CNN-based End-to-End Adaptive Controller with Stability Guarantees (2403.03499v1)
Abstract: This letter proposes a convolutional neural network (CNN)-based adaptive controller wtih three notable features: 1) it determines control input directly from historical sensor data (in an end-to-end process); 2) it learns the desired control policy during real-time implementation without using a pretrained network (in an online adaptive manner); and 3) the asymptotic tracking error convergence is proven during the learning process (to deliver a stability guarantee). An adaptive law for learning the desired control policy is derived using the gradient descent optimization method, and its stability is analyzed based on the Lyapunov approach. A simulation study using a control-affine nonlinear system demonstrated that the proposed controller exhibits these features, and its performance can be tuned by manipulating the design parameters. In addition, it is shown that the proposed controller has a superior tracking performance to that of a deep neural network (DNN)-based adaptive controller.
- Springer, 2022.
- Springer, 2009.
- O. S. Patil, D. M. Le, M. L. Greene, and W. E. Dixon, “Lyapunov-derived control and adaptive update laws for inner and outer layer weights of a deep neural network,” IEEE Control Systems Letters, vol. 6, pp. 1855–1860, 2021.
- E. J. Griffis, O. S. Patil, Z. I. Bell, and W. E. Dixon, “Lyapunov-based long short-term memory (Lb-LSTM) neural network-based control,” IEEE Control Systems Letters, 2023.
- R. G. Hart, E. J. Griffis, O. S. Patil, and W. E. Dixon, “Lyapunov-based physics-informed long short-term memory (LSTM) neural network-based adaptive control,” IEEE Control Systems Letters, 2023.
- V. Rausch, A. Hansen, E. Solowjow, C. Liu, E. Kreuzer, and J. K. Hedrick, “Learning a deep neural net policy for end-to-end control of autonomous vehicles,” in 2017 American Control Conference (ACC), pp. 4914–4919, IEEE.
- J. Jhung, I. Bae, J. Moon, T. Kim, J. Kim, and S. Kim, “End-to-end steering controller with cnn-based closed-loop feedback for autonomous vehicles,” in 2018 IEEE intelligent vehicles symposium (IV), pp. 617–622, IEEE.
- X. Liu and Y. Dong, “A convolutional neural networks approach to devise controller,” in MATEC Web of Conferences, vol. 139, p. 00168, EDP Sciences.
- H. Nobahari and Y. Seifouripour, “A nonlinear controller based on the convolutional neural networks,” in 2019 7th International Conference on Robotics and Mechatronics (ICRoM), pp. 362–367, IEEE.
- Princeton university press, 2009.
- D.-X. Zhou, “Universality of deep convolutional neural networks,” Applied and computational harmonic analysis, vol. 48, no. 2, pp. 787–794, 2020.
- John Wiley & Sons, Inc., 1995.
- H. K. Khalil, Nonlinear systems; 3rd ed. Upper Saddle River, NJ: Prentice-Hall, 2002. The book can be consulted by contacting: PH-AID: Wallet, Lionel.
- Springer Science & Business Media, 2013.