Deep Model Reference Adaptive Control
Machine Learning
2019-09-19 v1 Systems and Control
Systems and Control
Abstract
We present a new neuroadaptive architecture: Deep Neural Network based Model Reference Adaptive Control (DMRAC). Our architecture utilizes the power of deep neural network representations for modeling significant nonlinearities while marrying it with the boundedness guarantees that characterize MRAC based controllers. We demonstrate through simulations and analysis that DMRAC can subsume previously studied learning based MRAC methods, such as concurrent learning and GP-MRAC. This makes DMRAC a highly powerful architecture for high-performance control of nonlinear systems with long-term learning properties.
Cite
@article{arxiv.1909.08602,
title = {Deep Model Reference Adaptive Control},
author = {Girish Joshi and Girish Chowdhary},
journal= {arXiv preprint arXiv:1909.08602},
year = {2019}
}
Comments
Accepted in IEEE CDC-2019