Decentralized Tracking Control for Modular Reconfigurable Robots Using Data-Based Concurrent Learning

2021 11th International Conference on Information Science and Technology (ICIST) ◽

10.1109/icist52614.2021.9440625 ◽

2021 ◽

Author(s):

Qiuye Wu ◽

Qiliang Luo ◽

Weichen Luo ◽

Derong Liu ◽

Bo Zhao

Keyword(s):

Tracking Control ◽

Concurrent Learning ◽

Reconfigurable Robots ◽

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A Coordinated Tracking Control of Multi-Agent Systems Using Data-Driven Methods

2018 37th Chinese Control Conference (CCC) ◽

10.23919/chicc.2018.8483722 ◽

2018 ◽

Author(s):

You Wu ◽

Guo-Ping Liu

Keyword(s):

Tracking Control ◽

Data Driven ◽

Multi Agent Systems ◽

Agent Systems ◽

Multi Agent ◽

Coordinated Tracking

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Optimal tracking control for completely unknown nonlinear discrete-time Markov jump systems using data-based reinforcement learning method

Neurocomputing ◽

10.1016/j.neucom.2016.02.029 ◽

2016 ◽

Vol 194 ◽

pp. 176-182 ◽

Author(s):

He Jiang ◽

Huaguang Zhang ◽

Yanhong Luo ◽

Junyi Wang

Keyword(s):

Reinforcement Learning ◽

Discrete Time ◽

Tracking Control ◽

Learning Method ◽

Markov Jump ◽

Markov Jump Systems ◽

Optimal Tracking ◽

Optimal Tracking Control ◽

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Global Exponential Tracking Control for an Autonomous Surface Vessel: An Integral Concurrent Learning Approach

IEEE Journal of Oceanic Engineering ◽

10.1109/joe.2018.2880622 ◽

2020 ◽

Vol 45 (2) ◽

pp. 362-370 ◽

Author(s):

Zachary Ian Bell ◽

Jason Nezvadovitz ◽

Anup Parikh ◽

Eric M. Schwartz ◽

Warren E. Dixon

Keyword(s):

Tracking Control ◽

Learning Approach ◽

Concurrent Learning ◽

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Concurrent Learning-Based Global Exponential Tracking Control of Uncertain Switched Systems With Mode-Dependent Average Dwell Time

IEEE Access ◽

10.1109/access.2018.2854720 ◽

2018 ◽

Vol 6 ◽

pp. 39086-39095 ◽

Author(s):

Caiyun Wu ◽

Xiaoping Huang ◽

Ben Niu ◽

Xue-Jun Xie

Keyword(s):

Switched Systems ◽

Tracking Control ◽

Average Dwell Time ◽

Concurrent Learning

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Approximate Optimal Sliding Mode Tracking Control for Modular Reconfigurable Robots Based on Critic-only Structure

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2021.04.222 ◽

2020 ◽

Vol 53 (5) ◽

pp. 554-559

Author(s):

Hongbing Xia ◽

Fuquan Xia ◽

Bo Zhao ◽

Yinghui Huang

Keyword(s):

Tracking Control ◽

Sliding Mode ◽

Reconfigurable Robots ◽

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Simultaneous Trajectory Tracking Control and Online Mass Estimation for a Regolith Excavating Robot via Integral Concurrent Learning

AIAA Scitech 2021 Forum ◽

10.2514/6.2021-1131 ◽

2021 ◽

Author(s):

Max L. Greene ◽

Michael DuPuis ◽

Joe Cloud ◽

Warren E. Dixon

Keyword(s):

Trajectory Tracking ◽

Tracking Control ◽

Trajectory Tracking Control ◽

Mass Estimation ◽

Concurrent Learning

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Critic-Identifier Structure ADP Based Near-optimal Decentralized Tracking Control of Modular and Reconfigurable Robots

2019 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC) ◽

10.1109/yac.2019.8787687 ◽

2019 ◽

Author(s):

Hongbing Xia ◽

Daiwei Xue ◽

Yanchun Wang ◽

Aimin Qiao ◽

Bo Zhao

Keyword(s):

Tracking Control ◽

Reconfigurable Robots

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Event-Triggered Decentralized Tracking Control of Modular Reconfigurable Robots Through Adaptive Dynamic Programming

IEEE Transactions on Industrial Electronics ◽

10.1109/tie.2019.2914571 ◽

2020 ◽

Vol 67 (4) ◽

pp. 3054-3064 ◽

Author(s):

Bo Zhao ◽

Derong Liu

Keyword(s):

Dynamic Programming ◽

Tracking Control ◽

Adaptive Dynamic Programming ◽

Adaptive Dynamic ◽

Reconfigurable Robots ◽

Event Triggered

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Concurrent Learning Based Tracking Control of Nonlinear Systems using Gaussian Process

10.1109/aim46487.2021.9517642 ◽

2021 ◽

Author(s):

Vedant Bhandari ◽

Erkan Kayacan

Keyword(s):

Nonlinear Systems ◽

Gaussian Process ◽

Tracking Control ◽

Concurrent Learning

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Decentralized Trajectory Tracking Control for Modular and Reconfigurable Robots With Torque Sensor: Adaptive Terminal Sliding Control-Based Approach

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4042550 ◽

2019 ◽

Vol 141 (6) ◽

Author(s):

Yan Li ◽

Zengpeng Lu ◽

Fan Zhou ◽

Bo Dong ◽

Keping Liu ◽

...

Keyword(s):

Decentralized Control ◽

Trajectory Tracking ◽

Tracking Control ◽

Sliding Mode ◽

Rbf Neural Network ◽

Torque Sensor ◽

Trajectory Tracking Control ◽

Terminal Sliding Mode ◽

Reconfigurable Robots ◽

The main technical challenge in decentralized control of modular and reconfigurable robots (MRRs) with torque sensor is related to the treatment of interconnection term and friction term. This paper proposed a modified adaptive sliding mode decentralized control strategy for trajectory tracking control of the MRRs. The radial basis function (RBF) neural network is used as an effective learning method to approximate the interconnection term and friction term, eliminating the effect of model uncertainty and reducing the controller gain. In addition, in order to provide faster convergence and higher precision control, the terminal sliding mode algorithm is introduced to the controller design. Based on the Lyapunov method, the stability of the MRRs is proved. Finally, experiments are performed to confirm the effectiveness of the method.

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