Vehicle Control

Vehicle Control research develops the algorithms, architectures, and validation methodologies that translate high-level intent — from a driver or autonomous planner — into precise, safe actuator commands. The field draws on classical control theory, modern optimal and predictive control, and increasingly on learning-based approaches to handle the complexity and uncertainty inherent in real-world driving. Key challenges include guaranteeing stability margins across the full operating envelope, handling actuator saturation and delay, and coordinating multiple subsystems such as braking, steering, and propulsion simultaneously. Research outcomes feed directly into electronic stability control, traction management, chassis coordination, and the low-level execution layers of autonomous driving stacks.

• Model predictive control for chassis coordination
• Robust and adaptive control under uncertainty
• Integrated braking, steering, and powertrain control
• Steer-by-wire and brake-by-wire system design
• Reinforcement learning for vehicle control policies
• Safety-critical control with formal stability guarantees