An Online Adaptive Policy Iteration-Based Reinforcement Learning for a Class of a Nonlinear 3D Overhead Crane

Alyazidi, Nezar M.; Hassanine, Abdalrahman M.; Mahmoud, Magdi S.

doi:10.1016/j.amc.2022.127810

Cited by 7 publications

(5 citation statements)

References 40 publications

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“…This paper also introduced a TDE technique to solve the timedelay issue and estimate the lumped uncertainty. There is a different approach to solving problems involving the threedimensional overhead crane which is to apply the reinforcement learning principle such as [17]. With the assumption that the rope length is fixed and the small swing angles, Nezar M. Alyazidi et al linearized the 5-Dof model of the overhead crane.…”

Section: B Related Papersmentioning

confidence: 99%

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A Novel Separation Principle-Based Stabilization for 6-Dof Overhead Crane Under Fault Injection Data Onslaught

Kim,

Manh Do,

Hoang

et al. 2023

IEEE Access

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This paper proposes a novel scheme for stabilization of the 6-DoF Overhead Crane to control the movements of this system without the state measurements through the separation principle. The movements of the system studied in this paper are described more closely than the existing crane model when considering six behaviors simultaneously: the movement of the trolley and the bridge, two swing angles along the x-axis and y-axis of the payload, the hoisting drum rotation, and axial payload oscillation. The mission of the proposal is to make all the above behaviors of the 6-DOF crane system to be stable accurately enough at the desired positions and minimize both the horizontal swings of the payload and the axial oscillation of the cable by utilizing information about the position of the trolley, the bridge, and the length of cable. To guarantee these objectives, a cooperation regime controller comprising a new asymptotic state observer which is elaborately constructed via a neural network, and the output feedback backstepping hierarchical sliding mode vehicle is integrated into the controller for improving the adaption of the closed-loop system. This cooperation controller is also evolved to prop up the fault injection data onslaught by eliminating all the information about the input system into the procedure. Furthermore, to enhance the flexibility of the closed scheme, an updating law for the observer's parameter is developed based on the data-driven principle. All of them are developed and designed not only to handle these above tasks but also to avoid the undesired finiteescape-time (FET) phenomenon. All the results are proven systematically by three theorems and validated their performance through two scenarios and simulated by Matlab/Simulink platform.INDEX TERMS Overhead Crane, adaptive state observer, nonlinear control, finite-escape-time phenomenon, separation principle-based output feedback control

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Section: B Related Papersmentioning

confidence: 99%

“…Therefore, the method studied here is obviously more general in these papers [5; 6; 29]. In addition, by using only y, the proposed observer is more simple than the observer studied in paper [17] which is utilized both input vector u and y for estimating.…”

Section: Notation and Problem Statement A Notationmentioning

confidence: 99%

A Novel Separation Principle-Based Stabilization for 6-Dof Overhead Crane Under Fault Injection Data Onslaught

Kim,

Manh Do,

Hoang

et al. 2023

IEEE Access

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“…In many cases, when the crane is in motion, the rope length does not change or change at a slow rate; hence, some works [1], [3]- [7], [9] assume fixed rope length; as a result, the number of states is reduced to two states only. In addition to fixed rope length, if the swing angle is assumed to be small as well, the system model can be linearized [1] [2]. On the other hand, more accurate models can be obtained by incorporating more dynamics into the model, like friction between running parts [8] and external disturbances [4].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Trajectory Tracking of 3D Overhead Crane Using Adaptive Sliding-Mode Control and Particle Swarm Optimization

Alyazidi,

Hassanine,

Mahmoud

et al. 2024

Journal of Robotics and Control

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Cranes hold a prominent position as one of the most extensively employed systems across global industries. Given their critical role in various sectors, a comprehensive examination was necessary to enhance their operational efficiency, performance, and facilitate the control of transporting loads. Furthermore, due to the complexities involved in disassembling and reinstalling cranes, as well as the challenges associated with precisely determining system parameters, it became essential to implement adaptive control methods capable of efficiently managing the system with minimal resource requirements. This work proposes a trajectory tracking control using adaptive sliding-mode control (SMC) with particle swarm optimization (PSO) to control the position and rope length of a 3D overhead crane system with unknown parameters. The PSO is mainly used to identify the model and estimate the uncertain parameters. Then, sliding-mode control is adapted using the PSO algorithm to minimize the tracking error and ensure robustness against model uncertainties. A model of the systems is derived assuming changing rope length. The model is nonlinear of second order with five states, three actuated states: position x and y, and rope length l, and two unactuated states, which are the rope angles θx and θy. The system has uncertain parameters, which are the system’s masses Mx, My and Mz, and viscous damping coefficients Dx, Dy and Dy. A simulation study is established to illustrate the influence and robustness of the developed controller and it can enhance the tracking trajectory under different scenarios to test the scheme.

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“…Later, with the deepening of the research, scholars began to focus on applying classical control theory to the anti-swing control of bridge crane, which mainly includes fuzzy PID control [3][4][5][6] and sliding mode control [7,8]. At the same time, many scholars have applied computer vision to the 3D map construction of cranes [9], swing angle measurement [10], and position detection [11,12], etc. From the perspective of most scholars' research, most of them focus on anti-swing and swing angle detection.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Positioning System Design for Bridge Crane in Hydropower Plant

Chen,

Xu,

Zou

et al. 2024

J. Phys.: Conf. Ser.

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The compound nested control method of PID control and fuzzy control is used to control the walking position and speed of the bridge crane, and image processing technology is applied to detect the critical position during rotor hoisting. The system simulation is carried out using Matlab/Simulink, and the feasibility of the control method is verified based on the overshoot, response time, and stability indicators with the unit step signal as the excitation. The results show that the time required to reach steady state through fixed parameter PID simulation analysis is 0.237 s, and the overshoot is 3.3%. The time required for steady state using fuzzy PID control is 0.12s, the response speed is better than that of fixed parameter PID control, and the system has no overshoot. The image of the stator captured by the camera is processed by OpenCV. Firstly, the image is grayed and the Gaussian filtering is used to remove the image noise. Then, the stator edge contour is detected based on the Canny edge detection algorithm. The test shows that OpenCV can accurately identify the edge of the stator and extract the central coordinates of the stator image, which assists the bridge crane in accurate positioning.

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An Online Adaptive Policy Iteration-Based Reinforcement Learning for a Class of a Nonlinear 3D Overhead Crane

Cited by 7 publications

References 40 publications

A Novel Separation Principle-Based Stabilization for 6-Dof Overhead Crane Under Fault Injection Data Onslaught

A Novel Separation Principle-Based Stabilization for 6-Dof Overhead Crane Under Fault Injection Data Onslaught

Enhanced Trajectory Tracking of 3D Overhead Crane Using Adaptive Sliding-Mode Control and Particle Swarm Optimization

Intelligent Positioning System Design for Bridge Crane in Hydropower Plant

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