Responsiveness Improvement of Idling Speed Control for Automotive Using SMC

Similar documents
High-Order Hamilton s Principle and the Hamilton s Principle of High-Order Lagrangian Function

Dynamic Modeling of a Synchronous Generator Using T-S Fuzzy Approach

On the Coordinated Control of Multiple HVDC Links: Modal Analysis Approach

Chapter 7: Conservation of Energy

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system

Suppose that there s a measured wndow of data fff k () ; :::; ff k g of a sze w, measured dscretely wth varable dscretzaton step. It s convenent to pl

WHY NOT USE THE ENTROPY METHOD FOR WEIGHT ESTIMATION?

Army Ants Tunneling for Classical Simulations

#64. ΔS for Isothermal Mixing of Ideal Gases

New Liu Estimators for the Poisson Regression Model: Method and Application

A New Generalized Controller for Engine in Idle Speed Condition

ENGI9496 Lecture Notes Multiport Models in Mechanics

Visualization of 2D Data By Rational Quadratic Functions

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

A new Approach for Solving Linear Ordinary Differential Equations

Mechanics Physics 151

Precision Tracking Control of a Piezoelectric-Actuated System

SIMPLIFIED MODEL-BASED OPTIMAL CONTROL OF VAV AIR- CONDITIONING SYSTEM

Large-Scale Data-Dependent Kernel Approximation Appendix

Introduction. - The Second Lyapunov Method. - The First Lyapunov Method

Note 10. Modeling and Simulation of Dynamic Systems

Analytical classical dynamics

Design of Optimum Controllers for Gas Turbine Engines

The equation of motion of a dynamical system is given by a set of differential equations. That is (1)

Pop-Click Noise Detection Using Inter-Frame Correlation for Improved Portable Auditory Sensing

Design and Optimization of Fuzzy Controller for Inverse Pendulum System Using Genetic Algorithm

Turbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH

A Note on the Numerical Solution for Fredholm Integral Equation of the Second Kind with Cauchy kernel

Explicit bounds for the return probability of simple random walk

IMPROVED TRAJECTORY CONTROL FOR AN INTERIOR PERMANENT MAGNET SYNCHRONOUS MOTOR DRIVE WITH EXTENDED OPERATING LIMIT

829. An adaptive method for inertia force identification in cantilever under moving mass

Difference Equations

COEFFICIENT DIAGRAM: A NOVEL TOOL IN POLYNOMIAL CONTROLLER DESIGN

Solutions to Practice Problems

Adaptive sliding mode reliable excitation control design for power systems

On a one-parameter family of Riordan arrays and the weight distribution of MDS codes

Design of dual-loop attitude controller for target missile based on fuzzy variable structure

Operating conditions of a mine fan under conditions of variable resistance

LNG CARGO TRANSFER CALCULATION METHODS AND ROUNDING-OFFS

Design Equations. ν ij r i V R. ν ij r i. Q n components. = Q f c jf Qc j + Continuous Stirred Tank Reactor (steady-state and constant phase)

ENTROPIC QUESTIONING

( ) = : a torque vector composed of shoulder torque and elbow torque, corresponding to

Discrete time state feedback with setpoint control, actual state observer and load estimation for a tumor growth model

First Law: A body at rest remains at rest, a body in motion continues to move at constant velocity, unless acted upon by an external force.

NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS

Module 3. Process Control. Version 2 EE IIT, Kharagpur 1

Workshop: Approximating energies and wave functions Quantum aspects of physical chemistry

Digital Signal Processing

An Algorithm to Solve the Inverse Kinematics Problem of a Robotic Manipulator Based on Rotation Vectors

Adaptive Synchronization of Rössler System Based on. Dynamic Surface ControlPF

Grover s Algorithm + Quantum Zeno Effect + Vaidman

DETERMINATION OF TEMPERATURE DISTRIBUTION FOR ANNULAR FINS WITH TEMPERATURE DEPENDENT THERMAL CONDUCTIVITY BY HPM

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

3. MODELING OF PARALLEL THREE-PHASE CURRENT-UNIDIRECTIONAL CONVERTERS 3. MODELING OF PARALLEL THREE-PHASE CURRENT-

Parallel MAC Based On Radix-4 & Radix-8 Booth Encodings

AGC Introduction

CHAPTER 14 GENERAL PERTURBATION THEORY

Study on Active Micro-vibration Isolation System with Linear Motor Actuator. Gong-yu PAN, Wen-yan GU and Dong LI

Design, Control and Application of Modular Multilevel Converters for HVDC Transmission Systems by Kamran Sharifabadi, Lennart Harnefors, Hans Peter

ONE DIMENSIONAL TRIANGULAR FIN EXPERIMENT. Technical Advisor: Dr. D.C. Look, Jr. Version: 11/03/00

Chaper 2: Stress in beams

Study on Non-Linear Dynamic Characteristic of Vehicle. Suspension Rubber Component

Wavelet chaotic neural networks and their application to continuous function optimization

CHAPTER 10 ROTATIONAL MOTION

Boise State University Department of Electrical and Computer Engineering ECE 212L Circuit Analysis and Design Lab

p(z) = 1 a e z/a 1(z 0) yi a i x (1/a) exp y i a i x a i=1 n i=1 (y i a i x) inf 1 (y Ax) inf Ax y (1 ν) y if A (1 ν) = 0 otherwise

Variable Structure Control ~ Motor Control

Regulating Characteristics Analysis of Boiler Feed-water Pump when 600MW Unit Sliding-pressure Operating

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

Analysis of the Magnetomotive Force of a Three-Phase Winding with Concentrated Coils and Different Symmetry Features

TORQUE-SPEED ADAPTIVE OBSERVER AND INERTIA IDENTIFICATION WITHOUT CURRENT TRANSDUCERS FOR CONTROL OF ELECTRIC DRIVES

LECTURE 8-9: THE BAKER-CAMPBELL-HAUSDORFF FORMULA

JAB Chain. Long-tail claims development. ASTIN - September 2005 B.Verdier A. Klinger

A Particle Swarm approach for the Design of Variable Structure Stabilizer for a Nonlinear Model of SMIB System

Improved delay-dependent stability criteria for discrete-time stochastic neural networks with time-varying delays

The Study of Teaching-learning-based Optimization Algorithm

1 Derivation of Rate Equations from Single-Cell Conductance (Hodgkin-Huxley-like) Equations

Chapter Newton s Method

On Blow-Out of Jet Spray Diffusion Flames

The Exact Formulation of the Inverse of the Tridiagonal Matrix for Solving the 1D Poisson Equation with the Finite Difference Method

Markov Chain Monte Carlo (MCMC), Gibbs Sampling, Metropolis Algorithms, and Simulated Annealing Bioinformatics Course Supplement

The Feynman path integral

Robust Dynamic Programming for Discounted Infinite-Horizon Markov Decision Processes with Uncertain Stationary Transition Matrice

SAMPLE PAGES TO BE FOLLOWED EXACTLY IN PREPARING SCRIPTS. ADAPTIVE SPEED CONTROL OF PMSMs WITH UNKNOWN LOAD TORQUE

ENGINE AIR PATH FAULT DIAGNOSIS USING ADAPTIVE NEURAL CLASSIFIER. M. S. Sangha, D. L. Yu, J. B. Gomm

Hard Problems from Advanced Partial Differential Equations (18.306)

Kernel Methods and SVMs Extension

Energy Storage Elements: Capacitors and Inductors

Non-negative Matrices and Distributed Control

Indeterminate pin-jointed frames (trusses)

PERFORMANCE OF HEAVY-DUTY PLANETARY GEARS

Mechanical Systems Part B: Digital Control Lecture BL4

Numerical Solutions of a Generalized Nth Order Boundary Value Problems Using Power Series Approximation Method

Global Sensitivity. Tuesday 20 th February, 2018

Numerical modeling of a non-linear viscous flow in order to determine how parameters in constitutive relations influence the entropy production

Chapter - 2. Distribution System Power Flow Analysis

Chapter 24 Work and Energy

Structure and Drive Paul A. Jensen Copyright July 20, 2003

NON-LINEAR CONVOLUTION: A NEW APPROACH FOR THE AURALIZATION OF DISTORTING SYSTEMS

29th Monitoring Research Review: Ground-Based Nuclear Explosion Monitoring Technologies

Transcription:

J. Software Engneerng & Applcatons, 009, : 309-315 o:10.436/jsea.009.5040 Publshe Onlne December 009 (http://www.scrp.org/journal/jsea) 309 Responsveness Improvement of Ilng Spee Control for Automotve Usng SMC Yang ZHANG 1, Nobuo KURIHARA, Hroyuk YAMAGUCHI 3 1 Doctor of Scence Program n Mechancal Systems, Hachnohe Insttute of Technology, Hachnohe, Japan; Grauate School of Engneerng, Hachnohe Insttute of Technology, Hachnohe, Japan; 3 Department of System an Informaton, Hachnohe Insttute of Technology, Hachnohe, Japan. Emal: kurhara@h-tech.ac.jp Receve August 8 th, 009; revse September 8 th, 009; accepte September 15 th, 009. ABSTRACT To mprove the responsveness of engne spee control to sturbances, robust controls were nvestgate by smulaton. The ntake ar control system of a gasolne engne s a typcal nonlnear system, an the sturbances an parameter perturbatons are generally regare as beng the unstable factors wth regar to engne control. In ths paper, a Mean-Value Engne Moel (MVEM) wth sturbances an parameter perturbatons s nvestgate usng Slng Moe Control (SMC), whch s a form of varable structure control, wth a vew to aress nstablty n the le spee control process. The smulaton results confrme that, compare wth a conventonal PI (Proportonal-Integral) controller, the stablty of the le spee for an engne that s beng subjecte to sturbances, parameter varatons an backgroun nose s greatly mprove by the applcaton of SMC. Keywors: Gasolne Engne, Intake Ar Control, Slng Moe Control, Smulaton 1. Introucton Snce the begnnng of the 1st century, envronmental an energy problems have been becomng ncreasngly serous. A lack of fuel economy for automobles s consere to be the man cause of the global energy crss, an ths ssue nees to be aresse. Ile spee s the mnmum operatng spee of a combuston engne [1]. The pero at le occupes 30% of rvng tme for urban traffc. Furthermore, f the le spee can be reuce to 100 rpm (revolutons per mnute) by mprovng the control metho, fuel consumpton wll be reuce by to 5%. Therefore, sgnfcant fuel economy an emssons mprovements can be acheve by lowerng the le spee of an engne. In orer to acheve a relatvely lower le spee whle at the same tme preventng the engne from stallng, t s necessary to mantan a stable le spee n the presence of both known sturbances (e.g. statonary steerng an evaporaton-gas purge) an unknown sturbances. The automotve engne s a typcal nonlnear, tme-elay, tme-varyng parameter system. Recently there are many stues that apply control theores such as LQG, PID, an aaptve control to le-spee control []. In partcular, because Varable structure control s sutable for lnear an nonlnear, contnual an screte, certan an uncertan systems, the applcaton of slng moe control s regare as the soluton to the problem of mprovng the le-spee control. Nevertheless there are some stues are applyng ths theory [3,4]. However, le-spee control s suppose to be nvestgate practcally. In ths paper, le spee control was stue base on a non-lnear gasolne engne moel. As the PID (proportonal ntegral ervatve) controller s usually use for le spee control, the PID control an slng moe control (SMC) were employe to mprove le-spee control. The several sturbances such as control nput sturbance, torque sturbance an fuel sturbance were ae n the engne moel to certfy the responsveness an stablty of two control methos. An the operaton of the suen start also was also stue uner the fuel sturbance. The system s smulate by MATLAB /Smulnk. Relyng on result of smulatons, the robustness of le-spee control was mprove by usng SMC.. Ile Spee Control System In ths paper, we use an le spee control system whch was bult base on the mean value engne moel (MVEM) [5]. The moel conssts of three unts: an ntake manfol unt, a fuel mass unt, an a crankshaft unt. Because le spee control s the subject of ths stuy, an electronc Copyrght 009 ScRes

310 Responsveness Improvement of Ilng Spee Control for Automotve Usng SMC Fgure 1. Engne moel for le spee system throttle s necessary. The usual control process for le spee s llustrate as follows. The ntake ar flow s ajuste to acheve the set pont for the engne spee by ajustng the angle an poston of the electronc throttle. The crank angle sensor etects the engne spee, an the angle of the electronc throttle epens on the controller n the engne control unt. Next, the amount of ar supple to the cylner s ajuste usng the electronc throttle. Once that has been one, an amount of fuel proportonate to the ar flow s njecte nto the cylner. Thus, the torque prouce by fuel combuston mantans a constant engne spee. A moel of an le spee control system s shown n Fgure 1. Next, we wll escrbe the three parts of the engne moel by provng the equatons we use. The man ntal parameters of the engne moel were as follows: the engne splacement (V ) s 1.3 L; the fuel energy constant (H u ) was 43000 K J/kg; the gas constant of ar (R) was 0.0087 mbar/k.kg; the atmospherc temperature (T a ) was 93 K; the ntake manfol volume V was 0.000564 m 3 the atmospherc pressure (P a ) was 1.013 bar; an the moment of engne nerta was 5.638 kgm. Here we consere four factors that can easly make le spee control unstable: torque sturbance, fuel vapor sturbance, control nput sturbance, an parameter errors..1 Crankshaft Block n = H m In P P P In (1) u f f p The varable n n Equaton (1) s the engne spee (the unt use was set at rpm/1000); η s the thermal effcency; an P f, P p an P b are frctonal power, pumpng power, an loa power, respectvely. = n p n= 0.015+0.558 10.39 n 036 p=0.87+0.58p 0.39P = 0.01711.740.745 =0.7+0.04 0.00048 mbt P f, P p an P b are calculate usng the three emprcal equatons below. 3 P 1. 673n 0. 7n 0. 0135n f p 0969nP 006. n P 3 b Mbn 60 Kbn P. P Here, P s the manfol pressure; θ s the gnton angle; θ mbt s the mean best torque spark avance; λ s the excess ar coeffcent; an Mb an K b are both for the torque loa.. Fuel Block m ff X m f m ff f m fv 1 Xm f X 0. 7P 0. 055n 0. 68 mbt f 1. 35 0. 067n 1. 68 P 0. 85 0. 06n 0. 15 0. 56 (3) Here, m ff s the evaporaton fuel n the cylner; m f s the njecte fuel; m fv s the gasfe fuel; m f s the total amount of fuel n the cylner; X s the coeffcent of the fuel epost; an f s the tme constant for fuel evaporaton..3 Manfol Block at ap () P RT m m V PT /T (4) Here, T s the temperature n the manfol; m at s the ntake ar whch passes through the throttle; an m ap s the cylner ntake ar. m m m (5) at s a m s where s the ntake ar that passes through the by-pass valve m 0. 3988 u P 0. 01 (6) a m a 1 Here, s the ntake ar that passes through the throttle. 1 u 11. 4073cos u 180 0. 4087cos u 180 P r 0415. 1 P r 0. 415 P r 1 0. 415 0 P r 0. 415 r (7) Copyrght 009 ScRes

Responsveness Improvement of Ilng Spee Control for Automotve Usng SMC 311 Here, u s the openng of the throttle an Pr s the rato of P to P a. Where s the engne volumetrc effcency. v 095. 0075. P v T R 0. 4m m T 14. m T T PV 3. Lnearzaton ap at at a As s well known, the engne moel s a typcal non- near system. For the followng esgn for SMC, we neee to lnearze the engne moel to a state-space equaton at the operaton pont. In ths case, we selecte an engne spee of n an manfol pressure of P as two states. Here, u s the nput of avance angle gnton an u s the nput of the throttle, an both were provsonally consere as control nputs. x1 n u x u x P u A1 x B1 x 0 Ax Bx A 0 B x x A x B x u 1 x A x 0. 1841P 0. 039nP 0. 3178 0. 0516n 0. 006n A x 61. 895 1 18nP 15. 5 1. 397n B x m 680. 7 n B x RT V 1 n p ap (8) Havng sorte out the affne non-lnear equatons as state above, we then aresse the target operaton pont (x ), whch s gven as follows: x n P Therefore, the engne moel s lnearze n the vcnty of x. x xx u uu x AxBu 1 1 (9) a11 a1 b11 b1 (10) A B a a b b Next, we substtute the tangent lne for the curve lne accorng to the affne non-lnear equaton. f1 f1 x1 x n f A x x,u fn fn x1 x n f1 f1 u1 u n f B u x,u fn fn u1 u n a 11 0. 039 0. 0516 0. 005n a 1 0. 18410. 0039n a 1 0. 039 0. 0516 0. 005n a 61. 895 18n P x b1 b1 0 b1 n pm ap 680. 7 n b 155 (11) As a result, we were able to obtan the fnal matrces for A an B of the lnearze moel at two certan values of the abovementone states. 4. PID Control Desgn In the paper, we employe PI controller shown n Fgure. The gan of Kp an K are etermne by the Zegler- Nchols metho. 5. Slng Moe Control Desgn SMC s a type of varable structure control n whch the ynamcs of a nonlnear system are altere by the applcaton of a hgh-frequency swtchng control [6]. In other wors, SMC uses practcally nfnte gan to force the trajectores of a ynamc system to sle along the re- mportant, ro- strcte slng moe subspace. Ths s an bust control approach that proves an aaptve approach to tacklng the parametrc system, uncertan parametrc system, an uncertan sturbance system. If a swtchng surface s approprately esgne wth esrable characterstcs, the system wll exhbt esrable behavor when confne to ths swtchng surface. To pursue the target value, le-spee control can be shown as a servo system. Thus, the slng moe for a sngle-nput sngle-output Fgure. Block agram of PI controller Copyrght 009 ScRes

31 Responsveness Improvement of Ilng Spee Control for Automotve Usng SMC Fgure 3. Block agram of SMC system has been mae nto the block agram shown n Fgure, whch llustrates a close-loop SMC. The control nput s the sum of the lnear an non-lnear nputs. Here, n s the target engne spee, n s the actual engne spee, an the four kns of sturbances we have consere n ths paper have also been ae n the Fgure 3. 5.1 Controller In ths paper, we are only conserng the angle of the throttle as the control nput. Consequently, the followng system s consere. x 1 a11 a1 x1 b1 u x a1 a x b (1) a a n b u 11 1 1 a1 a P b In orer to apply SMC to the servo system, an expanson system s use n whch a new state, z, s assume as the value for the ntegraton of the fference between the target value an nput. The varable z s efne n Equaton (13) below. z rx n n (13) 1 The expanson servo system s gven n Equaton (14) below. z 0 1 0 z 0 1 x 1 0 a11 a 1 x1 0 u 0 r (14) x 0 a1 a x b 0 Equaton (14) can be rewrtten as follows: x Ax Bu Er (15) The swtchng functon s as follows: z x x Sx S x1 (16) The esgn for the swtchng surface s escrbe late. When the system s n slng moe, the swtchng functon s as follows: x 0 (17) When the system s n slng moe state, the ynamc characterstc s exhbte. When above the swtchng hyper-plane, the system mantans 0. Hence, by usng S x, substtutng Equaton (15) nto 0 gves: 0 SAx SBueq SE r (18) Takng the control law as: eq 1 u SB SAx SE (19) Equaton (19) gves the equvalent lnear control of a servo system wth slng moe. 5. Hyper-Plane For the system stablty margn, plane S s gven as follows: T r the swtchng hyper- S B P (0) where P s the postve efnte soluton of the Rccat equatons, so that: T PA A P PBB P Q 0 A A I Where s the stablty margn coeffcent, >0 s assume. To etermne the swtchng law, the Lyapunov func- ton canate wth s efne as follows: T (1) V 1 x () If the frst-orer ervatve V of the Lyapunov functon s a negatve efnte functon, can converge to 0. So the control nput s the sum of the lnear an non-lnear nputs. u u u (3) eq Here, non-lnear nput u nl s obtane as follows: u 1 nl k SB nl (4) If k >0, >0, k s the non-lnear nput swtchng gan an t s effcent n compensatng for unknown sturbances. To allevate chatter n the control output of the slng moe controller, we use as the output alteraton, whch s generally calle the smooth functon Copyrght 009 ScRes

Responsveness Improvement of Ilng Spee Control for Automotve Usng SMC 313 for replacng a conventonal sgnal functon. V k 0 (5) Accorng to Lyapunov s secon theorem on stablty, the hyper-plane measures the exstence of the slng m oe an reachablty. 6. Smulaton In orer to confrm robustness wth respect to a loa change sturbance urng le spee, the system was smulate uner the contons below. Frst, the ntal le spee of the engne was 700 rpm, gven the exstence of sturbances such as fuel purges an power wnow, a unt step nput was ae for the sturbance that results from an evaporaton-gas purge n the fuel moule an another unt step nput was ae for the sturbance that results from statonary steerng or other types of torque varatons n the crankshaft moule. The smulaton results are shown n Fgure 4. Secon, we consere the varable factors n a real engne, allowng us to nput the engne moel error, the Fgure 4. Responses for two sturbances Fgure 5. Responses for nput error an parameter pertur- batons control nput error, the gnton angle error, an the A/F (ar/fuel) rato nto the engne moel. Fgure 5 shows the results. From Fgure 4, we can see that f two entcal sturbances are loae at 5 secons an 1 secons, the control system s force to spen a conserable amount of tme reachng the target spee agan when PID s use. In other wors, the response tme s long an the compensaton effect s weak. In contrast, the sturbances were almost compensate for because of the relatvely large value of the compensatng gan k we use n the nonln- ear nput. A ±3% ranom error was ae n front of the engne moel as a control nput error. In aton, ±3% an ±1% ranom errors were ae to the stochometrc fuel ar rato an the gnton angle, respectvely. The results were as follows. From Fgure 5, we can see that there s lttle fluctuaton when PID s use, an the use of SMC oes not change the results. Thr, Moreover, as backgroun nose, the engne spee fluctuatons measure on an actual engne was ae to the engne moel, meanwhle, two sturbances as same as those n Fgure 4 were loae. The smulaton result s Copyrght 009 ScRes

314 Responsveness Improvement of Ilng Spee Control for Automotve Usng SMC shown n Fgure 6. The control system wth the slng moe controller s clearly more effectve aganst both of the two sturbances as f backgroun nose was loae. Fnally, we consere the responsveness an tracng ablty when the engne s mae a suen start from a lower le spee to a hgher spee such as 000rpm. As far as we know, there s usually lttle fuel loss n the suen start conton ue to some of fuel rops attache to the manfol, whch sometmes leas to the unesrable spee own. So we assume the suen start occurre at 6 secons an a step sturbance as fuel loss was loa at the tme. The smulaton results are shown n Fgure 7. Apparently, the transton of the suen start by SMC s faster than that by PI although the sturbance s loae. Base on the aforementone smulaton results, we can see that a control system wth a slng moe controller s more effectve wth respect to ether of the two sturbances, as well as to some errors n the actual en- provng the robustness of SMC. In aton, the gne, tracng ablty of le spee also appears to be mprove Fgure 7. Responses for a suen start from 700 rpm Fgure 6. Responses for loang engne-spee sgnal n the work conton uner the suen start wth the fuel sturbance. 7. Conclusons In ths paper we aresse the ssue of le spee control n orer to mprove the stablty of an engne s le spee an mprove ts fuel economy. To acheve hgh stablty an robustness for the le spee control system, the electronc throttle (whch regulates the ntake ar flow when an engne s lng) was taken as the control object, the engne was moularze by MATLAB/SIMULINK, an gven that the engne system s a typcal nonlnear system, the moel was lnearze at the operatng pont. Usng a lnearze state-space moel, we bult a hy- per-plane whch s aaptve to the controlle plant, after whch we also esgne a control nput for a controlle plant whch s the sum of the nonlnear an lnear nputs, so the SMC s constructe n m-fle. To prouce comparatve results, PI control was use, an n accorance wth Zegler-Nchols, the parameters of proporton an ntegral were ajuste on the ntal engne spee of 700 rpm. By usng SMC, the swtchng hyper-plane was esgne base on system zeros, an the system was esgne as a servo system n orer to acheve the target value. Compare wth conventonal PI control, the stablty aganst sturbances was mprove. Furthermore, the Copyrght 009 ScRes

Responsveness Improvement of Ilng Spee Control for Automotve Usng SMC 315 relatve robustness of SMC was confrme when an engne system was smulate uner parameter perturbatons an backgroun nose. Above all, t s much more potental to mprove le spee control an pursue low spee of the le spee by slng moe control. REFERENCES [1] H. Ano, Gasolne rect njecton engnes-present an future, Journal of Software Engneerng an Applcatons, Vol. 53, No. 9, 1999. [] F.-C. Hseh an B.-C. Chen, Aaptve Ile-spee control for spark-gnton engnes, SAE paper: 011197, 007. [3] B. Kwak an Y. Park, Robust vehcle stablty controller base on multple slng moe control, SAE paper: 011060, 001. [4] Y. Zhang, T. Koorkawa an N. Kurhara, Evaluaton of slng moe Ile-spee control for SI engnes, Journal of Software Engneerng an Applcatons, Vol. 40, No. 4, pp. 997 100, 009. [5] E. Henrcks, A. Chevaler an M. Jansen, et al., Moelng of the ntake manfol fllng ynamcs [J], SAE960037, pp. 1 5, 1996. [6] K. Nonam an H. Tan, Slng moe control of nonlnear robust control theory, Chapter 3, Corona publsher, Tokyo, 1994. Copyrght 009 ScRes