Syllabus for |
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| MKM095 - Model based controls systems |
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| Owner: TAUTA |
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4,0 Credits (ECTS 6) |
| Grading: TH - Five, Four, Three, Not passed |
| Level: A |
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Department: 32 - ELECTRICAL ENGINEERING
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Teaching language: Swedish
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Credit distribution |
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Examination dates |
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Sp1 |
Sp2 |
Sp3 |
Sp4 |
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No Sp |
| 0100 |
Laboratory |
1,0 c |
Grading: UG |
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1,0 c
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| 0200 |
Examination |
3,0 c |
Grading: TH |
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3,0 c
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17 Mar 2007 pm V, |
20 Jan 2007 pm V, |
30 Aug 2007 am M |
In programs
TAUTA AUTOMATION AND MECHATRONICS ENGENEERING, Year 4 (elective)
Examiner:
Professor
Bo Egardt
Eligibility:
For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.
Course specific prerequisites
Introductory control course.
Aim
Aim and Goal:
The aim of the course is to give knowledge in control useful for
problems of larger complexity than a simple control loop. By
introducing state space descriptions new possibilities arise:
* Insights of dynamic properties of complex, eg nonlinear and
multi-input and multi-output, systems by computer simulation.
* Give design methods where the performance of the closed loop system
is formulated as a criterion which should be optimized (optimal
control).
* Improved accuracy and reliability by using estimation techniques,
eg, non measurable signals are estimated using other signals and
model of the system. With these techniques it is also possible to
detect faults among the sensor signals.
Insights of the techniques to estimate the state of a system using a
model and available sensor signals, and to use the estimate for
closed loop control. Stochastic descriptions of disturbances and
noise are used and methods to minimize their influence are introduced.
Content
Tools for modeling, analysis and design of dynamic systems: Modeling of linear and
nonlinear systems. Tools for nonlinear systems, phase plane and
describing function. Computer simulation. Linearisation. Linear state
space techniques for continuous and discrete time systems. The
solution of the state space description, sampling of continuous time
systems, dead time. Analysis of properties of dynamic systems,
features such as stability, observable and controllable systems,
duality. Change of state space representation and canonical forms.
State feedback: designed by closed loop specifications, integral
action and feed forward compensation of the reference signal. Design
by quadratic criteria functions, linear quadratic optimal control
(LQ).
State estimation using observers: design by pole placement and by
criterion minimization with stochastic assumptions on disturbances
and noise (Kalman filter).
Control based on estimated states: Linear Quadratic Gaussian
controller (LQG). Robustness and sensitivity of LQG controllers.
Design and implementation of continuous and discrete time systems
using Matlab and Control System Toolbox.
Application example: Electro-mechanical actuators, navigation and
control of autonomous vehicles, active damping of disturbances and vibrations
Hand-in Assignments:
1) Nonlinear systems and discrete time simulation
2) Analysis and design using Matlab
Laboratory exercises:
Modeling and control of a robot arm
Organisation
Lectures and problem solving sessions.
Hand-in assignments and laboratory session (mandatory).
Literature
B.Schmidtbauer, Modellbaserade reglersystem (Studentlitteratur, 1999, in Swedish) or alternative to be defined.
Examination
Written exam.