Robust Finite-Time Adaptive Nonlinear Control System for an EOD Robotic Manipulator: Design, Implementation, and Experimental Validation

• Published in: IEEE access Vol. 12; pp. 93859 - 93875
• Main Authors: Ccari, Luis F. Canaza, Aguilar, Walker, Supo, Elvis, Espinoza, Erasmo Sulla, Vidal, Yuri Silva, Medina, Nicolas, Pari, Lizardo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuators; Adaptive algorithms; Adaptive control; Adaptive systems; Asymptotic stability; Back propagation networks; backstepping; Closed loops; Control stability; Control systems design; Controllers; Convergence; EOD manipulator; Feedback control; Human engineering; Manipulator dynamics; Manipulators; Neural networks; Nonlinear control; nonlinear control system; Nonlinear control systems; Numerical stability; Ordnance; Real time; Robot arms; Robot control; Robotics; Robust control; Sliding mode control; Stability analysis; Tracking control; Tracking errors; Trajectory control
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3424463

To improve the efficiency of human-robot interaction (HRI) in hazardous ordnance manipulation tasks, the design of an advanced control system for a robotic manipulator needs to be investigated. This article presents a robust finite-time adaptive nonlinear control system designed to address the trajectory tracking control of explosive ordnance disposal (EOD) robotic manipulator, considering the actuator dynamics and subject to external disturbances and uncertainties. First, the theoretical design of the controller is developed. In order to achieve fast convergence, high tracking accuracy, and strong robustness, which are fundamental features in EOD applications, a Backstepping Fast Terminal Sliding Mode Control (BFTSMC) strategy is proposed. The sliding surface coefficients are adaptively tuned in real-time by a neural network, where the backpropagation algorithm updates the neural network weight. The combination of the BFTSMC strategy and the adaptive neural network scheme results in the Adaptive Backstepping Fast Terminal Sliding Mode Control (ABFTSMC) strategy, achieving improved control system performance. The stability of the closed-loop control system is demonstrated using Lyapunov theory to guarantee the convergence of tracking errors to the origin in finite time. Next, a detailed description of the hardware system, encompassing sensors, actuators, and controller boards, is provided for the practical implementation of the ABFTSMC strategy. Finally, the proposed control system is experimentally validated through several tests on a real EOD robotic manipulator, and an extensive comparison analysis with other control approaches is performed. The results evidence that the ABFTSMC strategy demonstrates high robustness, fast convergence in finite time, and good tracking accuracy, which are crucial qualities in real-world EOD applications.