Cooperative-game-theoretic optimal robust path tracking control for autonomous vehicles

Modeling uncertainties are a major concern in vehicle path tracking control. As a practical engineering system, the uncertainties in vehicle lateral dynamics can be time-varying while bounded and have certain distributions wherein. The fuzzy set theory can effectively describe system uncertainties in terms of boundary and distribution. Contrary to fuzzy logic-based approaches, this article puts forward an explicit multiparameter optimal robust control law to ensure the uniform boundedness and ultimate uniform boundedness of the closed-loop path tracking dynamical system. Then, the tracking performance as well as the control cost is quantified as cost functions using fuzzy set theories. Finally, an optimization problem is established in the content of cooperative game to seek the optimal values for the tunable parameters. Simulations are conducted using CarSim and Simulink under double lane change and serpentine driving conditions. The results show that the proposed robust optimal control exhibits superior tracking performance.