Modeling and learning control system design for robots using deep learning techniques
Date
2021-08-18
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Abstract
In recent years, research and development in artificial intelligence dramatically impacted fields
like health care, agriculture, and manufacturing. In the robotics field, innovation such as
recognition, interaction, and manipulation made a breakthrough in "How a robot can help to
improve the quality of human life?". Deep learning for real-time applications became viable as
computer power and data became more readily available. In the past classical methods of modeling
such as Euler-Lagrange method widley have been used to construct accurate dynamic model of
the robots, but due to the uncertainty in the parameters and external disturbances, the accurate and
generalized analytical model is too complex to build. On the other hand, deep learning algorithms
are general non-linear modelling techniques that can learn from input-output data, making them a
good choice for modelling and model-based control system design for robots. In this research,
recurrent neural network techniques such as Long Short-Term Memory (LSTM) and Gated
Recurrent Unit (GRU) are adopted to overcome the issues of modelling. Then, by effectively
combining adaptive sliding mode controller (ASMC) with the deep-learning-based (LSTM-based)
inverse dynamic model of the robot a model-based control can be developed. . The deep-learningbased controller is applied for trajectory tracking on various scenarios to verify the effectiveness
of this approach. The primary strategy of designing a controller with a deep recurrent neural
network is to benefit from its memory, resulting in better generalization, accuracy enhancement,
and better estimation of time-varying parameters and disturbances. The simulation and
experimental findings show that the suggested controller operates adequately on unknown
trajectories and disturbances even without tuning settings.
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Keywords
Robot learning control, deep learning, deep recurrent neural network, adaptive sliding mode control, long short-term memory, gated recurrent unit