특정 점의 추적을 위한 모델예측제어가 결합된 새로운 반복학습제어 기법

학위논문 (박사)-- 서울대학교 대학원 : 화학생물공학부, 2017. 2. 이종민.본 논문에서는 제약조건이 있는 다변수 회분식 공정의 제어를 위해 반복학습제어(Iterative learning control, ILC)와 모델예측제어(Model predictive control, MPC)를 결합한 반복학습 모델예측제어(Iterative learning model predictive control, ILMPC)를 다룬다. 일반적인 ILC는 모델의 불확실성이 있더라도 이전 회분의 정보를 이용해 학습하기 때문에 출력을 기준궤적에 수렴시킬 수 있다. 하지만 기본적으로 개루프 제어이기 때문에 실시간 외란을 제거할 수 없다. MPC는 이전 회분의 정보를 이용하지 않기 때문에 모든 회분에서 동일한 성능을 보이며 모델의 정확도에 크게 의존한다. 본 논문에서 ILC와 MPC의 모든 장점을 포함하는 ILMPC를 제안한다. 많은 회분식 또는 반복 공정에서 출력은 모든 시간에서의 기준궤적을 추적할 필요가 없다. 따라서 본 논문에서는 원하는 점에만 수렴할 수 있는 새로운 ILMPC 기법을 제안한다. 제안한 기법을 사용할 경우 원하는 점을 지나는 기준궤적을 만드는 과정이 필요 없게 된다. 또한 본 논문은 점대점 추적, 반복 학습, 제약조건, 실시간 외란 제거 등의 성능을 보이기 위한 다양한 예제를 제공한다.In this thesis, we study an iterative learning control (ILC) technique combined with model predictive control (MPC), called the iterative learning model predictive control (ILMPC), for constrained multivariable control of batch processes. Although the general ILC makes the outputs converge to reference trajectories under model uncertainty, it uses open-loop control within a batchthus, it cannot reject real-time disturbances. The MPC algorithm shows identical performance for all batches, and it highly depends on model quality because it does not use previous batch information. We integrate the advantages of the two algorithms. In many batch or repetitive processes, the output does not need to track all points of a reference trajectory. We propose a novel ILMPC method which can only consider the desired reference points, not an entire reference trajectory. It does not require to generate a reference trajectory which passes through the specific desired points. Numerical examples are provided to demonstrate the performances of the suggested approach on point-to-point tracking, iterative learning, constraints handling, and real-time disturbance rejection.1. Introduction 1 1.1 Background and Motivation 1 1.2 Literature Review 4 1.2.1 Iterative Learning Control 4 1.2.2 Iterative Learning Control Combined with Model Predictive Control 15 1.2.3 Iterative Learning Control for Point-to-Point Tracking 17 1.3 Major Contributions of This Thesis 18 1.4 Outline of This Thesis 19 2. Iterative Learning Control Combined with Model Predictive Control 22 2.1 Introduction 22 2.2 Prediction Model for Iterative Learning Model Predictive Control 25 2.2.1 Incremental State-Space Model 25 2.2.2 Prediction Model 30 2.3 Iterative Learning Model Predictive Controller 34 2.3.1 Unconstrained ILMPC 34 2.3.2 Constrained ILMPC 35 2.3.3 Convergence Property 37 2.3.4 Extension for Disturbance Model 42 2.4 Numerical Illustrations 44 2.4.1 (Case 1) Unconstrained and Constrained Linear SISO System 45 2.4.2 (Case 2) Constrained Linear MIMO System 49 2.4.3 (Case 3) Nonlinear Batch Reactor 53 2.5 Conclusion 59 3. Iterative Learning Control Combined with Model Predictive Control for Non-Zero Convergence 60 3.1 Iterative Learning Model Predictive Controller for Nonzero Convergence 60 3.2 Convergence Analysis 63 3.2.1 Convergence Analysis for an Input Trajectory 63 3.2.2 Convergence Analysis for an Output Error 65 3.3 Illustrative Example 71 3.4 Conclusions 75 4. Iterative Learning Control Combined with Model Predictive Control for Tracking Specific Points 77 4.1 Introduction 77 4.2 Point-to-Point Iterative Learning Model Predictive Control 79 4.2.1 Extraction Matrix Formulation 79 4.2.2 Constrained PTP ILMPC 82 4.2.3 Iterative Learning Observer 86 4.3 Convergence Analysis 89 4.3.1 Convergence of Input Trajectory 89 4.3.2 Convergence of Error 95 4.4 Numerical Examples 98 4.4.1 Example 1 (Linear SISO System with Disturbance) 98 4.4.2 Example 2 (Linear SISO System) 104 4.4.3 Example 3 (Comparison between the Proposed PTP ILMPC and PTP ILC) 107 4.4.4 Example 4 (Nonlinear Semi-Batch Reactor) 113 4.5 Conclusion 119 5. Stochastic Iterative Learning Control for Batch-varying Reference Trajectory 120 5.1 Introduction 121 5.2 ILC for Batch-Varying Reference Trajectories 123 5.2.1 Convergence Property for ILC with Batch-Varying Reference Trajectories 123 5.2.2 Iterative Learning Identification 126 5.2.3 Deterministic ILC Controller for Batch-Varying Reference Trajectories 129 5.3 ILC for LTI Stochastic System with Batch-Varying Reference Trajectories 132 5.3.1 Approach1: Batch-Domain Kalman Filter-Based Approach 133 5.3.2 Approach2: Time-Domain Kalman Filter-Based Approach 137 5.4 Numerical Examples 141 5.4.1 Example 1 (Random Reference Trajectories 141 5.4.2 Example 2 (Particular Types of Reference Trajectories 149 5.5 Conclusion 151 6. Conclusions and Future Works 156 6.1 Conclusions 156 6.2 Future work 157 Bibliography 158 초록 170Docto

Next generation automotive embedded systems-on-chip and their applications

It is a well known fact in the automotive industry that critical and costly delays in the development cycle of powertrain1 controllers are unavoidable due to the complex nature of the systems-on-chip used in them. The primary goal of this portfolio is to show the development of new methodologies for the fast and efficient implementation of next generation powertrain applications and the associated automotive qualified systems-on-chip. A general guideline for rapid automotive applications development, promoting the integration of state-of-the-art tools and techniques necessary, is presented. The methods developed in this portfolio demonstrate a new and better approach to co-design of automotive systems that also raises the level of design abstraction.An integrated business plan for the development of a camless engine controller platform is presented. The plan provides details for the marketing plan, management and financial data.A comprehensive real-time system level development methodology for the implementation of an electromagnetic actuator based camless internal combustion engine is developed. The proposed development platform enables developers to complete complex software and hardware development before moving to silicon, significantly shortening the development cycle and improving confidence in the design.A novel high performance internal combustion engine knock processing strategy using the next generation automotive system-on-chip, particularly highlighting the capabilities of the first-of-its-kind single-instruction-multiple-data micro-architecture is presented. A patent application has been filed for the methodology and the details of the invention are also presented.Enhancements required for the performance optimisation of several resource properties such as memory accesses, energy consumption and execution time of embedded powertrain applications running on the developed system-on-chip and its next generation of devices is proposed. The approach used allows the replacement of various software segments by hardware units to speed up processing.1 Powertrain: A name applied to the group of components used to transmit engine power to the driving wheels. It can consist of engine, clutch, transmission, universal joints, drive shaft, differential gear, and axle shafts

Frequency Domain Based Analysis and Design of Norm-Optimal Iterative Learning Control

In this thesis, novel frequency domain based analysis and design methods on Norm-Optimal Iterative Learning Control (NO-ILC) are developed for Single-Input-Single-Output (SISO) Linear Time Invariant (LTI) systems. Modeling errors in general degrade the convergence performance of NO-ILC and hence ensuring Robust Monotonic Convergence (RMC) against model uncertainties is important. Although the robustness of NO-ILC has been studied in the literature, determining the allowable range of modeling errors for a given NO-ILC design is still an open research question. To fill this gap, a frequency domain analysis with a multiplicity formulation of model uncertainty is developed in this work to quantify and visualize the allowable modeling errors. Compared with the traditional formulation, the proposed new uncertainty formulation provides a less conservative representation of the allowable model uncertainty range by taking additional phase information into account and thus allows for a more complete evaluation of the robustness of NO-ILC. The analysis also clarifies how the RMC region changes as a function of NO-ILC weighting terms and therefore leads to several frequency domain design tools to achieve RMC for given model uncertainties. Along with this frequency domain analysis, rather than some qualitative understanding in the literature, an equation quantitatively characterizing the fundamental trade-off of NO-ILC with respect to robustness, convergence speed and steady state error at each frequency is presented, which motivates the proposed loop-shaping like design methods for NO-ILC to achieve different performance requirements at various frequencies. The proposed analysis also demonstrates that NO-ILC is the optimal solution for general LTI ILC updating laws in the scope of balancing the trade-off between robustness, convergence speed and steady state error.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137007/1/gexinyi_1.pd