This thesis apply robust output feedback model predictive control (ROFMPC) for precision stop control of metropolitan train. For modeling the train, we consider actuation delay of train’s actuator, switching of actuators, nonlinearity of actuator’s saturation, so we consider a train as an nonlinear switching system. However, we consider a train as a uncertain linear system, because it is possible to obtain the exact train's acutator model. Therefore, ROFMPC is selected as controller for precision stop control of a train. We simulate precision stop control of a train with ROFMPC and evaluate the performance of ROFMPC. We compare ROFMPC and output feedback MPC (OFMPC) in performance of precision stop control of train. Also, we test for performance of ROFMPC under uncertain train’s mass and running resistance. Results show that ROFMPC is better than OFMPC and ROFMPC has robustness of uncertain train’s mass and running resistance. Because the state-feedback control's structure is included in robust MPC (RMPC)'s structure, we compare state-feedback control and RMPC in performance of simple model with disturbance. Also, we simulate the precision stop control of train using state-feedback control with state estimation because the result of simulation of comparison of RMPC and state-feedback control shows state-feedback control's step response is similar with RMPC's step response for the simple model with disturbance. As a result, state-feedback control with state estimation is appropriate for precision stop control of train and state-feedback control with state estimation is robust in performance of precision stop control of train under uncertain train's mass and running resistance. ⓒ 2017 DGIST
Table Of Contents
1 Introduction 1 -- 1.1 Motivation 1 -- 1.2 Previous work 2 -- 1.3 Approach to precision stop control 3 -- 1.4 Contribution 4 -- 1.5 Thesis outline 4 -- 2 Background information of metropolitan train system 5 -- 2.1 Automatic train control (ATC) 5 -- 2.2 Automatic train operation (ATO) 5 -- 2.3 Car types 5 -- 2.4 Train formation 6 -- 2.5 Precision stop marker (PSM) 6 -- 2.6 Velocity reference generation 7 -- 3 Modeling 9 -- 3.1 Train model 9 -- 3.2 Actuators of a train 11 -- 3.2.1 Traction motor 12 -- 3.2.2 Regenerative brake 13 -- 3.2.3 Air brake 14 -- 3.3 Nonlinearity of actuator’s saturation 15 -- 3.4 Switching of actuator 16 -- 3.5 Running resistance 17 -- 4 Model predictive control review 19 -- 4.1 Introduction 19 -- 4.2 Model predictive control 19 -- 4.3 Output feedback MPC 21 -- 4.4 Robust MPC 22 -- 4.5 Robust output feedback MPC 23 -- 5 Precision stop controller design 25 -- 5.1 Control strategy 25 -- 5.2 Controllability and observability 27 -- 5.3 Output feedback MPC design 28 -- 5.4 Robust output feedback MPC design 29 -- 6 Simulations 31 -- 6.1 Simulation scenario 31 -- 6.2 Reference for simulation 31 -- 6.3 Test for performance of ROFMPC 32 -- 6.4 Comparison of ROFMPC and OFMPC 32 -- 6.5 Test for performance of ROFMPC under uncertain train’s mass 32 -- 6.6 Test for performance of ROFMPC under running resistance 32 -- 6.7 Comparison of Control input and disturbance of train 33 -- 6.8 Calculation time of ROFMPC 33 -- 7 Conclusion 43 -- 8 Further discussion 45 -- 8.1 Introduction 45 -- 8.2 Comparison of RMPC and state-feedback control for simple model 45 -- 8.3 State-feedback control with state estimation for precision stop control of train 47 -- 8.4 Conclusion 48 -- 8.5 Future work 49 -- A Numerical simulation MATLAB codes 51 -- A.1 Precision stop control of train with ROFMPC 51 -- Bibliography 68
Research Interests
Resilient control systems; Control systems with nonlinear sensors and actuators; Quasi-linear control systems; Intelligent transportation systems; Networked control systems