Deep Model Reference Adaptive Control
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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.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Sep 2019.
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Citing papers per year: 2021: 6; 2022: 2; 2023: 3; 2024: 0; 2025: 2; 2026: 1.
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- 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 337
- 2021Adaptive-Control-Oriented Meta-Learning for Nonlinear Systemscited by 38
- 2022Control-oriented meta-learningcited by 17
- 2022Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Modelscited by 3
- 2021Bridging the Model-Reality Gap with Lipschitz Network Adaptationcited by 2
- 2023Unmatched uncertainty mitigation through neural network supported model predictive controlcited by 2
- 2021Adaptive Approach Phase Guidance for a Hypersonic Glider via Reinforcement Meta Learningcited by 1
- 2025World Models as Reference Trajectories for Rapid Motor Adaptationcited by 1
- 2021Stochastic Deep Model Reference Adaptive Controlcited by 0
- 2021Guaranteed Trajectory Tracking under Learned Dynamics with Contraction Metrics and Disturbance Estimationcited by 0
- 2023Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentationcited by 0
- 2023Lyapunov-Based Dropout Deep Neural Network (Lb-DDNN) Controllercited by 0
- 2026Neural ODEs Meet Concurrent Learning: Stable Online Learning with Lyapunov Guarantees26 Sep 2026
- 2025Algorithmic design and implementation considerations of deep MPC21 Nov 2025
- 2025World Models as Reference Trajectories for Rapid Motor Adaptation21 May 2025
- 2023Lyapunov-Based Dropout Deep Neural Network (Lb-DDNN) Controller30 Oct 2023
- 2023Unmatched uncertainty mitigation through neural network supported model predictive control22 Apr 2023
- 2023Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentation28 Mar 2023
- 2022Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Models11 May 2022
- 2022Control-oriented meta-learning14 Apr 2022
- 2021Guaranteed Trajectory Tracking under Learned Dynamics with Contraction Metrics and Disturbance Estimation15 Dec 2021
- 2021Bridging the Model-Reality Gap with Lipschitz Network Adaptation7 Dec 2021
- 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning13 Aug 2021
- 2021Stochastic Deep Model Reference Adaptive Control4 Aug 2021
- 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 337
- 2021Adaptive-Control-Oriented Meta-Learning for Nonlinear Systemscited by 38
- 2022Control-oriented meta-learningcited by 17
- 2022Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Modelscited by 3
- 2021Bridging the Model-Reality Gap with Lipschitz Network Adaptationcited by 2
- 2023Unmatched uncertainty mitigation through neural network supported model predictive controlcited by 2
- 2021Adaptive Approach Phase Guidance for a Hypersonic Glider via Reinforcement Meta Learningcited by 1
- 2021Stochastic Deep Model Reference Adaptive Controlcited by 0
- 2021Guaranteed Trajectory Tracking under Learned Dynamics with Contraction Metrics and Disturbance Estimationcited by 0
- 2023Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentationcited by 0
- 2023Lyapunov-Based Dropout Deep Neural Network (Lb-DDNN) Controllercited by 0
- 2025Algorithmic design and implementation considerations of deep MPCcited by 0
12 most cited of 13 All 13 (JSON)
- 2026Neural ODEs Meet Concurrent Learning: Stable Online Learning with Lyapunov Guarantees26 Sep 2026
- 2025Algorithmic design and implementation considerations of deep MPC21 Nov 2025
- 2023Lyapunov-Based Dropout Deep Neural Network (Lb-DDNN) Controller30 Oct 2023
- 2023Unmatched uncertainty mitigation through neural network supported model predictive control22 Apr 2023
- 2023Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentation28 Mar 2023
- 2022Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Models11 May 2022
- 2022Control-oriented meta-learning14 Apr 2022
- 2021Guaranteed Trajectory Tracking under Learned Dynamics with Contraction Metrics and Disturbance Estimation15 Dec 2021
- 2021Bridging the Model-Reality Gap with Lipschitz Network Adaptation7 Dec 2021
- 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning13 Aug 2021
- 2021Stochastic Deep Model Reference Adaptive Control4 Aug 2021
- 2021Adaptive Approach Phase Guidance for a Hypersonic Glider via Reinforcement Meta Learning30 Jul 2021
12 newest of 13 All 13 (JSON)