Anticipation-Based Autonomous Platoon Control Strategy with Minimum Parameter Learning Adaptive Radial Basis Function Neural Network (MPL-ARBF-NN) Sliding Mode Control
2022-01-0348
03/29/2022
- Content
- Human-driver is stochastic in nature and usually difficult to predict. In car following scene, where an autonomous vehicle platoon follows a human-driven vehicle having a complex acceleration behavior therefore needs greater attention for smooth traffic flow. To guarantee a string stable platoon operation, an efficient vehicle controller must be developed for fast convergence, adaptation simplicity, ease of design and real-time performance. The research proposed an adaptive fuzzy system-based compound variable exponential power reaching law (CVEPRL) sliding mode control in a backstepping design framework. The fuzzy system employs an efficient adaptation to minimize computational burden in real-time operation of the platoon. The ease of controller development arise from the backstepping control design and robustness of the controller can be inherited from sliding mode control (SMC). The autonomous vehicle can be modeled as a third order nonlinear plant with a varying disturbance term with first order lag time and unknown resistance force term. Since most of the external disturbances and road resistance forces are unavailable to the system, the efficient fuzzy system can be used to identify the unmodeled time-varying uncertain term and an extended disturbance observer will also be integrated with the controller to observe the unknown disturbance of the plant. A comparative performance study of the proposed method with conventional SMC with exponential, power and double power reaching laws has been performed. The proposed method was able to guarantee string stability in the ideal where a sensor signal delay.
- Citation
- Negash, N., and Yang, J., "Anticipation-Based Autonomous Platoon Control Strategy with Minimum Parameter Learning Adaptive Radial Basis Function Neural Network (MPL-ARBF-NN) Sliding Mode Control ," SAE Technical Paper 2022-01-0348, 2022, .