Dynamic regressor extension and mixing control Lyapunov functions and high-order control Barrier functions

Abstract

This paper studies the safety-critical tracking control problem for unknown structured systems with bounded disturbances and high relative degree. Firstly, a dynamic regressor extension and mixing high-order control barrier function (DREM-HOCBF) is constructed with a DREM-based robust finite-time parameter identification law. Our approach directly enforces forward invariance of the system containing parameter estimates and worst-case estimation errors to ensure system safety against parameterised uncertainty. Due to DREM, this method can approach the safety set boundary and reduce the conservatism. Meanwhile, it's also applicable to higher relative degree constraints that are more common in actual systems. Then, a DREM control Lyapunov function (DREM-CLF) that leverages the same worst-case estimation errors is proposed to guarantee that the tracking errors converge exponentially. Moreover, the method's robustness against bounded disturbances is considered. Finally, the feasibility of the developed method is verified via an adaptive cruise control (ACC) problem with unknown parameters and bounded disturbances.

Keywords:

• Adaptive control
• safety-critical control
• dynamic regressor extension and mixing
• finite-time identification
• control barrier function
• adaptive cruise control

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China [grant numbers 62273079 and 61420106016]; in part by the Fundamental Research Funds for the Central Universities in China [grant numbers N2004002, N2104005, and N182608004]; and in part by the Research Fund of State Key Laboratory of Synthetical Automation for Process Industries in China [grant number 2013ZCX01].

Notes on contributors

Peng Sun

Peng Sun received the B.S. degree in automation from Qingdao University, Qingdao, China, in 2022, and he is currently a M.S. student in control science and engineering of Northeastern University, Shenyang, China. His current research interests include adaptive control and vehicle safety control.

Jiuxiang Dong

Jiuxiang Dong received the Ph.D. degree in navigation guidance and control from Northeastern University, China in 2009. He is currently a Professor at the College of Information Science and Engineering, Northeastern University. His research interests include fault tolerant control, fault diagnosis and adaptive control.