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Syllabus for

Academic year
SSY285 - Linear control system design  
Design av linjära reglersystem
 
Syllabus adopted 2016-02-01 by Head of Programme (or corresponding)
Owner: MPSYS
7,5 Credits
Grading: TH - Five, Four, Three, Fail
Education cycle: Second-cycle
Major subject: Automation and Mechatronics Engineering, Electrical Engineering, Chemical Engineering with Engineering Physics, Engineering Physics
Department: 32 - ELECTRICAL ENGINEERING


Teaching language: English
Open for exchange students: No
Block schedule: A
Maximum participants: 180

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0111 Design exercise + laboratory 3,0c Grading: UG   3,0c    
0211 Examination 4,5c Grading: TH   4,5c   14 Jan 2019 pm H   24 Apr 2019 am H   30 Aug 2019 am M  

In programs

MPEPO ELECTRIC POWER ENGINEERING, MSC PROGR, Year 1 (elective)
MPEPO ELECTRIC POWER ENGINEERING, MSC PROGR, Year 2 (elective)
MPISC INNOVATIVE AND SUSTAINABLE CHEMICAL ENGINEERING, MSC PROGR, Year 1 (compulsory elective)
MPISC INNOVATIVE AND SUSTAINABLE CHEMICAL ENGINEERING, MSC PROGR, Year 2 (elective)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 1 (compulsory)
MPAUT AUTOMOTIVE ENGINEERING, MSC PROGR, Year 2 (elective)
MPBME BIOMEDICAL ENGINEERING, MSC PROGR, Year 2 (elective)

Examiner:

Torsten Wik

Replaces

SSY160   Digital and multivariable control


Eligibility:


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

Course specific prerequisites

A basic course in automatic control including some familiarity with state space models.

Aim

The purpose of this course is to introduce and investigate techniques to analyze and design model based control systems. Linear state space modeling framework is applied to create a basis for different type of controller and state estimation methods. Starting from a pure state-feedback concept down to the optimal control methods, a large variety of feedback control techniques are presented with special attention on applications. Kalman filtering as an optimal way of state reconstruction is discussed in details. In this course, systems with multiple input and outputs are also analyzed from input-output point of view, through transfer function matrices. Disturbances, modeling uncertainties and robustness are also highlighted in the course. Exercises are playing an important role along the entire course.

Learning outcomes (after completion of the course the student should be able to)


  • Design control algorithms for linear time-invariant (LTI) dynamical systems with some state-space methods presented.
  • Become familiar with the concept of the state-space terminology.
  • Linearize nonlinear continuous time multivariable models (MIMO). Have some knowledge on deriving discrete time forms from continuous time LTI descriptions by a suitable sampling.
  • Understand model descriptions for linear time-invariant multivariable systems. Analyze these type of systems from the point of view controllability, observability and stability.
  • Explain and design discrete time multivariable state feedback controllers, based on linear quadratic optimization.
  • Explain, design, and analyze Kalman filters, and apply them for state estimation combined with controller design, i.e. LQG-control. Understand the principle of separation, analyze closed-loop optimal behavior.
  • Become familiar with the the basics of MIMO transfer functions and with their most important analytical properties. Understand concept for frequency domain analysis and synthesis of MIMO systems.
  • Define stability of dynamic systems under the presence of additive and multiplicative uncertainties. Provide with robustness uncertainty tests. Understand and design robust control techniques. Understand basic concept of decentralized and distributed control algorithms.

Content


  • Multivariable systems. MIMO (Multiple input-multiple output) vs. SISO (single input-single output) dynamical systems. Nonlinear dynamical systems and linearization. Basic control concepts (feedback, stability).
  • State-space realizations, state transformation. Continuous and discrete time descriptions. Discretization technique. Analytic properties of linear dynamical systems. Controllability, observability, multivariable poles and zeros, stability.
  • Analytic properties of linear dynamical systems. Controllability, observability, multivariable poles and zeros, stability.
  • Closed-loop control systems in state-space. Linear quadratic regulation
  • State observer design. Kalman filtering. Separation principle. Linear quadratic gaussian control (LQG).
  • Transfer function matrices. Sensitivity, robustness. Performance limitations in controlled systems.
  • Uncertainty and robustness. Disturbance rejecting robust control and state estimator. 
  • Introduction to large-scale system design. Distributed and decentralized control. Decoupling.


Organisation

The course is divided into a series of lectures, problem solving, and a mandatory project including assignments and laboratory sessions.

Literature

Control textbook and lecture slides.

Examination including compulsory elements

Written exam with TH grading; project with assignments and laboratory sessions (pass/fail).


Published: Mon 28 Nov 2016.