Section: New Results

Plug&Play control for highly non-linear systems: Stability analysis of autonomous vehicles

Participants : Francisco Navas, Vicente Milanés, Fawzi Nashashibi.

The final stage for automating a vehicle relies on the control algorithms. They are in charge of providing the proper behavior and performance to the vehicle, leading to provide fully automated capabilities. Controllability and stability of dynamic complex systems are the key aspects when it comes to design intelligent control algorithms for vehicles.

Nowadays, the problem is that control systems are “monolithic”. That means that a minor change in the system could require the entire redesign of the control system. It addresses a major challenge, a system able to adapt the control structure automatically when a change occurred.

An autonomous vehicle is built by combining a set-of-sensors and actuators together with sophisticated algorithms. Since sensors and actuators are prone to intermittent faults, the use of different sensors is better and more cost effective than duplicating the same sensor type. The problem is to deal with the different availability of each sensor/actuator and how the vehicle should react to these changes. A methodology that improves the security of autonomous driving systems by providing a framework managing different sensor/actuator setups should be carried out. New trends are proposing intelligent algorithms able to handle any unexpected circumstances as unpredicted uncertainties or even fully outages from sensors. This is the case of Plug&Play control, which is able to provide stability responses for autonomous vehicles under uncontrolled circumstances, including modifications on the input/output sensors.

In order to meet with the idea of automatically handling those changes into the system, different research lines should be followed:

• Reconfiguration of existing controllers whenever changes are introduced in the system being controlled. In that line, the already commercially available Adaptive Cruise Controller (ACC) system, and its evolution by adding vehicle-to-vehicle communication (CACC) are examined. Plug&Play control is used for providing stable transitions between both controllers when the vehicle-to-vehicle communication link is changing from available to available or vice versa. More detail can be found in [38]. Gain scheduling approaches can be achieved by using the same structure. An Advanced-CACC is developed by using it. Hybrid behaviors between controllers with different head times are carried out depending on the traffic situation.

• Online closed loop identification of the vehicle and its components. Plug&Play control also provides a way for doing online closed loop identification of any system as open loop like systems. Here, the obtained models for the vehicle will be compared with the physical lateral model (Bicycle and 2GDL) and the longitudinal model together with the tire models (Pacejka, Dugoff and Buckhardt). It is also possible to identify new sensors or actuators connected to the system.

• Automatic control reconfiguration to achieve optimal performance together with identification of the new situation. Once a new situation has been identified in the system, the controller should be reconfigured to achieve the optimal performance of the autonomous vehicle.