Toyota's Next Generation Powertrain Control System for Maximizing Vehicle Performance: Model Predictive Approach

Authors

Akio Matsunaga, Hayato Nakada, Hayato Shirai, Hiroyuki Tominaga,
Toyota Motor Corporation, Shizuoka, Japan

Summary

The model predictive approach (MPA) is attracting attention as methods to be able to simultaneously track references and satisfy constraints. This paper describes case studies based on the reference governor (RG) and the model predictive control (MPC) techniques as typical MPA methods. Firstly, in the RG case study, a modelprediction-based optimization algorithm to be able to modify references is successfully applied to a conventional constraint enforcement calibration process.
Next, the MPC case study shows MPC, which calculates actuator positions considering constraint satisfaction on an actual engine, has a capability to further improve calibration efficiency. Finally, this paper describes the outlook of MPA as a basic concept for next-generation powertrain control structures.

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