A data-driven control methodology applied to throttle valves

• Published in: Control engineering practice Vol. 139; no. October; p. 105634
• Main Authors: Witrant, Emmanuel, Landau, Ioan Doré, Vaillant, Marie-Pierre
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2023; Elsevier
• Subjects: Adaptive control; Artificial Intelligence; Computer Science; Data-driven control; Electronic throttle control (ETC); PI design; System identification; Electronic throttle control (ETC); System identification; PI design; Data-driven control; Adaptive control; adaptive control; system identification
• ISSN: 0967-0661; 1873-6939
• DOI: 10.1016/j.conengprac.2023.105634

Electric throttle valves represent a challenge for control design, as their dynamics involves strong nonlinearities, characterized by an asymmetric hysteresis. Large variability in the characteristics of each valve and erratic steady-state behaviors can also be noticed by carrying out experiments on multiple valves, impairing classical model-based control strategies. Nevertheless, local data-driven linear models can be obtained by system identification, and simple proportional–integral (PI) digital controllers can be tuned individually for each valve, providing good tracking performance. As these controllers cannot be transposed from one valve to another, a robust control design is considered. Taking into account the variability of electric throttle valves, a real-time data-driven strategy is then proposed, using identification in closed-loop and controller re-design. This methodology is necessary if control performance is a key issue, and can be embedded on a low-cost controller board (Arduino® Mega 2560). Experimental results going from frequency analysis and linear design to real-time data-driven control illustrate the methodology presented in the paper. •A linear data-based control method can handle the nonlinearities of throttle valves.•Robust design copes with the variability in the characteristics of each valve.•Adaptive control estimates the parameters and tunes the controller in real-time.•The data-based control method can easily be embedded in a low-cost Arduino chip.