Underwater Manipulator Linear Active Disturbance Rejection Control Based on Hierarchical Cascaded Linear Extended State Observer - Yulong Xu; Kailan Gao; Xuelin Wang; Qian Li; Changlin Wang; Peng Wang (IEEE Access)
Addressing the challenges of strong coupling, nonlinearity, and time-varying characteristics in underwater manipulator systems, along with the inevitable environmental disturbances such as gravity, buoyancy, and hydrodynamic forces that reduce system state convergence speed and affect control performance, an underwater manipulator dynamic model integrating the Lagrange equation, Morison equation, and nonlinear friction forces is built. A linear active disturbance rejection control (LADRC) strategy incorporating a hierarchical cascade linear extended state observer (HCLESO) is proposed. According to LADRC theory, the underwater manipulator system is approximated as an independent subsystem, which reduces the coupling effect between each joint of the manipulator arm. To address the incomplete estimation issue of the Linear Extended State Observer (LESO), a HCLESO is designed to estimate the total system disturbance, suppress high-frequency measurement noise, and reduce the disturbance observation residual, thereby improving the control precision and robustness of the underwater manipulator. The simulation results indicate that the proposed control strategy significantly improves system performance in terms of smaller control inputs, reduced overshoot, and lower steady-state errors. Compared to conventional PD control, the proposed strategy exhibits superior control accuracy and faster response. Compared to the standard LADRC, the introduction of HCLESO provides notable enhancements. With HCLESO ( $p=2$ ), the Integral of Absolute Error (IAE) for joints $q_{1}$ and $q_{2}$ is reduced by 50.1% and 64.0%, respectively, while the Integral of Time-weighted Absolute Error (ITAE) decreases by 36.2% and 50.0%. Further improvements are observed with HCLESO ( $p=3$ ), where the IAE for joints $q_{1}$ and $q_{2}$ is reduced by 50.1% and 65.1%, respectively, and the ITAE by 46.2% and 57.8%.