NARMAX modelling and robust control of internal combustion engines

Spark Ignition Engine Idle Speed Control Using a Novel Framework and Enabling Control of the Tradeoff between Fuel Consumption and Load Rejection Capability

“…They range from linear control techniques based around an operating point (e.g., LQG [3], H I [4]) to nonlinear techniques (e.g., sliding mode [5], feedback linearisation [6], fuzzy control [2], neural networks [7], other [8,9] 2 ).…”

Model selection approaches for non-linear system identification: a review

“…Linear-in-the-parameter models have been widely applied for monitoring, controlling and supervisioning across all traditional engineering sectors, like mechanical (Parlitz et al 2004;Govindhasamy, McLoone, Irwin, French, and Doyle 2005;Altinkok 2006) electrical and electronic (Park et al 1991;Leva and Piroddi 2002), chemical (Soumelidis and Stobart 2006), energy and power (Glass and Franchek 1999;Jurado 2004;Li, Thompson, and Peng 2004;Basso, Giaue, Groppi, and Zappa 2005), aerospace and aeronautical (Faller and Schreck 1996), civil (Flood and Kartam 1998), and environmental systems (Nunnari et al 2004;Peng et al 2004). More recently, applications in newer areas are being reported including communication networks (Chen, Gibson, Cowan, and Grant 1991;Clarkson 1999), biomedical, biochemical and life systems (Ma and Wang 2000;Gamero, Armentano, Barra, Simon, and, Levenson 2001;Sargantanis and Karim 2004;Karayiannis et al 2006), as well as other sectors other than engineering, such as financial (Marose 1990), social (Garson 1991), health care and medical (Dybowski and Gant 2001).…”

“…For example, it has been used in the modelling and control of power systems, such as internal combustion engine (Glass and Franchek 1999), automotive diesel engine (Billings, Chen, and Backhouse 1989), power plant gas turbine and micro-turbine modelling (Basso et al 2005;Jurado 2005), magneto-rheological damping devices (Leva and Piroddi 2002) and fuel cell plants (Jurado 2004), modelling and control of longitudinal vehicle dynamics (Kalkkuhl, Hunt, and Fritze 1999), identification of pre-sliding friction dynamics (Parlitz et al 2004), modelling of a pH waste water neutralisation process (Luo, Morris, Karim, Martin, and Hong 1996), dynamic modelling of three-way catalysts (Soumelidis and Stobart 2006), and air pollution modelling and control Soumelidis and Stobart 2006). It has also been used to model arterial wall dynamics in animals (Gamero et al 2001), and modelling and control of a batch Bacillus subtilis fermentation process (Sargantanis and Karim 2004).…”

Identification of time-varying systems using multiresolution wavelet models

“…The noise variable , with maximum lag , accommodates the effects of measurement noise, modelling errors and unmeasured disturbances. A rigorous derivation of the NARMAX model and many applications have been proposed in the literature (see Leontaritis and Billings 1985, Tabrizi 1990, Cooper 1991, Noshiro et al 1993, Jang and Kim 1994, Aguirre and Billings 1995, Tabrizi 1998, Radhakrishnan et al 1999, Glass and Franchek 1999. …”