The student is able to…
- Linearize a nonlinear system around an operating point and obtain its state-space description and transfer function;
- Analyse the stability of a feedback loop via frequency response as well as algebraic methods;
- Synthesise controllers based on manual as well as automated (optimization-based) loop-shaping;
- Identify and handle fundamental design trade-offs and limitations in feedback controller synthesis;
- Discretise and implement a controller in computer and simulate the overall system behaviour;
- Determine the reachability and observability of a linear state-space model and synthesize an observer-based controller based on the separation theorem;
- Analyse the stability of a (nonlinear) system based on Lyapunov’s theorem, LaSalle’s invariance principle and passivity theorem;
- Synthesise a controller for a nonlinear robotic system in joint space as well as in operation space.
- Consider legal aspects in control design for autonomous robots (for MSc Robotics students)
Short description of course content:
- Linearization and state-space models, transfer function and frequency response, feedback and feed-forward, decoupling, loop gain and sensitivities, characteristic polynomial and internal stability, Bode and Nyquist stability criterions, stability margins, loop-shaping, mixed-sensitivity synthesis, waterbed effect and bandwidth limitations.
- Reachability and observability of a state-space model, pole placement, linear-quadratic regulator, Kalman filter, separation principle and observer-based controller synthesis.
- Lyapunov stability theory, LaSalle’s invariance principle, passivity and small-gain theorems, inverse dynamics compensation, feedback linearization, computed torque control, control in joint space and operation space.
- Sampling and discretization, sampling rate selection, computer implementation and simulation.
- Legal aspects of autonomous robots (for MSc Robotics students)
The course is taught with the following teaching methods:
Students can contact the teacher for any questions through Canvas and by pre-scheduling meetings.
- Lectures (about 16 lectures (32h) of frontal teaching in block 1B).
- Tutorials (about 9 tutorial (18h) of tutorials in block 1B).
The learning objectives are assessed as follows:
- Weekly assignments (40% of the total score) The weekly assignments partly prepare for the project assignment
- Project assignment (20% of the total score) The project contributes to the Challenge Based Learning portfolio for MSc-Robotics students.
- Written exam (40% of the total score)
Assumed previous knowledge
|Mandatory basic knowledge on differential equations, classical dynamical mechanical modelling, linear systems, Laplace and Fourier transforms, PID control.|
This knowledge can be obtained through the following UT Bachelor Modules:
• ME module Mechatronics (201700128)
• EE module Systems and Control (201700145)
• Minor Biorobotics (201800178)
|Master Biomedical Engineering|
|Master Electrical Engineering|
|Master Mechanical Engineering|
|Master Systems and Control|
|Master Interaction Technology||Required materials|
|Feedback Systems, K.J. Åström and R.M. Murray (freely available online)
|A Mathematical Introduction to Robotic Manipulation, R.M. Murray, Z. Li, S. Shankar Sastry (freely available online)
|Feedback Control Theory, J.C. Doyle, B.A. Francis, A.R. Tannenbaum (freely available online)
|Multivariable Feedback Control – Analysis and Design, S. Skogestad, I. Postlethwaite (Chapters 1, 2, 3 available online)
|Modern Control Engineering, K. Ogata
|Linear System Theory and Design, C. T. Chen, (available as e-book via UT library) ISBN: 978-0195117783|