A receding horizon optimal control approach to active state and parameter estimation in automotive systems

Abstract

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A receding horizon optimal control approach is proposed to control a system in such a way as to best estimate on-line its states and parameters, without significantly degrading tracking performance or violating pointwise-in-time constraints. Several automotive examples are considered to illustrate the potential of the approach. They include: vehicle mass and road grade estimation, engine wall-wetting parameter estimation, engine mapping, and estimation of the position and velocity of a moving vehicle from angle-only (passive) measurements by another vehicle.