Research on feedforward NMPC path tracking control method for autonomous vehicles
In high-curvature scenarios like sharp bends, the hysteresis in the vehicle ' s steering system and the linearization of the vehicle model may cause insufficient steering and steady-state errors, thereby impacting the precision of autonomous vehicle path tracking and its response speed.To address this issue, we introduce a novel path tracking framework, which triggers a feedforward control controller to generate an ideal steering angle sequence, proactively guiding the steering mechanism to approach the optimal steering angle in advance.Subsequently, a nonlinear model predictive controller incorporating a repelling target function optimally determines the best control sequence, which is then applied to the vehicle to update the ideal steering angle sequence.An autonomous vehicle experimental platform is built, and simulation verification is conducted in various scenarios.Our results indicate that, in comparison to traditional model predictive control methods that disregard hysteresis, the feedforward nonlinear model predictive controller improves the tracking accuracy and response speed.In high-curvature scenarios in particular, our framework reduces the lateral root mean square error by nearly 30％.
autonomous vehiclessteering hysteresisfeedforward controlnonlinear model prediction controlpath tracking
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