A Frequency-Limited Adaptive Controller for Underwater Vehicle-Manipulator Systems Under Large Wave Disturbances

Lu, Wenjie; Liu, Dikai

doi:10.1109/wcica.2018.8630712

Cited by 10 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Disturbance rejection control [50,1,34,14] often assumes disturbances bounded and relatively smaller than the control saturation [19]. One popular improvement to these controllers is to add a feedforward compensation based on some disturbance estimation techniques [58].…”

Section: Disturbance Rejectionmentioning

confidence: 99%

“…While a control strategy should be able to adapt to various environments (described by a set of multiple POMDPs) online. This philosophy has found its roots in existing literature of control designs, such as robust control [50], adaptive control [1,33,34], sliding mode control [14], H-infinity control [13]. Understanding the interplay between the environments and the controlled systems is essential for theoretical analysis and further improvement in control design.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A2: Extracting cyclic switchings from DOB-nets for rejecting excessive disturbances

Liu

2020

Neurocomputing

Self Cite

View full text Add to dashboard Cite

Section: Disturbance Rejectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A2: Extracting cyclic switchings from DOB-nets for rejecting excessive disturbances

Liu

2020

Neurocomputing

Self Cite

View full text Add to dashboard Cite

“…Defining E as the errors between the current robot states and the desired path toward the target pile, the goal of the SPIR motion controller is to generate the control signals to minimise E under the large disturbance of environments. Several control algorithms such as anti-windup PID controller, reinforcement learning [8], and adaptive controller [9] were studied and successfully designed for this objective; those results are specifically described in Section IV.…”

Section: A Localisation and Navigation In Open Watermentioning

confidence: 99%

The SPIR: An Autonomous Underwater Robot for Bridge Pile Cleaning and Condition Assessment

Leighton

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

View full text Add to dashboard Cite

The SPIR, Submersible Pylon Inspection Robot, is developed to provide an innovative and practical solution to keep workers safe during maintenance of underwater structures in shallow waters, which involves working in dangerous water currents, and highpressure water-jet cleaning. More advanced than workclass Remotely Operated Vehicles technology, the SPIR is automated and required minimum involvement of humans into the working process, thus effectively lowered the learning curve required to conduct work. To make SPIR operate effectively in poor visibility and highly disturbed environments, the multiple new technologies are developed and implemented into the system, including SBL-SONARbased navigation, 6-DOF stabilisation, and vision-based 3D mapping. Extensive testing and field trials in various bridges are conducted to verify the robotic system. The results demonstrate the suitability of the SPIR in substituting humans for underwater hazardous tasks such as autonomous cleaning and inspection of bridge and wharf piles

show abstract

“…In the early development of disturbance rejection control, feedback control strategies are used to suppress the unknown disturbances. Examples of feedback controllers include robust control [9], adaptive control [10], [11], optimal control [12], sliding mode control [13], etc. Then, disturbance estimation and attenuation methods through adding a feedforward compensation term [8], [14] have been proposed and practiced, such as DOB [7] and Extended State Observer (ESO) [15].…”

Section: Related Work a Feedback And Predictive Controlmentioning

confidence: 99%

DOB-Net: Actively Rejecting Unknown Excessive Time-Varying Disturbances

Wang

Yan

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

This paper presents an observer-integrated Reinforcement Learning (RL) approach, called Disturbance OBserver Network (DOB-Net), for robots operating in environments where disturbances are unknown and time-varying, and may frequently exceed robot control capabilities. The DOB-Net integrates a disturbance dynamics observer network and a controller network. Originated from classical DOB mechanisms, the observer is built and enhanced via Recurrent Neural Networks (RNNs), encoding estimation of past values and prediction of future values of unknown disturbances in RNN hidden state. Such encoding allows the controller generate optimal control signals to actively reject disturbances, under the constraints of robot control capabilities. The observer and the controller are jointly learned within policy optimization by advantage actor critic. Numerical simulations on position regulation tasks have demonstrated that the proposed DOB-Net significantly outperforms a canonical feedback controller and classical RL algorithms.

show abstract

A Frequency-Limited Adaptive Controller for Underwater Vehicle-Manipulator Systems Under Large Wave Disturbances

Cited by 10 publications

References 28 publications

A2: Extracting cyclic switchings from DOB-nets for rejecting excessive disturbances

A2: Extracting cyclic switchings from DOB-nets for rejecting excessive disturbances

The SPIR: An Autonomous Underwater Robot for Bridge Pile Cleaning and Condition Assessment

DOB-Net: Actively Rejecting Unknown Excessive Time-Varying Disturbances

Contact Info

Product

Resources

About