Control Of Overhead Crane Research Articles

Data-driven discrete learning sliding mode control for overhead cranes suffering from disturbances

Data-driven discrete learning sliding mode control for overhead cranes suffering from disturbances

Consensus Tracking and Vibration Control for Multiple Overhead Cranes With Flexible Cables Under Time-Varying Actuator Faults

Consensus Tracking and Vibration Control for Multiple Overhead Cranes With Flexible Cables Under Time-Varying Actuator Faults

Discrete adaptive sliding mode controller design for overhead cranes considering measurement noise and external disturbances

AbstractResearch on the motion control of overhead cranes, constrained by underactuated characteristics, helps improve the efficiency of payload transportation. Most studies require all system state variables (trolley displacement, payload swing angle, and their velocities). In practice, sensors measure and transmit these variables, but noise affects their accuracy, reducing control performance. Additionally, uncertainties in crane parameters, unmodeled friction, and unknown disturbances threaten the system's stability. Traditional methods struggle to address these issues effectively. To address these challenges, this article proposes an adaptive discrete sliding mode control (DSMC) method with a Kalman filter. By extending the state system and considering disturbances as new variables, the Kalman filter effectively eliminates signal noise, accurately estimates disturbances, and estimates system states simultaneously. The proposed method incorporates disturbance compensators into the adaptive DSMC, utilizing exponential terms to suppress oscillations caused by excessively high or low control gains, thus increasing control speed. Experimental comparisons demonstrate the superiority and robustness of the proposed control method under various disturbance conditions.

Adaptive coupled double-pendulum overhead crane control strategy with enhanced attitude suppression under initial input constraints

Adaptive coupled double-pendulum overhead crane control strategy with enhanced attitude suppression under initial input constraints

Neural network based adaptive robust synchronous control of double-lift overhead crane considering input saturation

This paper proposes a novel model-free controller, considering the double-lift overhead crane system cannot be accurately modeled and affected by external disturbances. Firstly, to improve the convergence speed of the system states, a time-varying sliding mode surface using a sigmoid-like function is proposed. Then, an adjustable weights radial basis function neural network (AWRBFNN) is introduced to estimate and compensate for the unknown dynamics of the system. This AWRBFNN introduces direct and effective model-free properties, but also brings approximation errors. To estimate the uncertain disturbances and the errors of the neural network, an adaptive uncertainty and disturbance estimator (AUDE) is designed. Furthermore, a fast hyperbolic power reaching law is developed to enhance the robustness while suppressing chattering. Considering that the driving capacity of the actual double-lift overhead crane system and the driving motor torque are limited, an innovative continuous auxiliary system is designed to mitigate the effects of the input saturation while reducing overshoot from synchronization errors. Finally, the stability of the control system is analyzed using the Lyapunov stability theory, and the effectiveness and robustness of the control scheme are verified by simulation.

ESO-based and FTDO-based anti-swing control for overhead cranes with external disturbance

Overhead cranes play a pivotal role in various industrial applications. This paper presents two methodologies for suppressing the payload’s swing and completing the accurate positioning. The original crane model is initially transformed into a chained form. Two nonlinear controllers are devised to regulate the trolley’s position and achieve the payload’s anti-swing control. The first approach utilizes backstepping, extended state observer, and filter technique to effectively estimate system state and disturbance and make the closed-loop system uniformly ultimately bounded. In contrast, the second method involves sliding mode control and finite-time disturbance observer to ensure the finite-time convergence of the resulting closed-loop system and the accurate disturbance estimation. Finally, simulation results are provided to validate the effectiveness of the proposed methods.

Finite-time model-free robust synchronous control of multi-lift overhead cranes based on iterative learning

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.

Adaptive PID control of overhead cranes based on ISOA–NN

Adaptive PID control of overhead cranes based on ISOA–NN

Robust Trajectory Tracking Control of a Differentially Flat Overhead Crane Using Sliding Mode

The control of overhead cranes is a benchmark problem, since it is an underactuated mechanism and its mathematical model is nonlinear. During operation the mass of the load is unknown, representing an uncertainty in the inertial parameters, which requires robustness of the controlled system. Our paper proposes a novel robust control method, that combines the differentially flat property of the dynamics with the robustness of the sliding mode control. The sliding surface is constructed to ensure the tracking of the configuration variables whose accelerations is calculated using the flatness property of the dynamic model. This formulation also allows achieving the matching conditions of the parameter uncertainties. Considering a simplified overhead crane model where the load motion is restricted in a vertical plane, two sliding surfaces are defined for the rope angle and rope length, since the cart position can be calculated from the previous two. The suggested control method is successfully validated in simulations as well as using a reduced-size overhead crane. For the real crane, the rope angle was estimated by utilizing the dynamical model, which uses the estimated cart acceleration.

Adaptive Tracking Control for Underactuated Double Pendulum Overhead Cranes With Variable Cable Length.

Although the literature on control of overhead crane systems is extensive and relatively mature, there is still a need to develop strategies that can simultaneously handle factors such as the double pendulum effect, variable cable length, input saturation, input dead zones, and external disturbances. This article is concerned with adaptive tracking control for underactuated overhead cranes in the presence of the above-mentioned challenging effects. The proposed controller is composed of the following two components. First, a tracking signal vector that effectively reduces system swing magnitudes is constructed to improve the transient performance and guarantee smooth operation of the system. Second, an adaptive law is designed to estimate and compensate for the overall effects of the friction, the external disturbances, and certain nonlinearities. The system stability has been proved rigorously via the Lyapunov method and Barbalat's lemma. Extensions to the cases with input saturation and dead zones have also been discussed. Extensive numerical simulations have been conducted to verify the performance and robustness of the proposed controller, in comparison to some existing methods.

Adaptive Anti-Swing Control for 7-DOF Overhead Crane With Double Spherical Pendulum and Varying Cable Length

Adaptive Anti-Swing Control for 7-DOF Overhead Crane With Double Spherical Pendulum and Varying Cable Length

A High-Order Fully Actuated System Approach to Control of Overhead Cranes

A High-Order Fully Actuated System Approach to Control of Overhead Cranes

Output Feedback Control of Overhead Cranes Based on Disturbance Compensation

In practice, various factors such as friction, unmodeled dynamics and uncertain external disturbances often affect overhead cranes. The existing crane control methods often neglect these factors or address these factors by robust techniques. Moreover, most of them do not take input saturation into account and require full-state feedback. In this paper, taking the practical issues of uncertain disturbances, input saturation and output feedback into account, we propose an input-saturated output feedback control strategy for the underactuated two-dimensional (2-D) overhead crane systems with uncertain disturbances. Specifically, we first design a disturbance observer that can accurately estimate the external disturbance. Then, the virtual horizontal location signal is introduced and the new energy storage function is constructed. A novel composite control method for overhead crane systems is proposed based on the developed disturbance observer and the new energy storage function. The stability and convergence analysis are given through Lyapunov techniques and LaSalle’s invariance theorem. In order to verify the performance of the proposed controller, we perform a series of simulation tests and compare the proposed method with some existing control methods.

Disturbance-observer-based discrete-time sliding mode predictive control for overhead cranes with matched and unmatched disturbances

In actual working environments, underactuated overhead cranes are susceptible to the adverse effects of the mechanical friction, uncertain parameters, and other disturbances. In particular, if the payload is disturbed (i.e. the unmatched disturbance), unpredictable swings would occur. Therefore, a discrete time sliding mode predictive control for overhead cranes based on a disturbance observer is proposed. Specifically, to eliminate uncertain disturbances, we propose a disturbance observer to estimate and compensate the disturbance to the input channel. Then, a new discrete sliding mode control law is obtained by adding the disturbance error estimator to the discrete sliding mode surface. In order to reduce the chattering inherent in the sliding mode, we integrate the discrete sliding mode law into the model predictive control, and use the rolling optimization and feedback correction of the predictive control, and finally output the optimal control law to act on the system. This control method can not only further improve the control accuracy, but also has sufficient robustness. Simulation and robustness experiments under various conditions verify the effectiveness of the proposed controller.

Deep reinforcement learning with domain randomization for overhead crane control with payload mass variations

Overhead cranes, as an important tool for loading and transporting, play an important role in modern industry. A key challenge in overhead crane control is payload mass variation: a policy learned to solve the overhead crane control in the fixed payload scenario often fails to solve the control task in the payload variation scenario. Therefore, from a practical perspective, this paper designs a novel deep reinforcement learning (DRL) control algorithm, domain randomization memory-augmented Beta proximal policy optimization (DR-MABPPO), which leverages the memory-augmented policy and incorporates the domain randomization (DR) training strategy to address the control problem of the overhead crane with payload masses variations. With the help of the DR training strategy and the memory-augmented policy, DR-MABPPO can learn a universal policy that is robust to the wide range of payload mass variations. As far as we know, this is the first time that the DRL technique is applied to solve the overhead crane control with payload mass variations. Simulation studies are conducted to demonstrate the effectiveness of the proposed method in the presence of payload mass variations, exhibiting satisfactory control performance when compared to PID and LQR.

Barrier function-based adaptive antisway control for underactuated overhead cranes

Barrier function-based adaptive antisway control for underactuated overhead cranes

Artificial-neural-network-based optimal Smoother design for oscillation suppression control of underactuated overhead cranes with distributed mass beams

As a kind of convenient and practical oscillation suppression method, the open-loop controllers are widely utilized in the control of actual cranes, yet most of them need to be designed for each mode frequency when targeting multimodal systems, which will degrade the robustness of the controller. Moreover, most open-loop controllers require linearization of the model in the design process, which will inevitably reduce the performance of the controller in terms of oscillation suppression. To solve the above prevalent problems, an optimal command-smoothing method is proposed in this paper. Specifically, the dynamic analysis of a 2-D overhead crane with distributed-mass beams (DMB) is first performed. Then, the particle swarm optimization algorithm (PSO) is used to solve the optimal values of the parameters of the Smoother under different system parameters, and a data set for ANN training is generated, together with a series of indices to verify the effectiveness of the designed ANN. Finally, in combination with the PD controller, the proposed controller is experimentally proven to accomplish the positioning requirements while improving the oscillation suppression efficiency by an average of 29% compared to the basic Smoother.

Input-limited optimal control for overhead cranes with payload hoisting/lowering and double pendulum effects

Input-limited optimal control for overhead cranes with payload hoisting/lowering and double pendulum effects

Implementation of a neural network in overhead crane control

This work is aimed at proecting an automated control system for an overhead crane based on a neural network, which will replace the operator, but it is possible to transfer this control back to the operator. The paper considers an approach to the development of the necessary software for the correct operation of the crane, the choice of equipment suitable for the task. The final part describes the program blocks necessary for operation, as well as locks.

Position-Commanding Anti-Sway Controller for 2-D Overhead Cranes Under Velocity and Acceleration Constraints

This paper proposes an anti-sway controller for two-dimensional overhead cranes. The controller is of the position-commanding type, i.e., it sends position commands to the position-controlled trolley, and it uses the sensor information of the payload sway angle. The position commands are determined so that the trolley tracks the desired position signal sent from an upper-level controller and also so that the payload sway is damped. The sway damping is realized by a leaky integral controller, which employs a high-pass filtered time integral of the sway angle. In addition, arbitrary limits can be imposed on the first and second derivatives of the position command, and thus it is applicable to cases where the desired position signal from the upper-level controller is nonsmooth or discontinuous. Due to these features, the controller is expected to be useful in both human-operated and autonomous systems. The controller was validated with a laboratory setup. This paper also presents an algorithm that can be attached to the proposed controller to prevent the overshoot, which can take place when the desired position signal is discontinuous.