Security-Based Resilient Triggered Output Feedback Lane Keeping Control for Human–Machine Cooperative Steering Intelligent Heavy Truck Under Denial-of-Service Attacks

Abstract

This article addresses dynamic authority allocation strategy and security-based resilient event-triggered output feedback lane keeping control (LKC) problem for human–machine cooperative steering intelligent heavy truck subject to nonperiodic energy bounded denial-of-service (DoS) attacks. For authority allocation strategy of considered LKC system, designed practical and easily-to-implement cooperative allocation factor (CAF) is adaptively adjusted by the correlation characteristics between buffered interested states for the first time. For derived fuzzy model of networked LKC system with time-varying CAF and path tracking velocity, a new security-based resilient event-triggered output feedback control with communication scheme consisting of adaptive adjusted auxiliary variable and attack-induced random but bounded uncertainty is proposed to reduce communication burden when free of attacks and simultaneously release necessary data for stability maintenance at the cost of predefined allowable control performance degradation when attack-induced uncertainty less than its upper bound. Furthermore, sufficient conditions are derived for exponentially stability of considered LKC system with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm{H}\infty $</tex-math></inline-formula> performance, and gain matrices for controller and trigger are cdesigned and obtained by solving certain matrix inequalities. Benefiting from saved communication resource and more sparse but necessary assistant torque commands under resilient event-triggered communication scheme as well as dynamic authority allocation strategy with adaptive CAF, the obtained results illustrating fewer driving conflict, less driver effort, and higher resistant ability to attack-induced yaw instability demonstrate the effectiveness of the proposed control strategy.