Volume 11, Issue 4 (1-2022)                   JEM 2022, 11(4): 2-9 | Back to browse issues page

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Faraji A, Ahmadi M. Energy Optimization of Under-actuated Crane Model for Time-Variant Load Transferring Using Optimized Adaptive Combined Hierarchical Sliding Mode Controller. JEM. 2022; 11 (4) :2-9
URL: http://energy.kashanu.ac.ir/article-1-1487-en.html
Department of Electrical and Computer Engineering, University of Kashan
Abstract:   (784 Views)
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 unknown parameters or un-modeled dynamics in 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 behavior in the presence of additive un-modeled dynamics of system and multiplicative friction forces. This under-actuated crane has two sub-systems: trolley and payload. Therefore, it can be controlled by a single input signal with combined hierarchical sliding mode controller (CHSMC) using a 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 an 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 the input signal, the 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.
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Type of Study: Research | Subject: Electrical Engineering
Received: 2020/06/23 | Revised: 2022/04/10 | Accepted: 2021/12/20 | Published: 2021/12/31

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