Using a packet switching network is an effective way to construct systems that use multi-actuators and sensors; however, the structural characteristics of a packet switching network, i.e., packet loss and packet transient time delay, affect the control performance of the systems that use a packet switching network. The model based predictive control scheme compensates well for the effect of the packet transient time delay, and the effect of packet loss as long as the resulting maximum delay is known; however, existing work has not considered external disturbance. External disturbance represents the unexpected control input factors on the operation environment. This thesis analyzes the effects of external disturbance on the model based predictive control system. In conventional feedback control systems, disturbance does not affect system stability; however, in systems with model based predictive control method, external disturbance may destabilize the control system. Existing work on model based predictive control does not properly analyze the above mentioned phenomenon. We use a lifting technique with current states, delayed states, and prediction error, in order to analyze closed loop system stability and performance with external disturbance. We show that the system matrix with lifted states define the closed loop stability. The results are validated experimentally on a Ball Balancer control system with time delay. The control system with a controller designed using the stability analysis of this thesis, exhibits stable behavior despite external disturbance and compensates for the performance loss due to time delay. ⓒ 2016 DGIST
Table Of Contents
1 Introduction 1 -- 1.1 Risk Factors of NCSs 2 -- 1.2 Motivation 2 -- 1.3 Thesis Outline 3 -- 2 Background 5 -- 2.1 Time Delay in Discrete Systems 6 -- 2.2 Tracking Control Systems 9 -- 2.3 Model Based Predictive Control Concept 11 -- 3 Stability Analysis for Model Based Predictive Control Systems 13 -- 3.1 Effect of External Disturbance 14 -- 3.2 Remodeling to Augmented Systems 16 -- 3.3 Numerical Simulation Results 17 -- 4 Validation with a Ball Balancer 25 -- 4.1 System Construction 26 -- 4.2 Experimentation Results 31 -- 5 Conclusion 35 -- A Numerical Simulation Matlab Codes 37 -- A.1 Model based predictive control system setup 37 -- A.2 Find safety reason 39 -- A.3 Simulink file 40 -- Bibliography 45
Research Interests
Resilient control systems; Control systems with nonlinear sensors and actuators; Quasi-linear control systems; Intelligent transportation systems; Networked control systems