Junho Lee, Hyuk-Jun Chang |

- 2020-07-10

- 13 M

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In this paper, explicit Model Predictive Control(MPC) is employed for automated

lane-keeping systems. MPC has been regarded as the key to handle such

constrained systems. However, the massive computational complexity of MPC,

which employs online optimization, has been a major drawback that limits the

range of its target application to relatively small and/or slow problems. Explicit

MPC can reduce this computational burden using a multiparametric quadratic

programming technique(mp-QP). The control objective is to derive an optimal front

steering wheel angle at each sampling time so that autonomous vehicles travel

along desired paths, including straight, circular, and clothoid parts, at high entry

speeds. In terms of the design of the proposed controller, a method of choosing

weighting matrices in an optimization problem and the range of horizons for

path-following control are described through simulations. For the verification of

the proposed controller, simulation results obtained using other control methods

such as MPC, Linear-Quadratic Regulator(LQR), and driver model are employed,

and CarSim, which reflects the features of a vehicle more realistically than

MATLAB/Simulink, is used for reliable demonstration.

1 : SIMULINK ⺻

1.1 SIMULINK 1

5

Ķ 7

ùķ̼ Ķ (Configuration Parameters) 8

ùķ̼ 9

Ķ ǥ 9

ǥ 11

2.2 ùķ̼ 13

̺й 17

º 23

DC ùķ̼ 24

Լ 29

й(difference equation) 34

Subsystem(νý) 37

2 :

Analysis of explicit model predictive control for path-following control

1. Introduction 41

2. Vehicle model 44

3. Explicit model predictive control 45

4. Controller design 49

5. Explicit MPC controller design 50

6. Results 53

7. Conclusions 56

8. References 58