Adaptive Control

Course Number: F032510 

Credit points/Contact hours: 2.0/36  

Semester: Fall

Course Title: Adaptive Control

School: School of Electronic, Information and Electrical Engineering

Instructor: Shaoyuan LI

Major Limit: Automatic Control; Industrial Automation

Prerequisite: Linear System Theory, Random Process, Matrix Theory

Course Discussion Hours: 36 (hours)       Course Laboratory Hours: 0 (hours)

Brief introduction of course contents:

Adaptive control is a continually developing control method and is getting more and more applied to practical industrial system control. This course is aimed at introducing the theoretical methods of adaptive control and its corresponding practical applications. And the main contents are: parameter identification technology, self-tuning regulators of deterministic systems, stochastic and predictive self-tuning regulators, model-reference adaptive control and analysis of adaptive control systems, etc. Moreover, this course also lays emphasis on the design of adaptive control in practical control systems, comprehensively introduces the design of some adaptive controllers of practical systems and thus shows the meanings of adaptive control for practical industrial systems.

Course Arrangements and Syllabus:

Chap. 1 What is Adaptive Control

1.1  Introduction

1.2  Linear Feedback

1.3  Effects of Process Variations

1.4  Robust Control

1.5  The Adaptive Control problem

Chap. 2 Real-time Parameter Estimation

2.1  Introduction

2.2  Least Squares and Regression Models

2.3  Estimating Parameters in Dynamic Systems

2.4  Experimental Conditions

2.5  Prior Information

Chap. 3 Deterministic Self-tuning Regulators

3.1  Introduction

3.2  Pole Placement Design

3.3  Indirect Self-tuning Regulators

3.4  Direct Self-tuning Regulators

Chap. 4 Stochastic and Predictive Self-tuning Regulators

4.1  Introduction

4.2  Minimum-variance Self-tuning Regulators

4.3  Generalized Minimum-variance STR

4.4  Adaptive Predictive Control

Chap. 5 Model-reference Adaptive Control

5.1  Introduction

5.2  The MIT Rule

5.3  Determination of Adaptation Gain

5.4  Lyapunov Theory

5.5  Design of MRAS using Lyapunov Theory

5.6  Adaptive Feedback Linearization

5.7  Adaptive Backstepping

Chap. 6 Multi-model Adaptive Control

6.1  Introduction

6.2  Multi-model Control System

6.3  Multi-model Predictive Control

6.4  Global System Stability Analysis

Course Assessment:

Students are required to master the course contents from the following three aspects:

1.      understand the essence of control systems; master the system properties and performance expectations of adaptive control and have a true understanding of course contents

2.      master the fundamental design methods and system analysis of adaptive control, mainly including self-tuning regulators, model-reference adaptive control and relevant identification of system models

3.      adaptive control scheme is not only a control algorithm but also a design scheme to system identification and control method now. And a lot of adaptive algorithms can be derived in many other design problems.

The course assessment is mainly based on the written exam and requires students to read some publications and write up feelings and thoughts of some problems.

References:

1. K.J. Astrom, Aadaptive Control, Science Press, 2003

2. Cengjin Han,aHhdddafsf Adaptive Control, TsinghuaUniversityPress, 1998