Extremum-Seeking-Based Adaptive Model-Free Control and Its Application to Automated Vehicle Path Tracking

Traditional automated vehicle path-tracking algorithms require plant models to derive the respective control laws. However, the accurate vehicle model is difficult to obtain due to the complex tire-road interaction, time-varying parameters, and unknown disturbances. Consequently, data-driven controllers, which do not rely on a predefined plant model, have become increasingly popular. Notably, the model-free control (MFC), which expresses the derivatives of a controlled output as the sums of an amplified control input and an offset term, provides a straightforward solution to ground vehicle path tracking. Despite its ease of use, the control gain tuning of MFC remains largely a trial-and-error process, which could be both painstaking and poorly performing. Existing adaptive gain-tuning methods either rely on the command at the last step to iteratively update the control gain at the current step, or try to simultaneously identify the control gain and the offset term. However, they could yield chattering or unbounded control gains. To provide a new perspective for the MFC control gain tuning while improving MFC's control performance, we integrate MFC with the extremum-seeking control (ESC). ESC updates the control gain of MFC in real time to progressively enhance the control performance of MFC. Simulink–CarSim simulations and scaled car field tests demonstrate the effectiveness of the proposed extremum-seeking-based adaptive model-free controller.