# Deep Model Reference Adaptive Control

arXiv: https://arxiv.org/abs/1909.08602 · HTML: https://mlsanity.com/paper/1909.08602/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 14 of 14)

- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems](https://mlsanity.com/paper/2103.04490/) (2021)
- [Control-oriented meta-learning](https://mlsanity.com/paper/2204.06716/) (2022)
- [Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Models](https://mlsanity.com/paper/2205.05787/) (2022)
- [Adaptive Approach Phase Guidance for a Hypersonic Glider via Reinforcement Meta Learning](https://mlsanity.com/paper/2107.14764/) (2021)
- [Bridging the Model-Reality Gap with Lipschitz Network Adaptation](https://mlsanity.com/paper/2112.03756/) (2021)
- [Unmatched uncertainty mitigation through neural network supported model predictive control](https://mlsanity.com/paper/2304.11315/) (2023)
- [World Models as Reference Trajectories for Rapid Motor Adaptation](https://mlsanity.com/paper/2505.15589/) (2025)
- [Stochastic Deep Model Reference Adaptive Control](https://mlsanity.com/paper/2108.03120/) (2021)
- [Guaranteed Trajectory Tracking under Learned Dynamics with Contraction Metrics and Disturbance Estimation](https://mlsanity.com/paper/2112.08222/) (2021)
- [Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentation](https://mlsanity.com/paper/2303.15688/) (2023)
- [Lyapunov-Based Dropout Deep Neural Network (Lb-DDNN) Controller](https://mlsanity.com/paper/2310.19938/) (2023)
- [Algorithmic design and implementation considerations of deep MPC](https://mlsanity.com/paper/2511.17233/) (2025)
- [Neural ODEs Meet Concurrent Learning: Stable Online Learning with Lyapunov Guarantees](https://mlsanity.com/paper/2609.32289/) (2026)

## Abstract (arXiv)

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.