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# Machine Learning Applications to Robot Control

Author: Omar Abdul-hadi Berkeley, CA : University of California, Berkeley, 2018. Ph. D. in Mechanical Engineering University of California, Berkeley 2018 Thesis/dissertation : Document : Thesis/dissertation : eBook  Computer File : English Control of robot manipulators can be greatly improved with the use of velocity and torque feedforward control. However, the effectiveness of feedforward control greatly relies on the accuracy of the model. In this study, kinematics and dynamics analysis is performed on a six axis arm, a Delta2 robot, and a Delta3 robot. Velocity feedforward calculation is performed using the traditional means of using the kinematics solution for velocity. However, a neural network is used to model the torque feedforward equations. For each of these mechanisms, we first solve the forward and inverse kinematics transformations. We then derive a dynamic model. Later, unlike traditional methods of obtaining the dynamics parameters of the dynamics model, the dynamics model is used to infer dependencies between the input and output variables for neural network torque estimation. The neural network is trained with joint positions, velocities, and accelerations as inputs, and joint torques as outputs. After training is complete, the neural network is used to estimate the feedforward torque effort. Additionally, an investigation is done on the use of neural networks for deriving the inverse kinematics solution of a six axis arm. Although the neural network demonstrated outstanding ability to model complex mathematical equations, the inverse kinematics solution was not accurate enough for practical use.  Read more... (not yet rated) 0 with reviews - Be the first.

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### Abstract:

Control of robot manipulators can be greatly improved with the use of velocity and torque feedforward control. However, the effectiveness of feedforward control greatly relies on the accuracy of the model. In this study, kinematics and dynamics analysis is performed on a six axis arm, a Delta2 robot, and a Delta3 robot. Velocity feedforward calculation is performed using the traditional means of using the kinematics solution for velocity. However, a neural network is used to model the torque feedforward equations. For each of these mechanisms, we first solve the forward and inverse kinematics transformations. We then derive a dynamic model. Later, unlike traditional methods of obtaining the dynamics parameters of the dynamics model, the dynamics model is used to infer dependencies between the input and output variables for neural network torque estimation. The neural network is trained with joint positions, velocities, and accelerations as inputs, and joint torques as outputs. After training is complete, the neural network is used to estimate the feedforward torque effort. Additionally, an investigation is done on the use of neural networks for deriving the inverse kinematics solution of a six axis arm. Although the neural network demonstrated outstanding ability to model complex mathematical equations, the inverse kinematics solution was not accurate enough for practical use.

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