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This paper presents a deep neural network (DNN)-and concurrent learning (CL)-based adaptive control architecture for an Euler-Lagrange dynamic system that guarantees system performance for the first time. The developed controller includes two DNNs with the same output-layer weights to ensure feasibility of the control system. In this work, a Lyapunov-and CL-based update law is developed to update the output-layer DNN weights in real-time; whereas, the inner-layer DNN weights are updated offline using data that is collected in real-time. A Lyapunov-like analysis is performed to prove that the proposed controller yields semi-global exponential convergence to an ultimate bound for the output-layer weight estimation errors and for the trajectory tracking errors.
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This content will become publicly available on July 10, 2025
Augmentation of a Lyapunov-Based Deep Neural Network Controller with Concurrent Learning for Control-Affine Nonlinear Systems
A deep neural network (DNN)-based adaptive controller with a real-time and concurrent learning (CL)-based adaptive update law is developed for a class of uncertain, nonlinear dynamic systems. The DNN in the control law is used to approximate the uncertain nonlinear dynamic model. The inner-layer weights of the DNN are updated offline using data collected in real-time; whereas, the output-layer DNN weights are updated online (i.e., in real-time) using the Lyapunov- and CL-based adaptation law. Specifically, the inner-layer weights of the DNN are trained offline (concurrent to real-time execution) after a sufficient amount of data is collected in real-time to improve the performance of the system, and after training is completed the inner-layer DNN weights are updated in batch-updates. The key development in this work is that the output-layer DNN update law is augmented with CL-based terms to ensure that the output-layer DNN weight estimates converge to within a ball of their optimal values. A Lyapunov-based stability analysis is performed to ensure semi-global exponential convergence to an ultimate bound for the trajectory tracking errors and the output-layer DNN weight estimation errors.
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- Award ID(s):
- 2230971
- PAR ID:
- 10562219
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-8265-5
- Page Range / eLocation ID:
- 2885 to 2890
- Subject(s) / Keyword(s):
- Nonlinear control Deep Neural Networks (DNNs) Concurrent Learning (CL) Lyapunov methods
- Format(s):
- Medium: X
- Location:
- Toronto, ON, Canada
- Sponsoring Org:
- National Science Foundation
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