A New Hybrid Control Scheme for Tracking Control Problem of AUVs With System Uncertainties and External Disruptions

• Published in: Journal of field robotics Vol. 42; no. 3; pp. 716 - 741
• Main Authors: Chaudhary, Km Shelly, Kumar, Naveen
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 01.05.2025
• Subjects: Autonomous underwater vehicles; Compensators; Control systems; Controllers; Convergence; Disturbances; Dynamical systems; finite‐time convergence; Fractional calculus; fractional‐order Barbalat's lemma; fractional‐order sliding mode control; Hybrid control; Neural networks; Nonlinear dynamics; Radial basis function; RBFNN; Simulation; Sliding mode control; Tracking control; Tracking errors; Transient response; Uncertainty
• ISSN: 1556-4959; 1556-4967
• DOI: 10.1002/rob.22492

ABSTRACT Autonomous underwater vehicles (AUVs) are highly nonlinear, coupled, uncertain, and time‐varying mechatronic systems that inevitably suffer from uncertainties and environmental disturbances. This study presents an intelligent hybrid fractional‐order fast terminal sliding mode controller that utilizes the positive aspects of a model‐free control approach, designed to enhance the tracking control of AUVs. Using a nonlinear fractional‐order fast terminal sliding manifold, the proposed control approach integrates intelligent hybrid sliding mode control with fractional calculus to guarantee finite‐time convergence of system states and provide explicit settling time estimates. The nonlinear dynamics of the AUVs is modeled using radial basis function neural networks, while bound on uncertainties, external disturbances, and the reconstruction errors are accommodated by the adaptive compensator. By using a fast terminal‐type sliding mode reaching law, the controller exhibits enhanced transient response, resulting in robustness and finite‐time convergence of tracking errors. Using fractional‐order Barbalat's lemma and the Lyapunov technique, the stability of the control scheme is validated. The effectiveness of the proposed control scheme is validated by a numerical simulation study, which also shows enhanced trajectory tracking performance for AUVs over existing control schemes. This hybrid technique addresses the complicated nature of AUV dynamics in unpredictable circumstances by utilizing the advantages of model‐free intelligent control and fractional calculus.