Tuning of model predictive engine controllers over transient drive cycles⁎This work was supported by Toyota Motor Corporation, Japan. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Maass, Alejandro I.; Manzie, Chris; Shames, Iman; Chin, Robert; Nešić, Dragan; Ulapane, Nalika; Nakada, Hayato
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 14022
• Page End: 14027
• Publication Date: 2020
• Subjects: Gradient-free optimisation -- model-based control -- controller calibration -- diesel engines -- automotive control; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.923
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: A framework for tuning the parameters of model predictive controllers (MPCs) based on gradient-free optimisation (GFO) is proposed. Efficient calibration of MPCs is often a difficult task given the large number of tuning parameters and their non-intuitive correlation with the output response. We propose an efficient and systematic framework for the tuning of MPC parameters that can be implemented iteratively within the closed-loop setting. The performance of the proposed GFO-based algorithm is evaluated through its application to air-path control for diesel engines over simulations and experiments. We illustrate that the tuned parameters provide satisfactory tracking of reference trajectories over engine drive cycles with only a few iterations. Thereby, we extend existing MPC tuning approaches that calibrate parameters using step responses on the fuel rate and engine speed onto tuning over a full drive cycle response.