Adaptive backstepping-based swing suppression control for tower crane with obstacle avoidance

Obstacle avoidance in tower cranes is a significant challenge due to underactuation, strong nonlinearities, and uncertain dynamics. To address this challenge, an adaptive backstepping-based obstacle-avoidance control method is proposed for tower crane systems. The proposed control method simultaneously addresses obstacle avoidance, positioning overshoot constraint, payload swing suppression, and uncertainty compensation. Specifically, a locally activated obstacle-avoidance barrier term that accounts for the payload swing envelope is first incorporated into the controller to achieve closed-loop obstacle avoidance. Next, based on the activation state of the obstacle-avoidance term, a dynamic scheduling mechanism coordinates obstacle avoidance with positioning overshoot constraints. Then, by exploiting the inherent dynamic coupling of the tower crane, the payload swing angular velocities are fed back into the actuated channels to construct a damping term for swing suppression. Furthermore, a generalized fuzzy hyperbolic model is introduced to compensate for lumped uncertainties, including uncertainties in payload mass, rope length, and friction. The stability and asymptotic convergence of the proposed control system are theoretically proven using Lyapunov stability theory. Finally, experimental results on a tower crane prototype demonstrate the superior performance of the proposed method over comparative approaches in positioning, payload swing suppression, and obstacle avoidance.