This webpage aims at collecting links to interesting tutorials and courses related to CACSD and polynomial matrices. Name of the course Lecturer Place and date Description of the course Polynomial methods for robust control Didier Henrion, LAAS CNRS Toulouse, France Universidad de los Andes, Merida, Venezuela, October through November 2001 A course for graduate students or researchers with a background in linear control systems, linear algebra and convex optimization. The course focuses on the use of polynomials and polynomial matrices for the analysis and design of linear systems affected by parametric uncertainty. Polynomial Methods for Control Analysis and Design Michael Sebek, Czech Technical University, Prague, Czech Republic Technische Universitat Hamburg-Harburg, Summer Term 1999-2000 Polynomials and polynomial matrices play an important role in linear system theory, from first principles multivariable linear systems are modelled by sets of differential equations in the input u and the output y of the form D(d/dt)y(t) = N(d/dt)u(t). D and N are polynomial matrices in the differential operator d/dt. Polynomial matrix models do not replace state space and frequency domain descriptions but provides a powerful additional tool. The course shows how to systems may be analyzed and manipulated using this approach based on the mathematical properties of polynomial matrices. Many of the characteristics of polynomial matrices are reviewed, such as canonical and reduced forms, elementary operations and unimodular polynomial matrices, and divisors, primness and factorization. The closed-loop properties of single-input single-output and multivariable systems may conveniently be analyzed using polynomial matrix theory. It is only a small step from stability analysis to the pole assignment problem. The pole assignment problem is intrinsically linked to linear polynomial matrix equations of the form DX + NY = C in the unknown polynomials or polynomial matrices X and Y. This crucial equation is extensively discussed. Polynomial methods lend themselves very well to the frequency domain solution of famous and proven control system design methods such as LQG, H2 and deadbeat. These approaches to control system design and their application are thoroughly reviewed. Whenever appropriate the Polynomial Toolbox 2 for MATLAB will be used for on-line illustrations and demonstrations. Copies of the slides will be made available to the participants. Design Methods for Control Systems. Maarten Steinbuch, Gjerrit Meinsma, O. H. Bosgra, H. Kwakernaak Dutch Institute of Systems and Control, The Netherlands. The course presents "classical," "modern" and "post modern" notions about linear control system design. First the basic principles, potentials, advantages, pitfalls and limitations of feedback control are presented. An effort is made to explain the fundamental design aspects of stability, performance and robustness. Next, various well-known classical single-loop control system design methods, including Quantitative Feedback Theory, are reviewed and their strengths and weaknesses are analyzed. Likewise, LQ, LQG and control theory and some of their extensions are reviewed. Their potential for single- and multi-loop design is examined. The course includes a survey of design aspects that are characteristic for multivariable systems, such as interaction, decoupling and input-output pairing. After a thorough presentation of structured and unstructured uncertainty model design methods based on H-infinity-optimization (in particular, the mixed sensitivity problem) and mu-synthesis are presented.