Partially saturated coupling-based control for underactuated overhead cranes with experimental verification

Zhang, Shengzeng; He, Xiongxiong; Chen, Qiang; Zhu, Zhengyang

doi:10.1016/j.mechatronics.2019.102284

Cited by 10 publications

(4 citation statements)

References 33 publications

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“…In [301], the PBC adopts barrier functions on the coupled-dissipation signal, so that the payload position is guaranteed in a predefined scope during the whole transportation. In the mentioned work, a new storage function containing both actuated and underactuated dynamics has been proposed to form a noncollocated control strategy (see also [47] where the barrier function-based antisway control of overhead crane is proposed).…”

Section: Passivity-based Controlmentioning

confidence: 99%

Modeling and control of overhead cranes: A tutorial overview and perspectives

Mojallizadeh

Brogliato

Prieur

2023

Annual Reviews in Control

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Section: Passivity-based Controlmentioning

confidence: 99%

Modeling and control of overhead cranes: A tutorial overview and perspectives

Mojallizadeh

Brogliato

Prieur

2023

Annual Reviews in Control

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“…The direct method is inherently more robust to model uncertainties, making it preferable in real-world applications where such uncertainties are inevitable. The direct method is capable of providing insights into nonlinear dynamics, which are often overlooked by the indirect method [Zhang et al, 2019;Zhou et al, 2014;Nakakuki et al, 2008;Yang & Minimis, 1991].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Efficient Computation of Lyapunov Functions Using Deep Neural Networks for the Assessment of Stability in Controller Design

Uyulan

2023

Preprint

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This paper presents a deep neural network (DNN) based method to estimate approximate Lyapunov functions and their orbital derivatives, which are key to the stability of the system in control theory. Our approach addresses the challenge of the curse of dimensionality in control and optimization problems, demonstrating that the computational effort required grows only polynomically with the state dimension. This is a significant improvement over traditional methods. We emphasize that the calculated functions are approximations of Lyapunov functions and not exact representations. This distinction is important, as validating these approximations in high-dimensional environments is challenging and opens new avenues for future research. Our approach diverges from traditional grid-based approaches and moves away from relying on small-gain theorems and accurate subsystem knowledge. This flexibility is proven in deriving Lyapunov functions for the development of stabilizing feedback rules in nonlinear systems. A crucial aspect of our approach is the use of ReLU activation features in neural networks, which is steady with modern deep getting-to-know traits. We also explore the feasibility of using DNNs to estimate fairly constructive Lyapunov functions, despite the demanding situations posed through uncertainty. Our set of rules' outcomes aren't unique, highlighting the want to set up criteria for figuring out especially useful Lyapunov features. The paper culminates with the aid of emphasizing the capacity of DNNs to approximate compositional Lyapunov capabilities, especially underneath small-gain conditions, to mitigate the curse of dimensionality. Our contributions are manifold, including scalability in dealing with systems of various dimensionality, flexibility in accommodating both low and excessive-dimensional structures, and performance in computing Lyapunov features through deep learning strategies. However, challenges continue to be in the approximation accuracy and the verification of Lyapunov functions in better dimensions.

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“…Due to their numerous benefits, including their large transport capacity and inexpensive prices, cranes are frequently employed in factories and harbors to move heavy freight. According to diverse mechanical architectures, cranes can be categorized into many types, such as overhead cranes [5][6][7], tower cranes [8,9], offshore container cranes [10,11], and so forth. In reality, skilled employees frequently operate cranes manually.…”

Section: Introductionmentioning

confidence: 99%

Active Disturbance Rejection Control Approach for Double Pendulum Cranes with Variable Rope Lengths

Yao¹,

Chen²,

Fan³

2022

JISC

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The overhead crane is a typical underactuated system with complicated dynamics and strong couplings. It is widely employed to transport heavy cargoes in many industrial fields. Due to the complexity of working environments, however, cranes often encounter various unfavorable factors, which may degrade the transportation efficiency. To enhance control performance and anti-disturbance ability, this paper proposes an active disturbance rejection control approach based on differential flatness for double pendulum cranes with variable rope lengths. The proposed approach can position the trolley accurately, regulate rope length, and suppress the swing angles of the payload and the hook simultaneously. During the controller design, flat outputs were constructed based on differential flatness technique to deal with system couplings, and the results prove that double pendulum crane system is differentially flat. After that, model uncertainties and external disturbances were estimated by the designed extended state observer. On this basis, a controller was developed based on the feedback control technique. Finally, a series of simulations were carried out to show that the control scheme is effective and robust.

show abstract

Partially saturated coupling-based control for underactuated overhead cranes with experimental verification

Cited by 10 publications

References 33 publications

Modeling and control of overhead cranes: A tutorial overview and perspectives

Modeling and control of overhead cranes: A tutorial overview and perspectives

Efficient Computation of Lyapunov Functions Using Deep Neural Networks for the Assessment of Stability in Controller Design

Active Disturbance Rejection Control Approach for Double Pendulum Cranes with Variable Rope Lengths

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