Auxiliary-Variable Adaptive Control Barrier Functions for Safety Critical Systems (2304.00372v3)
Abstract: This paper studies safety guarantees for systems with time-varying control bounds. It has been shown that optimizing quadratic costs subject to state and control constraints can be reduced to a sequence of Quadratic Programs (QPs) using Control Barrier Functions (CBFs). One of the main challenges in this method is that the CBF-based QP could easily become infeasible under tight control bounds, especially when the control bounds are time-varying. The recently proposed adaptive CBFs have addressed such infeasibility issues, but require extensive and non-trivial hyperparameter tuning for the CBF-based QP and may introduce overshooting control near the boundaries of safe sets. To address these issues, we propose a new type of adaptive CBFs called Auxiliary-Variable Adaptive CBFs (AVCBFs). Specifically, we introduce an auxiliary variable that multiplies each CBF itself, and define dynamics for the auxiliary variable to adapt it in constructing the corresponding CBF constraint. In this way, we can improve the feasibility of the CBF-based QP while avoiding extensive parameter tuning with non-overshooting control since the formulation is identical to classical CBF methods. We demonstrate the advantages of using AVCBFs and compare them with existing techniques on an Adaptive Cruise Control (ACC) problem with time-varying control bounds.
- K. P. Tee, S. S. Ge, and E. H. Tay, “Barrier lyapunov functions for the control of output-constrained nonlinear systems,” Automatica, vol. 45, no. 4, pp. 918–927, 2009.
- S. Prajna, A. Jadbabaie, and G. J. Pappas, “A framework for worst-case and stochastic safety verification using barrier certificates,” IEEE Transactions on Automatic Control, vol. 52, no. 8, pp. 1415–1428, 2007.
- D. Panagou, D. M. Stipanovič, and P. G. Voulgaris, “Multi-objective control for multi-agent systems using lyapunov-like barrier functions,” in 52nd IEEE Conference on Decision and Control, 2013, pp. 1478–1483.
- L. Wang, A. D. Ames, and M. Egerstedt, “Multi-objective compositions for collision-free connectivity maintenance in teams of mobile robots,” in 2016 IEEE 55th Conference on Decision and Control (CDC), 2016, pp. 2659–2664.
- A. D. Ames, X. Xu, J. W. Grizzle, and P. Tabuada, “Control barrier function based quadratic programs for safety critical systems,” IEEE Transactions on Automatic Control, vol. 62, no. 8, pp. 3861–3876, 2016.
- P. Glotfelter, J. Cortés, and M. Egerstedt, “Nonsmooth barrier functions with applications to multi-robot systems,” IEEE control systems letters, vol. 1, no. 2, pp. 310–315, 2017.
- A. D. Ames, K. Galloway, and J. W. Grizzle, “Control lyapunov functions and hybrid zero dynamics,” in 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012, pp. 6837–6842.
- Q. Nguyen and K. Sreenath, “Exponential control barrier functions for enforcing high relative-degree safety-critical constraints,” in 2016 American Control Conference (ACC), 2016, pp. 322–328.
- W. Xiao and C. A. Belta, “High-order control barrier functions,” IEEE Transactions on Automatic Control, vol. 67, no. 7, pp. 3655–3662, 2021.
- S.-C. Hsu, X. Xu, and A. D. Ames, “Control barrier function based quadratic programs with application to bipedal robotic walking,” in 2015 American Control Conference (ACC), 2015, pp. 4542–4548.
- U. Borrmann, L. Wang, A. D. Ames, and M. Egerstedt, “Control barrier certificates for safe swarm behavior,” IFAC-PapersOnLine, vol. 48, no. 27, pp. 68–73, 2015.
- J. Zeng, Z. Li, and K. Sreenath, “Enhancing feasibility and safety of nonlinear model predictive control with discrete-time control barrier functions,” in 2021 60th IEEE Conference on Decision and Control (CDC), 2021, pp. 6137–6144.
- S. Liu, J. Zeng, K. Sreenath, and C. A. Belta, “Iterative convex optimization for model predictive control with discrete-time high-order control barrier functions,” in 2023 American Control Conference (ACC), 2023, pp. 3368–3375.
- T. Gurriet, M. Mote, A. D. Ames, and E. Feron, “An online approach to active set invariance,” in 2018 IEEE Conference on Decision and Control (CDC), 2018, pp. 3592–3599.
- A. Singletary, P. Nilsson, T. Gurriet, and A. D. Ames, “Online active safety for robotic manipulators,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019, pp. 173–178.
- T. Gurriet, M. Mote, A. Singletary, P. Nilsson, E. Feron, and A. D. Ames, “A scalable safety critical control framework for nonlinear systems,” IEEE Access, vol. 8, pp. 187 249–187 275, 2020.
- Y. Chen, M. Jankovic, M. Santillo, and A. D. Ames, “Backup control barrier functions: Formulation and comparative study,” in 2021 60th IEEE Conference on Decision and Control (CDC), 2021, pp. 6835–6841.
- E. Squires, P. Pierpaoli, and M. Egerstedt, “Constructive barrier certificates with applications to fixed-wing aircraft collision avoidance,” in 2018 IEEE Conference on Control Technology and Applications (CCTA), 2018, pp. 1656–1661.
- J. Breeden and D. Panagou, “High relative degree control barrier functions under input constraints,” in 2021 60th IEEE Conference on Decision and Control (CDC), 2021, pp. 6119–6124.
- W. Xiao, C. A. Belta, and C. G. Cassandras, “Sufficient conditions for feasibility of optimal control problems using control barrier functions,” Automatica, vol. 135, p. 109960, 2022.
- ——, “Adaptive control barrier functions,” IEEE Transactions on Automatic Control, vol. 67, no. 5, pp. 2267–2281, 2021.