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Dispersed-learning optimal control method of reconfigurable robot in contact with uncertain environment

A technology for reconfiguring robots and optimal control, applied in adaptive control, general control systems, control/regulation systems, etc., can solve robot joint chattering effects, low stability and control accuracy, and uncertain reconfigurable robots System high-precision decentralized optimal control and other issues

Active Publication Date: 2018-01-05
CHANGCHUN UNIV OF TECH
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Problems solved by technology

[0005] In order to solve the problems of chattering effect of robot joints and low stability and control precision in the traditional reconfigurable robot control method, the present invention proposes a distributed learning optimal control method with good performance to realize High-Precision Decentralized Optimal Control of Reconfigurable Robotic Systems

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  • Dispersed-learning optimal control method of reconfigurable robot in contact with uncertain environment
  • Dispersed-learning optimal control method of reconfigurable robot in contact with uncertain environment
  • Dispersed-learning optimal control method of reconfigurable robot in contact with uncertain environment

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Embodiment Construction

[0071] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0072] Such as figure 1 As shown, using the distributed learning optimal control method for reconfigurable robots of the present invention, the key processing methods and processes in its realization are as follows:

[0073] 1. Establishment of dynamic model.

[0074] The dynamic model of the reconfigurable robot system is established as follows:

[0075]

[0076] In the above formula, the subscript i represents the i-th module, and I mi is the moment of inertia of the rotating shaft, γ i is the gear ratio, θ i , and are the joint positions, velocities and accelerations, respectively, is the joint friction term, is the coupling torque cross-link term between joint subsystems, τf i is the joint output torque, τ i is the motor output torque.

[0077] In formula (1), the joint friction term It can be considered as a function of joint position...

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Abstract

The invention discloses a dispersed-learning optimal control method of a reconfigurable robot in contact with an uncertain environment, and belongs to the field of robot control algorithms. The methodaims at solving the problems of poor buffeting effect and stability and low control accuracy of robot joints in traditional reconfigurable robot control methods, and includes the steps of firstly, creating a reconfigurable robot system dynamics model, and constructing a cost function and an HJB equation according to the analysis of coupling moment crosslinking items among reconfigurable robot joint subsystems; by means of a learning algorithm based on strategy iteration, figuring out the solution to the HJB equation; adopting a neural network to conduct approximation on the cost function, andfinally verifying the validity of the presented control method through simulation; the method can make the robot system achieve great stability and control accuracy under the condition of uncertain environment-oriented contact, improve the control accuracy of the reconfigurable robot, and meanwhile reduce energy consumption of a system actuator and complexity of the robot system dynamics model.

Description

technical field [0001] The invention relates to a distributed learning optimal control method for a reconfigurable robot system, belonging to the field of robot control algorithms. Background technique [0002] A reconfigurable robot consists of modules such as power sources, processing systems, actuators, and sensors. The combination of these modules meets the standard electromechanical interfaces of different configurations to adapt to various task requirements in complex working environments. Based on the above advantages, reconfigurable robots are often used in uncertain and dangerous environments, such as space exploration, disaster rescue, high-speed Working in low temperature environment, etc. In addition, in the face of complex and uncertain environments, reconfigurable robots need a suitable control system that balances control accuracy and power consumption. [0003] In general, in order to achieve modularity and reconfigurability, reconfigurable robots should ha...

Claims

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Application Information

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IPC IPC(8): G05B13/04
Inventor 董博王梓旭周帆李岩刘克平李元春
Owner CHANGCHUN UNIV OF TECH
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