Unveiling Four Key Factors for Tire Force Control Allocation in 4WID-4WIS Electric Vehicles at Handling Limits
The four-wheel independent drive and four-wheel independent steering (4WID-4WIS) configurations enhance control flexibility and dynamic performance potential for more integrated electric vehicles. This paper comprehensively analyzes the impacts of four key factors on tire force control allocation: vertical load estimation, actuator dynamic characteristics, tire force constraints, and wheel steering precision at handling limits. The study demonstrates that precise vertical load estimation enhances lateral force allocation accuracy. Additionally, the self-compensating effect of lateral tire forces minimizes the impact of small deviations in vertical load estimation on tire force control allocation. A novel control allocation method considering actuator dynamics is introduced, effectively improving yaw rate response and reducing tracking errors. Considering tire-road adhesion and actuator rate constraints, an innovative method to calculate the real-time attainable tire force volume is proposed based on the tire slip ratio and slip angle. Feedforward control with bump steer compensation is implemented to improve wheel steering precision and lateral tire force control accuracy. Matlab/Simulink and Carsim co-simulation results emphasize the importance of these key factors' individual impacts and combined effects. This analysis offers valuable insights for developing advanced tire force control allocation strategies in 4WID-4WIS electric vehicles.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Extended Pacejka Tire Model for Enhanced Vehicle Stability Control
Despite their widespread use, current tire models have demonstrated a certain level of inaccuracy, primarily due to uncertainties related to unaccounted nonlinearity and disturbance effects resulting from tire operating …
Autonomous Circular Drift Control for 4WD-4WS Vehicles Without Precomputed Drifting Equilibrium
Under extreme conditions, autonomous drifting enables vehicles to follow predefined paths at large slip angles, significantly enhancing the control system's capability to handle hazardous scenarios. Four-wheel-drive and …
From Pixels to Factors: Learning Independently Controllable State Variables for Reinforcement Learning
Algorithms that exploit factored Markov decision processes are far more sample-efficient than factor-agnostic methods, yet they assume a factored representation is known a priori -- a requirement that breaks down when th…
Reinforcement LearningContrastive LearningLateral tracking control of all-wheel steering vehicles with intelligent tires
The accurate characterization of tire dynamics is critical for advancing control strategies in autonomous road vehicles, as tire behavior significantly influences handling and stability through the generation of forces a…
A hierarchical adaptive nonlinear model predictive control approach for maximizing tire force usage in autonomous vehicles
The ability to reliably maximize tire force usage would improve the safety of autonomous vehicles, especially in challenging edge cases. However, vehicle control near the limits of handling has many challenges, including…
Autonomous VehiclesComputational EfficiencyFrictionModel Predictive Control