A hybrid switching predictive controller based on bi-level kernel-based ELM and online trajectory builder for automotive coldstart emissions reduction

Azad, Nasser L.; Mozaffari, Ahmad; Hedrick, J. Karl

doi:10.1016/j.neucom.2015.08.070

Cited by 8 publications

(1 citation statement)

References 36 publications

(41 reference statements)

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“…ELM techniques have been applied successfully to engine fault detection (Yin et al, 2014;Wong et al, 2015b) and modeling diesel and biodiesel engines using data (Wong et al, 2013(Wong et al, , 2015aMozaffari and Azad, 2014) showing its suitability in modeling combustion engines. Furthermore, ELM models have been successfully applied for automotive control purposes where control algorithms using ELM have been developed for engine cold start emission control (Azad et al, 2015), air-ratio control (Wong et al, 2014) and autonomous cruise control of electric vehicles (Mozaffari et al, 2015). The major advantage of ELM (and data based modeling in general) is that it approximates the real system and makes no (or minimal) simplifying assumptions about the underlying phenomena.…”

Section: Introductionmentioning

confidence: 99%

An ELM based predictive control method for HCCI engines

Janakiraman

Nguyen

Assanis

2016

Engineering Applications of Artificial Intelligence

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We formulate and develop a control method for homogeneous charge compression ignition (HCCI) engines using model predictive control (MPC) and models learned from operational data. An HCCI engine is a highly efficient but complex combustion system that operates with a high fuel efficiency and reduced emissions compared to the present technology. HCCI control is a nonlinear, multi-input multi-output problem with state and actuator constraints which makes controller design a challenging task. In this paper, we propose an MPC approach where the constraints are elegantly included in the control problem along with optimality in control. We develop the engine models using experimental data so that the complexity and time involved in the modeling process can be reduced. An extreme learning machine (ELM) is used to capture the engine dynamic behavior and is used by the MPC controller to evaluate control actions. We also used a simplified quadratic programming making use of the convexity of the MPC problem so that the algorithm can be implemented on the engine control unit that is limited in memory. The working and effectiveness of the proposed MPC methodology has been analyzed in simulation using a nonlinear HCCI engine model. The controller tracks several reference signals taking into account the constraints defined by HCCI states, actuators and operational limits.

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