Dual‐Adaptive Control for Smooth Obstacle Avoidance in Hybrid Boom Lifts

Abstract

Hybrid boom lifts operating in cluttered workspaces with rigid obstacles face a control conflict between accurate trajectory tracking and safe obstacle avoidance, which can lead to discontinuous joint velocities and abrupt end‐effector motions. Existing methods still struggle to balance tracking performance, collision safety, and motion smoothness. To address this problem, this paper presents a Dual‐Adaptive Control (DAC) framework that coordinates task priorities through two synergistic mechanisms. The Adaptive Obstacle Response mechanism introduces a dual‐threshold strategy with critical and warning distances, adjusting the commanded avoidance velocity online according to estimated collision risks. In parallel, the Adaptive Control Priority mechanism links feedback gains to the pseudo‐distance so that control authority between tracking and avoidance is reallocated smoothly rather than abruptly. Simulation and experimental studies on a 15 m hybrid boom lift show that, compared with a conventional fixed‐gain method and representative real‐time obstacle‐avoidance algorithms including APF, VFF, CBF‐QP, and WGPM, the DAC framework maintains continuous joint velocities while improving trajectory smoothness. Additional validations include moving‐obstacle speed‐sweep simulation, 3D avoidance under irregular composite obstacles, and tests under two payload configurations, Empty and approximately 190 kg Loaded, conducted with identical controller parameters to verify parameter consistency. The peak rate of tracking‐error change is reduced by 89.7%, and the peak joint angular acceleration is reduced by more than 99%, supporting the engineering applicability of the proposed approach under the tested scenarios.