Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 32, Issue 12, Pages 5502-5511Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3053269
Keywords
Autonomous vehicles; Vehicle dynamics; Process control; Mathematical model; Trajectory; Optimal control; Cost function; Adaptive dynamic programming (ADP); autonomous vehicle; event-triggered control; optimal control; resilient control; tracking control
Categories
Funding
- National Postdoctoral Program for Innovative Talents of China [BX20200357]
- China Postdoctoral Science Foundation [2020M680718]
- Singapore National Research Foundation Delta-NTU Corporate Lab Program (DELTA-NTU CORP-SMA-RP2)
- Singapore Ministry of Education Tier 1 Academic Research Grant [2013-T1-002-177]
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This article investigates adaptive resilient event-triggered control for RWDA vehicles using an iterative single critic learning framework to balance the frequency/changes in adjusting the vehicle's control. By combining event-triggered sampling mechanism and novel utility function design, the control input can be guaranteed into an applicable saturated bound, demonstrating effectiveness and practicality in driving RWDA vehicles.
This article investigates the adaptive resilient event-triggered control for rear-wheel-drive autonomous (RWDA) vehicles based on an iterative single critic learning framework, which can effectively balance the frequency/changes in adjusting the vehicle's control during the running process. According to the kinematic equation of RWDA vehicles and the desired trajectory, the tracking error system during the autonomous driving process is first built, where the denial-of-service (DoS) attacking signals are injected into the networked communication and transmission. Combining the event-triggered sampling mechanism and iterative single critic learning framework, a new event-triggered condition is developed for the adaptive resilient control algorithm, and the novel utility function design is considered for driving the autonomous vehicle, where the control input can be guaranteed into an applicable saturated bound. Finally, we apply the new adaptive resilient control scheme to a case of driving the RWDA vehicles, and the simulation results illustrate the effectiveness and practicality successfully.
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