Energy Optimization of Under-actuated Crane model for Time-Variant Load Transferring using Optimized Adaptive Combined Hierarchical Sliding Mode Controller

This paper designs an Optimized Adaptive Combined Hierarchical Sliding Mode Controller (OACHSMC) for a time-varying crane model in presence of uncertainties. Uncertainties have always been one of the most important challenges in designing control systems, which include the unknown parameters or un-modeled dynamics in the systems. Sliding mode controller (SMC) is able to compensate the system in the presence of uncertainties due to un-modeled dynamics and is used for robust stability and performance behaviors in the presence of additive un-modeled dynamics of system and multiplicative friction forces. This under-actuated crane has two subsystems: trolley and payload, so it can be controlled by a single input signal with combined hierarchical sliding mode controller (CHSMC) using two layer sliding manifold accurately. Payload mass and cable length are time variant variables through load transferring. Due to the Time-varying models and the inefficiency of most controllers, the use of adaptive controller can help improve system performance. This controller is adapted by considering a time-varying coefficient of the second layer sliding manifold. For energy saving of input signal, parameter of the first layer sliding manifold of ACHSMC is optimized by two intelligent strategies: genetic algorithm (GA) and particle swarm optimization (PSO) method. The simulation results show robust stability and performance of the proposed optimized controller.