A partially saturated adaptive learning controller for overhead cranes with payload hoisting/lowering and unknown parameters

For underactuated overhead cranes with payload hoisting/lowering, a partially saturated adaptive controller subject to unknown or uncertain system parameters is presented in this paper. To decrease the convergence time in the case of the overhead crane parameters already experienced by the system, the learning component is added to the proposed partially saturated adaptive controller. By introducing hyperbolic tangent functions into the control methods, the proposed controllers can guarantee soft trolley start even in the case of high initial velocities of trolley and cable. The convergence and stability performance of the closed-loop system is proven by Lyapunov techniques and LaSalle’s invariance theorem. Simulation results are listed to verify the adaptive performance with reduced actuating forces and strong robustness with respect to different external disturbances of the proposed controllers.