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Iterative Learning Control Algorithm for Feedforward Controller of EGR and VGT Systems in a CRDI Diesel Engine

  • Kyunghan Min
  • Myoungho Sunwoo
  • Manbae Han
Article
  • 121 Downloads

Abstract

The modern diesel engines equip the exhaust gas recirculation (EGR) system to suppress the NOx emissions. In addition, the variable geometric turbocharger (VGT) system is installed to improve the drivability and fuel efficiency. These EGR and VGT systems have cross-coupled behavior because they interact with the intake and the exhaust manifolds. Furthermore, the turbocharger time delay, gas flow dynamics through EGR pipe cause the nonlinearity characteristics. These nonlinear multi-input-multi-output characteristics cause the degradation of control accuracy, especially during the transient condition. In order to improve the control accuracy, this study proposes an iterative learning control (ILC) algorithm for feedforward controller of EGR and VGT systems. The feedforward controller obtains the values about EGR and VGT actuators using the previous control results in predefined transient states. The ILC algorithm consists of a PD-type learning function and a low-pass filter. The control gains of learning function are determined to guarantee the convergence of learning results. In addition, the low-pass filter is designed for robustness against plant disturbance. The proposed control algorithm was validated by engine experiment which repeated predefined transient states. The error was reduced by 15 % according to the gain. As a result, the proposed algorithm is affordable for improving the transient control performance.

Key words

Diesel engine control Learning control EGR VGT Iterative learning control Transient control 

Nomenclature

y

plant output

u

input

d

uncertainty of transient condition

k

time step index

e

tracking error

Q

Q-filter

L

learning function

P

plant response

p

impulse system response

q

time shift index

y

vectors of plant output

u

vectors of plant input

d

vectors of uncertainty

W

known and stable disturbance

Δ

unknown and stable disturbance

Kd

derivative learning gain

Kp

proportional learning gain

Subscripts

j

iteration index

d

desired value

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Copyright information

© The Korean Society of Automotive Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Automotive EngineeringHanyang UniversitySeoulKorea
  2. 2.Department of Mechanical and Automotive EngineeringKeimyung UniversityDaeguKorea

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