Course unit, curriculum year 2023–2024
AUT.350
LQG Control with Matlab, 5–7 cr
Tampere University
- Description
- Completion options
Teaching periods
Active in period 1 (1.8.2023–22.10.2023)
Active in period 2 (23.10.2023–31.12.2023)
Active in period 3 (1.1.2024–3.3.2024)
Course code
AUT.350Language of instruction
EnglishAcademic years
2022–2023, 2023–2024Level of study
Advanced studiesGrading scale
General scale, 0-5Persons responsible
Responsible teacher:
Terho JussilaResponsible organisation
Faculty of Engineering and Natural Sciences 100 %
Coordinating organisation
Automation Technology Studies 100 %
Core content for 5 cr
- Quadratic norms of vectors, matrixces, functions and systems
- Quadratic non-norm performance indices
- Random variables & stochastic performance indices like variances of the system responses.
- Weighted Least Squares
- Use of suitable matrix factorizations, Lagrange multipliers
- State-feedback, Kalman predictor and filter
- Stationary LQG regulation and control
- Deterministic finite horizon LQ regulation
- Parseval Theorems
- Exponential time-weighting
- Spectral factorization.
- Transfer function matrix methods. Frequency response studies. Principal gains and Hinf norm. Studies of robust stability using unstructured uncertainty, too.
- Introduction to DT Square Root Algorithms.
Complementary knowledge for 5 cr
- Quadratic costs with polynomial time weighting
- Balanced realization of state space models
- LQ methods for model reduction
- LQ design of partial state feedback regulation
- LQ designs using Hamilton matrix
Specialist knowledge for 5 cr
- Use of Matlab tools ode45, dde23 (for differential equation models) & fzero, fsolve (to solve algebraic equations) & fminbnd, fminsearch (to minimize and maximize algebraic functions).
Extension C for an extra 1 cr
- Introduction to Model Predictive Control of MIMO plants
- Generalized Predictive Control of SISO plants
- Constrained Model Predictive Control of MIMO plants
Extension D for an extra 1 cr
- DT Kalman Filter in Recursive Identification
- Morf-Kailath Algorithms for DT Kalman Filter
- Special Lyapunov and Riccati Algorithms
Learning outcomes
Prerequisites
Further information
Learning material
Equivalences
Studies that include this course
Completion option 1
Participation in teaching
28.08.2023 – 20.01.2024
Active in period 1 (1.8.2023–22.10.2023)
Active in period 2 (23.10.2023–31.12.2023)
Active in period 3 (1.1.2024–3.3.2024)