Risk-Informed Model-Free Safe Control of Linear Parameter-Varying Systems

Babak Esmaeili; Hamidreza Modares

doi:10.1109/JAS.2024.124479

Volume 11 Issue 9

Sep. 2024

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2024 > 11(9): 1918-1932

B. Esmaeili and H. Modares, “Risk-informed model-free safe control of linear parameter-varying systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1918–1932, Sept. 2024. doi: 10.1109/JAS.2024.124479

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B. Esmaeili and H. Modares, “Risk-informed model-free safe control of linear parameter-varying systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1918–1932, Sept. 2024. doi: 10.1109/JAS.2024.124479

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Risk-Informed Model-Free Safe Control of Linear Parameter-Varying Systems

doi: 10.1109/JAS.2024.124479

Funds: This work was supported in part by the Department of Navy award (N00014-22-1-2159) and the National Science Foundation under award (ECCS-2227311)

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Author Bio:
Babak Esmaeili (Graduate Student Member, IEEE) received the B.Sc. degree in electrical engineering and control and the M.Sc. degree in mechatronics engineering from the University of Tabriz, Iran, in 2014 and 2017, respectively. He is currently pursuing the Ph.D. degree with the Department of Mechanical Engineering, Michigan State University, USA. His current research interests include data-based control, safe reinforcement learning, and autonomous systems. Mr. Esmaeili serves as a Reviewer for several international journals, including IEEE Transactions on Cybernetics, ISA Transactions, and Journal of Vibration and Control

Hamidreza Modares (Senior Member, IEEE) received the B.S. degree from the University of Tehran, Iran, in 2004, the M.S. degree from the Shahrood University of Technology, Iran, in 2006, and the Ph.D. degree from The University of Texas at Arlington, USA, in 2015. He was a Senior Lecturer with the Shahrood University of Technology from 2006 to 2009 and a Faculty Research Associate with The University of Texas at Arlington from 2015 to 2016. He is currently an Assistant Professor with the Department of Mechanical Engineering, Michigan State University, USA. His current research interests include cyber-physical systems, reinforcement learning, distributed control, robotics, and machine learning. Dr. Modares was a recipient of the Best Paper Award from the 2015 IEEE International Symposium on Resilient Control Systems. He is also an Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Systems, Man, and Cybernetics (SMC), and Neurocomputing
Corresponding author: Hamidreza Modares, e-mail: modaresh@msu.edu
¹ To watch the animation of the path tracking performance, please click on the following link: https://youtube.com/shorts/va6sQXzegdw.
² To watch the video of the set-point tracking performance of the robot in Gazebo, please click on the following link: https://www.youtube.com/watch?v=3ULDY4uTluE.
³ To watch the video of the set-point tracking performance of the robot in real-world, please click on the following link: https://www.youtube.com/shorts/uvQIkGUE2fQ.
Received Date: 2024-02-23
Revised Date: 2024-04-04
Accepted Date: 2024-04-14

Available Online: 2024-06-13

Abstract

Abstract

This paper presents a risk-informed data-driven safe control design approach for a class of stochastic uncertain nonlinear discrete-time systems. The nonlinear system is modeled using linear parameter-varying (LPV) systems. A model-based probabilistic safe controller is first designed to guarantee probabilistic $\lambda $-contractivity (i.e., stability and invariance) of the LPV system with respect to a given polyhedral safe set. To obviate the requirement of knowing the LPV system model and to bypass identifying its open-loop model, its closed-loop data-based representation is provided in terms of state and scheduling data as well as a decision variable. It is shown that the variance of the closed-loop system, as well as the probability of safety satisfaction, depends on the decision variable and the noise covariance. A minimum-variance direct data-driven gain-scheduling safe control design approach is presented next by designing the decision variable such that all possible closed-loop system realizations satisfy safety with the highest confidence level. This minimum-variance approach is a control-oriented learning method since it minimizes the variance of the state of the closed-loop system with respect to the safe set, and thus minimizes the risk of safety violation. Unlike the certainty-equivalent approach that results in a risk-neutral control design, the minimum-variance method leads to a risk-averse control design. It is shown that the presented direct risk-averse learning approach requires weaker data richness conditions than existing indirect learning methods based on system identification and can lead to a lower risk of safety violation. Two simulation examples along with an experimental validation on an autonomous vehicle are provided to show the effectiveness of the presented approach.
- Data-driven control,
- linear parameter-varying systems,
- probabilistic control,
- safe control

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¹ To watch the animation of the path tracking performance, please click on the following link: https://youtube.com/shorts/va6sQXzegdw.
² To watch the video of the set-point tracking performance of the robot in Gazebo, please click on the following link: https://www.youtube.com/watch?v=3ULDY4uTluE.
³ To watch the video of the set-point tracking performance of the robot in real-world, please click on the following link: https://www.youtube.com/shorts/uvQIkGUE2fQ.

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Data-Driven Safe Control: Introduces a method for designing safe controls in nonlinear systems using direct data, without requiring system models
Risk-Averse Design: Implements a minimum-variance approach to enhance safety and reduce risk, favoring robust over neutral strategies
Practical Validation: Demonstrates effectiveness through simulations and an experimental autonomous vehicle application

Risk-Informed Model-Free Safe Control of Linear Parameter-Varying Systems

doi: 10.1109/JAS.2024.124479

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