’s dynamic capability. Compared with active disturbance rejection controller, interval type-2 fuzzy logic controller, input shaping-active disturbance rejection controller, modified linear active disturbance rejection controller, genetic algorithm-fuzzy logic controller, and fuzzy-tuned PID, the experimental results indicate that the hybrid input shaping enhanced fuzzy based active disturbance rejection controller control law is efficient and resilient. The advised controller achieves the highest robustness regarding position control, disturbance rejection, trajectory tracking, and parameter variation, which is superior to other hybrid controllers in most performance metrics. Rotary Flexible Joint Manipulator system is excellent at tracking and suppressing disturbances, despite uncertainties and nonlinearity of flexible joints.
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DOI: 10.1109/access.2024.3453944
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