Data driven design of tonal noise feedback cancellers

Abstract

DOI : 10.1016/j.ifacol.2017.08.087International audienceThis paper emphasizes the design methodology for active tonal noise feedback cancellers starting from data collected on the system. To design such control systems, an accurate dynamic model of the system is necessary. Physical modeling can provide qualitative results but fails to yield enough accurate models for control design. The main point in the methodology is identification of primary path (noise propagation) and secondary path (compensation) models from data. The procedure is investigated in details starting with transfer functions' order estimations, continuing with parameters estimation and model's validation. The second aspect is the design of a noise canceller using the Internal Model Principle and the sensitivity function shaping in order to reduce the " water-bed " effect. The estimated model's quality for control design is illustrated by the experimental performance of a tonal noise feedback canceller implemented on a test bench

Beyond the delay barrier in adaptive feedforward active noise control using Youla–Kučera parametrization

“…The models of the systems have been identified experimentally using the identification procedure described in [9]. The frequency characteristics 5 of the identified models for the primary 6 , secondary and reverse paths are shown in Fig.…”

“…are the measurements provided by the primary transducer. 9 The measured residual error satisfies the following equation:…”

On the use of Youla–Kucera parametrisation in adaptive active noise and vibration control – a review

“…Landau, Karimi, and Constantinescu (2001); I. Landau and Zito (2005); Melendez et al (2017). Since in this paper we are focused on regulation, the controller to be designed is a RS-type polynomial controller (I.…”

“…Therefore, when the model of the disturbance is unknown and/or time varying in order to apply the IMP, one has to identify in real time the model of the disturbance and then solve in real-time a Bezout equation which in general is of high order (the orders of the polynomials A and B are large in ANC and AVC -see I. Landau et al (2016) and Melendez et al (2017). This lead to an indirect adaptive regulation scheme.…”

“…In addition using system identification methodology an accurate model of the secondary path (as well as of the reverse path in feedforward control) can be obtained from on site experimental data (see I. Landau et al (2016), Melendez, Landau, Dugard, and Buche (2017)). Therefore one can assume that the plant model is constant and known and the eventually small variations of the plant model are quantifiable and bounded and therefore, their effect can be handled by the control design.…”