Adaptive Nonlinear Model Predictive Cruise Controller: Trailer Tow Use Case

Conventional cruise control systems in automotive applications are usually designed to maintain the constant speed of the vehicle based on the desired set-point. It has been shown that fuel economy while in cruise control can be improved using advanced control methods namely adopting the Model Predictive Control (MPC) technology utilizing the road grade preview information and allowance of the vehicle speed variation. This paper is focused on the extension of the Adaptive Nonlinear Model Predictive Controller (ANLMPC) reported earlier by application to the trailer tow use-case. As the connected trailer changes the aerodynamic drag and the overall vehicle mass, it may lead to the undesired downshifts for the conventional cruise controller introducing the fuel economy losses. In this work, the ANLMPC concept is extended to avoid downshifts by translating the downshift conditions to the constraints of the underlying optimization problem to be solved. To deal with significant noise factors, e.g., overall vehicle mass, change of aerodynamic drag, actual weather conditions, fuel type, the on-line adaptation of the parameters is performed by the constrained Recursive Least Squares (RLS) method. The issue of the lack of the excitation due to the limited changes in torque and engine speed in cruise is solved by the directional forgetting and dead-zone approach together with limits on the parameters. The implementation details of ANLMPC are given describing a technique leading the significant reduction of the RAM memory. The ANLMPC has been validated in real world driving conditions running in production PCM module of a Sport Utility Vehicle (SUV) towing a trailer with 1600 kg of load, showing 6.3-8.8% fuel economy improvement compared to production cruise controller with the same time of arrival.