فهرست نشریات علمی دسترسی آزاد ایران

High-speed lane-change maneuvers in autonomous vehicles require control strategies that remain safe and well-damped under strongly nonlinear tire–vehicle dynamics and uncertain interactions with surrounding traffic. This paper proposes a stability-aware fuzzy control framework built on a nonlinear single-track (bicycle) vehicle model augmented with an enhanced semi-empirical Pacejka tire formulation to capture the coupled longitudinal–lateral–yaw behavior under realistic tire-condition effects. Two coordinated Mamdani type-1 fuzzy controllers are designed: one generates the steering command for lateral–yaw regulation and trajectory tracking, and the other produces the longitudinal acceleration command for speed adaptation and headway preservation. All membership-function parameters and rule weights are jointly tuned via constrained simulation-based optimization using the Slime Mould Algorithm (SMA), while actuator bounds and an explicit hard collision-avoidance constraint are enforced throughout the maneuver horizon. A Lyapunov-inspired penalty is embedded in the objective to suppress oscillations and promote well-damped responses. Simulations of high-speed lane changes (up to 72 km/h) show rapid convergence of lateral and longitudinal errors (≈2-3 s over a ≈450 m path), smooth control actions, and collision-free behavior. Compared with an unoptimized fuzzy baseline, the tuned design reduces peak lateral deviation by ≈35% and yaw-rate oscillations by ≈40%. Monte-Carlo trials with parametric uncertainty, noise measurement, and actuator delays further confirm repeatable closed-loop performance and robust safety margins in mixed-traffic scenarios.