Reduced Model Based Control of Two Link Flexible Space Robot

Kumar, Amit; Pathak, Pushparaj Mani; Sukavanam, N.

doi:10.4236/ica.2011.22013

Cited by 14 publications

(10 citation statements)

References 11 publications

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“…In such cases, reinforcement learning and policy search algorithms that can learn from a robot's experience have been shown to be successful [8], [9] for tasks such as object manipulation [10], [11], [12], locomotion [13], [14], [15], [16] and flight [17]. However, most of this work involves using a model-free component to approximate features of the robot or the world that cannot be modeled while still using model-based controllers for other parts of the system [12], [18] In work where flexibility is taken into consideration, learning is still based either on building a more complex model [6], [19], [20], an approximate model [21] or plugging in a learned-model component into a model-based controller. Recently, work involving end-to-end model-free methods using deep reinforcement learning have been demonstrated successfully in rigid real robots [22], [23], [16].…”

Section: Related Workmentioning

confidence: 99%

On Training Flexible Robots using Deep Reinforcement Learning

Dwiel

Candadai

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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The use of robotics in controlled environments has flourished over the last several decades and training robots to perform tasks using control strategies developed from dynamical models of their hardware have proven very effective. However, in many real-world settings, the uncertainties of the environment, the safety requirements and generalized capabilities that are expected of robots make rigid industrial robots unsuitable. This created great research interest into developing control strategies for flexible robot hardware for which building dynamical models are challenging. In this paper, inspired by the success of deep reinforcement learning (DRL) in other areas, we systematically study the efficacy of policy search methods using DRL in training flexible robots. Our results indicate that DRL is successfully able to learn efficient and robust policies for complex tasks at various degrees of flexibility. We also note that DRL using Deep Deterministic Policy Gradients can be sensitive to the choice of sensors and adding more informative sensors does not necessarily make the task easier to learn.

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Section: Related Workmentioning

confidence: 99%

On Training Flexible Robots using Deep Reinforcement Learning

Dwiel

Candadai

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…The system dynamics and parameters do not appear in controller (16), which make it very robust to shocks from the system parameter variations.…”

Section: Remarkmentioning

confidence: 99%

“…The parameters K p and K d in the proposed controller (16) have the same values with that in PD control, and the noncollocated control parameters are determined as K e = diag(0, 0, 0, 440, 320, 0, 0, 0, 0, 0, 0) and Comparing with the results of PD control (26), it can be seen that the proposed noncollocated model-free position control (16) can achieve better performance of position regulation with less overshoots and vibrations, thereby the tip position trajectory of the end effector are smoother shown in Fig. 8.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…To increase the agility of space robot during the space mission, multi-link flexible manipulators are introduced into the space robotic system. Numerous dynamics and control approaches for space robotic system with two-link flexible manipulator have been applied successfully in many kinds of applications [7][8][9][10][11][12][13][14][15][16][17][18][19]. Considering that the noncollocated signal can directly reflect the flexible vibration, several studies of noncollocated feedback control Shuzhi Sam Ge is also with Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore.…”

Section: Introductionmentioning

confidence: 99%

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Dynamics and Noncollocated Model‐Free Position Control for a Space Robot with Multi‐Link Flexible Manipulators

Yang

Liu

2018

Asian Journal of Control

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In this paper, both the dynamics and noncollocated model-free position control (NMPC) for a space robot with multi-link flexible manipulators are developed. Using assumed modes approach to describe the flexible deformation, the dynamic model of the flexible space robotic system is derived with Lagrangian method to represent the system dynamic behaviors. Based on Lyapunov's direct method, the robust model-free position control with noncollocated feedback is designed for position regulation of the space robot and vibration suppression of the flexible manipulators. The closed-loop stability of the space robotic system can be guaranteed and the guideline of choosing noncollocated feedback is analyzed. The proposed control is easily implementable for flexible space robot with both uncertain complicated dynamic model and unknown system parameters, and all the control signals can be measured by sensors directly or obtained by a backward difference algorithm. Numerical simulations on a two-link flexible space robot are provided to demonstrate the effectiveness of the proposed control.

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“…There has been a great number of studies coping with controlling flexible-arm robots, many of which investigate both theoretical and experimental aspects in this field [4] [5]. On grounds of the flexibility of the arms, along with a trajectory-tracking control problem, vibration control should be also considered for such systems to improve the control system performance [6].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Robust Approach to Modeling and Control of Flexible Arm Robots

Bagheri¹,

Behjat²,

Sun³

2018

WJET

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In this paper, a novel adaptive robust approach to modeling and control of a class of flexible-arm robots subject to actuators unmodeled dynamics is proposed. It is shown how real-time signals measured from a dynamical system can be utilized to improve the accuracy of the mathematical model of flexible robots. Given the elasticity of the robot's arms, flexible manipulators have both passive and active degrees of freedom. A nonlinear robust controller is designed for the active degrees of freedom to enable the robot to follow desired trajectories in the presence of actuators unmodeled dynamics. Furthermore, it is shown that under some feasible conditions, another nonlinear robust controller is designed for the passive degrees of freedom. Moreover, to use the system response for model extraction, two auxiliary signals are proposed to provide sufficient information for improving the accuracy of the dynamics of the system numerically. Additionally, two adaptive laws are proposed in each case to update the two introduced auxiliary signals. As a result, the controller controls the passive degrees of freedom after the active degrees of freedom converge to their desired trajectories. Simultaneously, the information collected from the system to update the auxiliary signals enhances the model accuracy. In the end, simulation results are presented to verify the performance of the proposed controller.

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Reduced Model Based Control of Two Link Flexible Space Robot

Cited by 14 publications

References 11 publications

On Training Flexible Robots using Deep Reinforcement Learning

On Training Flexible Robots using Deep Reinforcement Learning

Dynamics and Noncollocated Model‐Free Position Control for a Space Robot with Multi‐Link Flexible Manipulators

An Adaptive Robust Approach to Modeling and Control of Flexible Arm Robots

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