책소개

In this paper, explicit Model Predictive Control(MPC) is employed for automated<br>lane-keeping systems. MPC has been regarded as the key to handle such<br>constrained systems. However, the massive computational complexity of MPC,<br>which employs online optimization, has been a major drawback that limits the<br>range of its target application to relatively small and/or slow problems. Explicit<br>MPC can reduce this computational burden using a multiparametric quadratic<br>programming technique(mp-QP). The control objective is to derive an optimal front<br>steering wheel angle at each sampling time so that autonomous vehicles travel<br>along desired paths, including straight, circular, and clothoid parts, at high entry<br>speeds. In terms of the design of the proposed controller, a method of choosing<br>weighting matrices in an optimization problem and the range of horizons for<br>path-following control are described through simulations. For the verification of<br>the proposed controller, simulation results obtained using other control methods<br>such as MPC, Linear-Quadratic Regulator(LQR), and driver model are employed,<br>and CarSim, which reflects the features of a vehicle more realistically than<br>MATLAB/Simulink, is used for reliable demonstration.

저자소개

목차

제 1편 : SIMULINK 기본편<br><br>1.1 SIMULINK의 시작 1<br>블록의 연결 5<br>블록 파라미터의 설정 7<br>시뮬레이션 파라미터 (Configuration Parameters)의 설정 8<br>시뮬레이션의 수행 9<br>블록 파라미터의 표시 9<br>복수 데이터의 표시 11<br>2.2 동적 시뮬레이션 13<br>이차 미분방정식 17<br>선형 상태변수 모델 23<br>DC 모터의 시뮬레이션 24<br>함수 블록의 사용 29<br>차분방정식(difference equation)의 모델링 34<br>Subsystem(부시스템)의 구성 37<br><br>제 2편 : 연구논문<br>Analysis of explicit model predictive control for path-following control<br><br>1. Introduction 41<br>2. Vehicle model 44<br>3. Explicit model predictive control 45<br>4. Controller design 49<br>5. Explicit MPC controller design 50<br>6. Results 53<br>7. Conclusions 56<br>8. References 58