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A model-free control method based on iterative learning law combined with adaptive super-twisting is proposed to realize the synchronous coordination control of multi-lift overhead crane system for the problems of inaccurate modeling, system parameter variation, and disturbance uncertainty that exist in multi-lift overhead crane system. First, a load-coupling model of the double-container overhead crane considering the deformation tangential force in the interlocking mode is established. Second, a time-varying sliding mode surface (TSMC) designed using nonlinear functions effectively improves the convergence speed of the system state. The method of iterative learning control is introduced to compensate the system dynamics to achieve model-free control, and the dynamic iterative learning control (DILC) is designed to improve the convergence speed of the error of the system and the steady-state performance. To suppress uncertainty disturbances and avoid control gain overestimation, an adaptive gain is added to the generalized super-twisting algorithm, which has the advantages of both finite-time convergence and chattering suppression, and improves the robustness and tracking performance of the multi-lift overhead crane system. The stability of the controlled system is analyzed using Lyapunov stability theory. The simulation experiments illustrate the effectiveness of the proposed synchronization control scheme. [ABSTRACT FROM AUTHOR]

In this paper, a model-free robust adaptive control scheme with finite-time convergence based on time-delay control is proposed for anti-sway and positioning control of two-dimensional underactuated overhead cranes. First, the whole overhead cranes system is simplified to an ultra-local model for time delay estimation (TDE). TDE brings a direct and effective model-free property but also an estimation error. Second, a sliding mode disturbance observer is designed to estimate and compensate for the TDE error. Third, sliding mode control (SMC) is used to enhance the robustness of the controller. An adaptive integral sliding surface is then designed to accelerate the sliding surface convergence rate and shorten the convergence time. To further optimize the selection of parameters, the parameter estimation is integrated to enhance the performance of model-free control. In the final analysis of the simulation, data yield that the introduction of parameter estimation increases the control performance by more than 20% on average, and the above facts verify the effectiveness of the scheme. Finally, the stability of the closed-loop control system is analyzed by using Lyapunov stability theory, and the effectiveness and robustness of the control scheme are verified through computer simulation results. [ABSTRACT FROM AUTHOR]

During the synchronous operation of the double-container, the double-lift overhead cranes suffer from the perturbation of system internal parameters, friction and external unknown disturbances. To address the impact of the above-mentioned negative factors, based on the mathematical model of induction motor and the coupled dynamics model of double-container, this paper proposes a synchronous control method combining variable gain extended state observer and nonlinear time-varying sliding mode surface for the synchronous coordination control of double-lift overhead cranes system. The load dynamics model of the double-container interlocking mode is established. Then, a nonlinear time-varying sliding mode surface is designed by means of nonlinear function and dynamic exponential term, which effectively speeds up the convergence of the system state and enhances the robustness of the system. Furthermore, the design adaptive reaching law is used to weaken the unwanted chattering and improve the performance of the controller. At the same time, the designed variable gain extended state observer estimates the aggregated disturbances in the system and then compensates them into the controller. The Lyapunov stability theory is used to prove the stability of the control system. The simulation experiments illustrate the effectiveness of the proposed synchronization control scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a novel model-free controller, considering the overhead crane systems cannot be accurately modeled and affected by external disturbances. The suggested scheme contains a variable exponential decoupled-like sliding mode control (VEDSMC), an adaptive power-reaching law (APRL), and a dynamic learning law-based iterative learning controller (DLLILC); they are combined in a parallel structure to form VEDSMC–APRL–DLLILC. The VEDSMC can effectively enhance the convergence speed of the displacement variables and improve the transient performance of the crane system. The APRL consists of an adaptive switching gain; it can estimate the optimal switching gain according to the unknown dynamics of the crane system and the disturbance, reduce the controller chattering, and guarantee the robustness. The DLLILC term can further improve anti-swing and positioning performance of the overhead crane without accurate information of the crane dynamics model in advance. Moreover, a nonlinear dynamic learning law (DLL) is developed to guarantee both convergence speed and steady-state accuracy in the learning process. Finally, the stability analysis of the designed controller is performed using Lyapunov theory and Barbalat’s lemma, and the simulation results illustrate the effectiveness of the suggested control scheme. [ABSTRACT FROM AUTHOR]

In this paper, a novel time-varying gain extended state observer (ESO)-based moving sliding mode control method is proposed for anti-sway and positioning control of two-dimensional underactuated overhead cranes. The designed moving sliding mode surface can adjust its slope in real time according to the state variable errors; in addition, a dynamic exponential term is added into the moving sliding mode surface so as to drive any initial state variable errors into the sliding surface rapidly, and thereby the robustness of crane systems is improved. Then, a chattering-free reaching law is designed to realize fast convergence of the system state errors, and the input is modelled as a saturated one due to the fact the motor torque is bounded and the control law and adaptive updating law of switching gain are derived in the sense of Lyapunov function, so the stability can be guaranteed even under the input saturation. Moreover, to suppress the matched and unmatched disturbance occurring in crane dynamic systems, a time-varying gain ESO is constructed to estimate the lumped disturbance, then the estimated value is used for feedforward compensation to establish the controller. Finally, the simulation results confirm the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]