Control System and Simulation Design for an All-Wheel-Drive Formula SAE Car Using a Neural Network Estimated Slip Angle Velocity

Beacock, Benjamin
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University of Guelph

In 2004, students at the University of Guelph designed and constructed an all-wheel-drive Formula SAE vehicle for competition. It utilized an electronically-controlled, hydraulic-actuated limited slip center coupling from Haldex Traction Ltd, to transfer torque to the front wheels. The initial control system design was not comprehensively conceived, so there was a need for a thoroughly developed control system for the all-wheel-drive actuator augmented with commonly available sensors and a low cost controller. This thesis presents a novel all-wheel-drive active torque transfer controller using a neural network estimated slip angle velocity. This controller specifically targets a racing vehicle by allowing rapid direction changes for maneuverability but damping slip angle changes for increased controllability. The slip angle velocity estimate was able to track the actual simulated value it was trained against with excellent phase matching but with some offsets and phantom spikes. Using the estimated slip angle velocity for control realized smooth control output, excellent stability, and a fast turn-in yaw response on par with rear-wheel-drive configurations. A full vehicle simulation with software-in-the-loop testing for control software was also developed to aid the system design process and avoid vehicle run time for tuning. This design flow should significantly decrease development time for controls algorithm work and help increase innovation within the team.

slip angle, neural network, FSAE, Formula SAE, vehicle control, vehicle simulation, stability control, AWD, four wheel drive, all wheel drive, soft computing, vehicle dynamics