Experimental Modelling and Control of a Tower Crane

Čápková, Romana; Kozáková, Alena

doi:10.1109/pc.2019.8815327

Cited by 6 publications

(4 citation statements)

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“…From the second equation of system ( 14), the equation of the angular coordinate of the drum of the trolley movement mechanism is found using the relation (15). As a result, the following is obtained:…”

Section: Methodsmentioning

confidence: 99%

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Minimisation of the driving torque of the derricking mechanism of a tower crane during steady load hoisting

Ловейкін¹,

Romasevych²,

Shymko³

et al. 2022

Tehnìka ta energetika

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Efficient use of a tower crane often requires combining various operations, such as hoisting load and derricking. In the case when the load is hoisted at a steady speed, the problem of optimal control of the trolley movement mechanism arises, which goes beyond engineering calculations and is a scientific and applied problem. Its relevance is related to improving the controllability of crane mechanisms, increasing the capacity and reliability of the crane, and improving the energy efficiency of its drive mechanisms. These indicators are related to the choice of optimisation criteria. Thus, the purpose of the study is to optimise the starting mode of the derricking mechanism according to the criterion of the RMS value of the driving moment during a steady load hoisting. To achieve this goal, the following methods were applied: dynamics of machines and mechanisms, mathematical modelling, integral and differential calculus, and the ME-D-PSO method. For the boundary conditions, parameters are selected that eliminate load oscillations on the flexible suspension when the derricking mechanism slews to the steady-state driving mode. Based on the results of optimisation of the joint movement of mechanisms for derricking and load hoisting, graphical dependences of kinematic, dynamic, and energy characteristics of the start-up transition process are constructed and their analysis is carried out. The obtained dependences reveal the conditions for eliminating load oscillations on a flexible suspension during steady-state movement and reducing dynamic loads and energy losses during the start-up of the derricking mechanism. To implement the optimal start mode of the derricking mechanism during steady load hoisting, it is recommended to use optimal control of the drive mechanisms. The results obtained should be applied to the development of new and modernisation of existing motion control systems for tower crane mechanisms

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Section: Methodsmentioning

confidence: 99%

“…Slovak researchers conducted experiments using a three-dimensional laboratory model suitable for research in the field of mechanism control. Their study [15] is devoted to the identification of a dynamic model and the development of a trolley position controller along the boom of a tower crane, and damping load oscillations.…”

Section: Introductionmentioning

confidence: 99%

Minimisation of the driving torque of the derricking mechanism of a tower crane during steady load hoisting

Ловейкін¹,

Romasevych²,

Shymko³

et al. 2022

Tehnìka ta energetika

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“…Three motors are driven by electronic drive units and optical sensors to measure position and speed [25]. There are many ways to obtain a mathematical model of a tower crane, including the use of finite element analysis and Lagrangian methods [1], [26]. In order to obtain an accurate dynamic model representing the tower crane systems, several parameters have to be included when deriving the mathematical model.…”

Section: Mathematical Model Of Tower Cranementioning

confidence: 99%

“…Crane control systems are a vital area of scientific research and need more studies and research works, especially with the development of computer and communication technologies. They have worked to develop control systems for cranes that have the ability to move the load as quickly as possible and keeping the swing angle as small as possible in the final position [1]. On the other hand, the crane needs a skilled operator who has the ability to handle the control system easily in order to move the payload and prevent it from swinging.…”

Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy-based anti-swing control of automatic tower crane

Al-Tuhaifi,

Al-Aubidy

2023

TELKOMNIKA

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Controlling the position of the final load and the anti-swing control of the loads during the operation of the tower crane are challenging tasks. These are the most important control issues for safe operation, which are difficult to achieve easily with conventional control systems. Hence, the need to integrate the concepts of soft-computing into the tower crane control system. The aim of this research work is to design an adaptive-network-based fuzzy inference system (ANFIS) controller to move the payload to the final position with the lowest possible swing angle. To evaluate the ability of the proposed controller to meet the control requirements, its performance was compared to three other controllers: a conventional proportional derivative (PD) controller, a fuzzy-tuned PD controller and a fuzzy controller. MATLAB-based computer simulations of the crane and controllers were carried out to verify and compare the performance of the proposed controllers. The obtained results show the effectiveness of the ANFIS-based controller in adjusting the load position while keeping the load fluctuations small at the final position. The load oscillation angle is about ±2.28° with the ANFIS controller while it is about ±10° when using the PD controller. In addition, only one ANFIS controller is used for both load position and swing angle control.

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Closed‐loop stability analysis of deep reinforcement learning controlled systems with experimental validation

Mohiuddin,

Boiko,

Azzam

et al. 2024

IET Control Theory & Appl

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Trained deep reinforcement learning (DRL) based controllers can effectively control dynamic systems where classical controllers can be ineffective and difficult to tune. However, the lack of closed‐loop stability guarantees of systems controlled by trained DRL agents hinders their adoption in practical applications. This research study investigates the closed‐loop stability of dynamic systems controlled by trained DRL agents using Lyapunov analysis based on a linear‐quadratic polynomial approximation of the trained agent. In addition, this work develops an understanding of the system's stability margin to determine operational boundaries and critical thresholds of the system's physical parameters for effective operation. The proposed analysis is verified on a DRL‐controlled system for several simulated and experimental scenarios. The DRL agent is trained using a detailed dynamic model of a non‐linear system and then tested on the corresponding real‐world hardware platform without any fine‐tuning. Experiments are conducted on a wide range of system states and physical parameters and the results have confirmed the validity of the proposed stability analysis (https://youtu.be/QlpeD5sTlPU).

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Experimental Modelling and Control of a Tower Crane

Cited by 6 publications

References 0 publications

Minimisation of the driving torque of the derricking mechanism of a tower crane during steady load hoisting

Minimisation of the driving torque of the derricking mechanism of a tower crane during steady load hoisting

Neuro-fuzzy-based anti-swing control of automatic tower crane

Closed‐loop stability analysis of deep reinforcement learning controlled systems with experimental validation

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