A novel adaptive impedance control algorithm for underwater manipulators

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Abstract
Introduction: Underwater manipulators performing precision operations are simultaneously affected by nonlinear hydrodynamic uncertainties and abrupt changes in environmental contact conditions, making it difficult for conventional fixed-parameter impedance control to maintain both trajectory-tracking accuracy and compliant interaction. To address this problem, this paper proposes an adaptive impedance control framework for underwater manipulators. Methods: A multi-degree-of-freedom dynamic model is first established by explicitly considering rigid-body dynamics, added-mass effects, nonlinear hydrodynamic damping, and lumped uncertainties. The lumped disturbance is then decomposed according to its functional dependence into a state-dependent unmodeled hydrodynamic component and a residual exogenous disturbance component. An RBF neural network is employed to approximate the state-dependent nonlinear component, while a nonlinear extended state observer estimates the remaining online disturbance, thereby forming a composite feedforward compensation mechanism. On this basis, a prescribed-performance-based adaptive impedance law is developed to regulate the virtual stiffness and damping according to the tracking and contact-force errors while constraining transient performance. Lyapunov analysis establishes the uniform ultimate boundedness of the resulting closed-loop signals. A MATLAB/Simulink–ROS/Gazebo co-simulation platform is constructed to evaluate the proposed controller under time-varying current disturbances and abrupt contact-stiffness transitions. Results: Repeated simulation trials under multiple disturbance conditions are conducted to evaluate the statistical robustness of the performance indices. Compared with traditional fixed-parameter impedance control, the proposed method reduces the end-effector trajectory RMSE by 82.1%, the peak contact force by 57.9%, and the force-settling time by 49.4%. Discussion: These results demonstrate improved disturbance rejection and compliant interaction within the tested operating conditions, validating the effectiveness of the proposed architecture without requiring prior knowledge of environmental stiffness.

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