On the Robustness of Adaptive Nonlinear Model Predictive Cruise Control

Paper #:
  • 2018-01-1360

  • 2018-04-03
In order to improve the fuel economy while in cruise, the Model Predictive Control (MPC) technology has been adopted utilizing the road grade preview information and allowance of the vehicle speed variation. This paper is focused on robustness study of delivered fuel economy benefit of Adaptive Nonlinear Model Predictive Controller (ANLMPC) reported earlier to several noise factors, e.g. vehicle platform, vehicle weight, fuel type etc. Further, as the vehicle position is obtained via GPS with finite precision and source of road grade preview might be inaccurate, the effect of inaccurate information of the road grade preview worsen the fuel economy benefits is studied and remedy to it is established. It is shown that the effect of scale and value offset error in the road grade preview can be eliminated by the on-line adaptation of the model parameters performed by the constrained Recursive Least Squares (RLS) method and the estimation of the additive acceleration by the Extended Kalman Filter (EKF). The effect of phase error in road grade preview is eliminated by the iterative improvement of the estimation of the vehicle’s true position over the grade map. The algorithm is based on minimization of vehicle model fitting residuals over a past horizon, which is formulated as a one-dimensional nonlinear optimization problem solved by Newton-Raphson iterations at each ANLMPC sample time. Success in grade error correction is verified by both model fitting quality and the resulting control performance benefit. The ANLMPC with road grade preview error correction has been validated in real-world driving conditions running in production PCM module of a light duty truck towing a trailer with 4500 kg of load, showing up to 10% fuel economy improvement compared to production cruise controller with the same time of arrival.
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