scholarly journals Position Control of Cable-Driven Robotic Soft Arm Based on Deep Reinforcement Learning

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 310
Author(s):  
Qiuxuan Wu ◽  
Yueqin Gu ◽  
Yancheng Li ◽  
Botao Zhang ◽  
Sergey A. Chepinskiy ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Control Strategy ◽  
Position Control ◽  
Control Method ◽  
Strategy Training ◽  
Soft Robot ◽  
Robot Arm ◽  
Control Task ◽  
Modeling And Control ◽  
And Migration

The cable-driven soft arm is mostly made of soft material; it is difficult to control because of the material characteristics, so the traditional robot arm modeling and control methods cannot be directly applied to the soft robot arm. In this paper, we combine the data-driven modeling method with the reinforcement learning control method to realize the position control task of robotic soft arm, the method of control strategy based on deep Q learning. In order to solve slow convergence and unstable effect in the process of simulation and migration when deep reinforcement learning is applied to the actual robot control task, a control strategy learning method is designed, which is based on the experimental data, to establish a simulation environment for control strategy training, and then applied to the real environment. Finally, it is proved by experiment that the method can effectively complete the control of the soft robot arm, which has better robustness than the traditional method.

scholarly journals A Collaborative Control Method of Dual-Arm Robots Based on Deep Reinforcement Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 1816
Author(s):  
Luyu Liu ◽  
Qianyuan Liu ◽  
Yong Song ◽  
Bao Pang ◽  
Xianfeng Yuan ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Control Strategy ◽  
Control Method ◽  
Gradient Algorithm ◽  
Robot Arm ◽  
Collaborative Control ◽  
Policy Gradient ◽  
Multi Agent ◽  
Dual Arm Robot ◽  
Dual Arm

Collaborative control of a dual-arm robot refers to collision avoidance and working together to accomplish a task. To prevent the collision of two arms, the control strategy of a robot arm needs to avoid competition and to cooperate with the other one during motion planning. In this paper, a dual-arm deep deterministic policy gradient (DADDPG) algorithm is proposed based on deep reinforcement learning of multi-agent cooperation. Firstly, the construction method of a replay buffer in a hindsight experience replay algorithm is introduced. The modeling and training method of the multi-agent deep deterministic policy gradient algorithm is explained. Secondly, a control strategy is assigned to each robotic arm. The arms share their observations and actions. The dual-arm robot is trained based on a mechanism of “rewarding cooperation and punishing competition”. Finally, the effectiveness of the algorithm is verified in the Reach, Push, and Pick up simulation environment built in this study. The experiment results show that the robot trained by the DADDPG algorithm can achieve cooperative tasks. The algorithm can make the robots explore the action space autonomously and reduce the level of competition with each other. The collaborative robots have better adaptability to coordination tasks.

Multiobjective Optimization for Stiffness and Position Control in a Soft Robot Arm Module

2018 ◽  
Vol 3 (1) ◽  
pp. 108-115 ◽  
Author(s):  
Y. Ansari ◽  
M. Manti ◽  
E. Falotico ◽  
M. Cianchetti ◽  
C. Laschi
Keyword(s):  
Multiobjective Optimization ◽  
Position Control ◽  
Soft Robot ◽  
Robot Arm

Time-Optimal Coordination Control for the Gear-Shifting Process in Electric-Driven Mechanical Transmission (Dog Clutch) without Impacts

2020 ◽  
Vol 9 (2) ◽  
pp. 155-168
Author(s):  
Ziwang Lu ◽  
◽  
Guangyu Tian ◽  
Keyword(s):  
Control Strategy ◽  
Position Control ◽  
Control Method ◽  
Synchronization Control ◽  
Mechanical Transmission ◽  
Coordination Control ◽  
Optimal Position ◽  
Drive Motor ◽  
Time Optimal ◽  
Optimal Coordination

Torque interruption and shift jerk are the two main issues that occur during the gear-shifting process of electric-driven mechanical transmission. Herein, a time-optimal coordination control strategy between the the drive motor and the shift motor is proposed to eliminate the impacts between the sleeve and the gear ring. To determine the optimal control law, first, a gear-shifting dynamic model is constructed to capture the drive motor and shift motor dynamics. Next, the time-optimal dual synchronization control for the drive motor and the time-optimal position control for the shift motor are designed. Moreover, a switched control for the shift motor between a bang-off-bang control and a receding horizon control (RHC) law is derived to match the time-optimal dual synchronization control strategy of the drive motor. Finally, two case studies are conducted to validate the bang-off-bang control and RHC. In addition, the method to obtain the appropriate parameters of the drive motor and shift motor is analyzed according to the coordination control method.

scholarly journals Novel Design and Position Control Strategy of a Soft Robot Arm

Robotics ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 72 ◽  
Author(s):  
Alaa Al-Ibadi ◽  
Samia Nefti-Meziani ◽  
Steve Davis ◽  
Theo Theodoridis
Keyword(s):  
Position Control ◽  
Control Strategies ◽  
Tensile Force ◽  
Compressive Force ◽  
Soft Robot ◽  
Robot Arm ◽  
Bending Actuator ◽  
Muscle Actuators ◽  
Novel Design ◽  
Bending Behaviour

This article presents a novel design of a continuum arm, which has the ability to extend and bend efficiently. Numerous designs and experiments have been done to different dimensions on both types of McKibben pneumatic muscle actuators (PMA) in order to study their performances. The contraction and extension behaviour have been illustrated with single contractor actuators and single extensor actuators, respectively. The tensile force for the contractor actuator and the compressive force for the extensor PMA are thoroughly explained and compared. Furthermore, the bending behaviour has been explained for a single extensor PMA, multi extensor actuators and multi contractor actuators. A two-section continuum arm has been implemented from both types of actuators to achieve multiple operations. Then, a novel construction is proposed to achieve efficient bending behaviour of a single contraction PMA. This novel design of a bending-actuator has been used to modify the presented continuum arm. Two different position control strategies are presented, arising from the results of the modified soft robot arm experiment. A cascaded position control is applied to control the position of the end effector of the soft arm at no load by efficiently controlling the pressure of all the actuators in the continuum arm. A new algorithm is then proposed by distributing the x, y and z-axis to the actuators and applying an effective closed-loop position control to the proposed arm at different load conditions.

scholarly journals Distributed Imitation-Orientated Deep Reinforcement Learning Method for Optimal PEMFC Output Voltage Control

2021 ◽  
Vol 9 ◽  
Author(s):  
Jiawen Li ◽  
Yaping Li ◽  
Tao Yu
Keyword(s):  
Reinforcement Learning ◽  
Control Strategy ◽  
Output Voltage ◽  
Proton Exchange Membrane ◽  
Control Method ◽  
Voltage Control ◽  
Proton Exchange ◽  
Gradient Algorithm ◽  
Voltage Control Strategy ◽  
The Stability

In order to improve the stability of proton exchange membrane fuel cell (PEMFC) output voltage, a data-driven output voltage control strategy based on regulation of the duty cycle of the DC-DC converter is proposed in this paper. In detail, an imitation-oriented twin delay deep deterministic (IO-TD3) policy gradient algorithm which offers a more robust voltage control strategy is demonstrated. This proposed output voltage control method is a distributed deep reinforcement learning training framework, the design of which is guided by the pedagogic concept of imitation learning. The effectiveness of the proposed control strategy is experimentally demonstrated.

Tip Position Control of Single Flexible Links via Parallel Feedforward Compensation

2019 ◽  
Author(s):  
Keyvan Noury ◽  
Bingen Yang
Keyword(s):  
Closed Loop ◽  
Position Control ◽  
Control Method ◽  
Robot Arm ◽  
Flexible Robot ◽  
Order Process ◽  
Nonminimum Phase ◽  
Model Mismatch ◽  
Tip Position ◽  
And Performance

Abstract Developed in this work, is a simple and innovative control method, by which a nonminimum-phase (NMP) process can be easily stabilized in a closed-loop setting. The method is named as the parallel feed-forward compensation with derivative effort (PFCD). Through use of a high order process, the control system designed by the PFCD method is shown to be less influenced by noise, disturbance, and model mismatch, compared to other methods. Moreover, the necessary data required for implementing the PFCD method are discussed. The proposed control method is illustrated on tip position control in a slewing beam as a flexible robot arm, in which the effectiveness of the PFCD method is demonstrated. In addition, the proposed control method is compared with the existing methods in terms of stability and performance. The paper is concluded with notes about the advantages.

scholarly journals Hybrid Force and Position Control Strategy of Robonaut Performing Object Transfer Task

2018 ◽  
Vol 160 ◽  
pp. 05003
Author(s):  
Gang Chen ◽  
Yu-Qi Wang ◽  
Qing-Xuan Jia ◽  
Pei-Lin Cai
Keyword(s):  
Control Strategy ◽  
Position Control ◽  
Control Method ◽  
Transfer Task ◽  
Internal Force ◽  
Matlab Simulation ◽  
Constraint Force ◽  
Common Control ◽  
Transfer Operation ◽  
External Forces

This paper proposes a coordinated hybrid force/position control strategy of robonaut performing object transfer operation. Firstly, the constraint relationships between robonaut and object are presented. Base on them, the unified dynamic model of the robonaut and object is established to design the hybrid force/position control method. The movement, the internal force and the external constraint force of the object are considered as the control targets of the control system. Finally, a MATLAB simulation of the robonaut performing object transfer task verifies the correctness and effectiveness of the proposed method. The results show that all the targets can be control accurately by using the method proposed in this paper. The presented control method can control both internal and external forces while maintaining control accuracy, which is a common control strategy.

Compliance Control and Stability Analysis of Cooperating Robot manipulators

Robotica ◽  
1989 ◽  
Vol 7 (3) ◽  
pp. 191-198 ◽  
Author(s):  
H. Kazerooni
Keyword(s):  
Stability Analysis ◽  
Control Strategy ◽  
Position Control ◽  
Control Method ◽  
Robot Manipulators ◽  
Contact Point ◽  
Sensitivity Function ◽  
Compliance Control ◽  
Cooperating Robots ◽  
The Stability

SUMMARYThe work presented here is the description of the control strategy of two cooperating robots. A two–finger hand is an example of such a System. The control method allows for position control of the contact point by one of the robots while the other robot controls the contact force. The stability analysis of two robot manipulators has been investigated using unstructured models for dynamic behavior of robot manipulators. For the stability of two robots, there must be some initial compliance in either robot. The initial compliance in the robots can be obtained by a non-zero sensitivity function for the tracking controller or a passive compliant element such as an RCC.

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