Cruise Controller With Fuel Optimization Based On Adaptive Nonlinear Predictive Control

Automotive cruise control systems are used to automatically maintain the speed of a vehicle at a desired speed set-point. It has been shown that fuel economy while in cruise control can be improved using advanced control methods. The objective of this paper is to validate an Adaptive Nonlinear Model Predictive Controller (ANLMPC) implemented in a vehicle equiped with standard production Powertrain Control Module (PCM). Application and analysis of Model Predictive Control utilizing road grade preview information has been reported by many authors, namely for commercial vehicles. The authors reported simulations and application of linear and nonlinear MPC based on models with fixed parameters, which may lead to inaccurate results in the real world driving conditions. The significant noise factors are namely vehicle mass, actual weather conditions, fuel type, etc. In the ANLMPC approach, the vehicle and fuel model parameters are adapted automatically, so accuracy of the prediction is ensured. The adaptation is implemented by a Recursive Least Square (RLS) algorithm and the numerical robustness is improved by adopting Bierman's implementation with exponential/directional forgetting, and with suitable RLS stopping condition. The ANLMPC has been validated in real world driving conditions running in a production PCM module of a Sport Utility Vehicle (SUV), showing up to 2.4% fuel economy improvement in average compared to the production cruise controller with the same time of arrival. It has been confirmed that the ANLMPC can be run in a standard PCM module with single precision arithmetic, together with its other powertrain control functions.