General Information on Graduate Level Courses:

  • All courses are elective with the exception of Scientific Methodology, which is compulsory.
  • All courses are offered to Master’s and Ph.D. students.
  • All courses belong the the general area of Control, Automation and System Engineering.
  • The credits of all courses correspond only to the theoretical work load.
  • Not all courses all offered in all academic semesters.

Courses Offered in each Phase

List of Courses [Click on the Title to Access the Course Content]:

  • Scientific Methodology (2 credits)

    Syllabus: Science, ethics and the society. Graduate level research. Research documentation. Research topic: problem formulation, research hypothesis, objectives, and theoretical milestone. Methodological procedures: data collection, validation, analysis and discussion of results. Research project. Work planning and structure. Paper and dissertation writing.


    • Fourez, G. A Construção das Ciências – Introdução à Filosofia e à Ética das Ciências, Editora Unesp, 1995.
    • Waslawick, R. S. Metodologia de Pesquisa para Ciência da Computação, Editora Campus, 2009.
    • Bianchetti, L.; Machado, A. N. N. Bússola do Escrever, Editora da UFSC, 2002.
    • Gil, A. C . Como Elaborar Projetos de Pesquisa, 5a. edição, Editora Atlas, 2010.

  • Techniques for Implementing Automated Systems (2 credits)

    Syllabus: Architecture and programming of multiprocessor systems. Programming techniques for digital control algorithms.  Interface with external devices. Software performance estimation. Concurrent programming. Real-time operating systems.


    • S. Heath. Embedded Systems Design. Newnes, 2003.
    • P. Koopman. Better Embedded System Software. Drumnadrochit Education LLC, 2010.
    • B. Nichols, D. Buttlar, J. P. Farrell. Pthreads Programming. O ́Reilly & Associates, 1996.
    • A. Burns, A. Wellings. Real-Time Systems and Programming Languages. Fourth Edition. Addison Wesley Longman, 2009.

    Remark: this course is suitable mainly for candidates who do no have background in engineering.

  • Fundamentals of Control and Automation (2 credits)

    Syllabus: Concepts about the automation process: measurement, actuation and control. Introduction do discrete event systems. Hierarchy in automation systems. Programmable Logic Controllers (PLCs) and their applications in automation.


    • Franklin, Gene et al. Feedback Control of Dynamic Systems. 4a. Edition, Prentice-Hall, 2002.
    • Schleicher, Manfred e Blasinger, Frank. Control Engineering – A Guide for Beginners. 3a. Edition, Jumo Gmbh & Co., 2003.
    • Webb, John et all. Programmable Logic Controllers: Principles and Applications. 4th edition, Prentice-Hall, 1998.
    • Stenerson, Jon. Fundamentals of Programmable Logic Controllers, Sensors and Communications. 2nd edition, Prentice-Hall, 1999.
    • Rohner, Peter. Automation With Programmable Logic Controllers, MacMillan, 1996.
    • De Oliveira, Júlio César Peixoto. Controlador Programável. Makron Books do Brasil, São Paulo, 1993.

    Remarks: this course is offered mainly to students with background in computer science, mechanical engineering or electrical engineering. It is not recommended to candidates holding degrees in Control and Automation Engineering because its syllabus covers basic knowledge in this area.

  • Stochastic Processes (2 credits)

    Syllabus: Review of probability. Random vectors. Gaussian Distributed Random Variables and the Central Limit. Stochastic Processes in Discrete Time.


    • Leon-Garcia, A. Probability, Statistics and Random Processes for Electrical Engineering, 3a Edition. Prentice Hall, 2008.
    • Papoulis, A., Pillai, S. U. Probability, Random Variables, and Stochastic Processes, 4a Edition, McGraw-Hill, 2002.
    • Gubner, J. A. Probability and Random Processes for Electrical and Computer Engineers, Cambridge University Press, 2006.
    • Kay, S. M. Intuitive Probability and Random Processes Using Matlab, Springer, 2006.

  • Fundamentals of Discrete Mathematics for Control and Automation (2 credits)

    Syllabus: Introduction to discrete mathematics. Relations: definition of partial order. Logic: motivation and principles. Propositional Logic: syntax, semantics and calculus. Logic of Predicates: syntax, semantics and calculus. Methods of Proof: tableaux, natural deduction, and resolution. Other Logics. Logic programming.


    • Alencar F., E. Teoria Elementar dos Conjuntos. Editora Nobel, 1990.
    • Rosen, K. H. Discrete Mathematics and Its Applications. McGraw-Hill, 1991.
    • Cassandras, C. G.; Lafortune, S. Introduction to Discrete Event Systems. Kluwer Academic, 1999.
    • James L. Hein. Discrete Structures, Logic, and Computability. Jones and Bartlett, 2010.
    • Frances Howard-Snyder; Daniel Howard-Snyder; Ryan Wasserman. The Power of Logic. McGraw-Hill, 2009.
    • Fitting, M. First-Order Logic and Automated Theorem Proving. Springer Verlag, 1990.

  • Formal Methods for Discrete Automation Systems (2 credits)

    Syllabus: Automata: languages and regular expressions, finite state automata, deterministic automata, theorem of Nerode, minimization of automata. Petri nets: definition, modeling, properties, and high level Petri nets. Extensions: timed automata, time Petri net.


    • J.E. Hopcroft; R. Motwani; J. Ullman. Introduction to Automata Theory, Languages, and Computation. Addison Wesley, 2nd ed., USA, 2001.
    • J. Carrol; D. Long. Theory of Finite Automata: with an Introduction to formal languages. Prentice-Hall International, USA, 1989.
    • M. Diaz. Petri Nets: Fundamental Models, Verification and Applications. John Wiley & Sons,USA, 2010.
    • W. Reisig. Understanding Petri Nets: Modeling Techniques, Analysis Methods, Case Studies. Springer-Verlag, De,2013.

  • Discrete Event Systems I (2 credits)

    Syllabus: basic concepts in supervisory control:  generators, automata composition, the supervisory control problem (SCP), controllability and the existence of non-blocking supervisors, optimal supervisor, algorithms for supervisor synthesis, modular supervisor and test of non conflict. Verification: principles, temporal logic, model checking, equivalence, languages oriented to verification and tools.


    • W. M. Wonham. Notes on Control of Discrete-Event Systems. Dept. of Electrical & Computer Eng., University of Toronto, 2003.
    • G. Cassandras; S. Lafortune. Introduction to Discrete Event Systems. Kluwer Academic Publishers, USA, 1999.
    • E. Clarke; O. Grumberg; D.A. Peled. Model Checking. M.I.T. Press, 2003
    • B. Bérard; M. Bidoit; A. Finkel; F. Laroussinié; A. Petit; L. Petrucci; Ph. Sschnoebelen. Systems and Software Verification: Model-checking Techniques and Tools. Springer-Verlag Ed., 2001.

  • Discrete Event Systems II (2 credits)

    Syllabus: Advanced control: hierarchical control, game-based automata. Modeling and analysis of reactive systems: synchronous approach, imperative languages (Esterel) and data flow (Lustre). Notions of hybrid systems. Applications of control and verification.


    • W. M. Wonham. Notes on Control of Discrete-Event Systems. Dept. of Electrical & Computer Eng., University of Toronto, 2003.
    • N. Halbwachs. Synchronous programming of reactive systems. Kluwer Academic Pub. 1993
    • D. Potop-Butucaru; S. A. Edwards; G. Berry. Compiling Esterel. Springer-Verlag. 2007
    • P. Tabuada. Verification and Control of Hybrid Systems. Springer-Verlag. 2009

  • Communication Networks for Control and Automation (2 credits)

    Syllabus: Principles of digital communication: topologies, multiplexing, and modulation. Architectures and patterns. The OSI reference model from ISO. The Internet architecture: general concepts, extensions (IP multicast, IPv6, IP QoS). Flow control: congestion control and queue management in routers. Protocols for multimidia communication.


    • Larrie Peterson, Brucie Davie. Computer Networks: A Systems Approach. Morgan Kaufmann, 2007.
    • James F. Kurose, Keith W. Ross. Computer Networking: A Top-Down Approach. Addison-Wesley, 2012.
    • Marcelo Stemmer. Redes Locais Industriais – A Integração da Produção Através das Redes de Comunicação. Ed. da UFSC, 2010.

  • Industrial Networks (2 credits)

    Syllabus: Industrial network hierarchy. Desirable characteristics of industrial networks: temporal behavior, robustness, connectivity, interoperability, standardization. Standardization projects: IEEE 802, MAP/TOP, Fieldbus (PROFIBUS, FIP, Foundation Fieldbus). Wireless networks (IEEE 802.11). General view of products and applications.


    • Marcelo Stemmer. Redes Locais Industriais – A Integração da Produção Através das Redes de Comunicação. Ed. da UFSC, 2010.
    • Andrew S. Tanenbaum. Computer Networks. Prentice Hall, 2010.
    • James F. Kurose, Keith W. Ross. Computer Networking: A Top-Down Approach. Addison-Wesley, 2012.

  • Performance Evaluation (2 credits)

    Syllabus: Introduction; performance indices; stochastic processes; Markov chains with discrete and continuous time models; queue theory; applications in networks, production systems, and other systems.


    • R. Jain. The Art of Computer Performance System Analysis. John Wiley & Sons, 1991.
    • G. Bolch, S. Greiner, Meer, K. Trivedi. Queueing Networks and Markov chains: Modeling and Performance Evaluation with Computer Science Applications. John Wiley & Sons, 1998.
    • N. Gunther. The Practical Performance Analyst, Prentice-Hall, 1998.
    • L. Kleinrock. Queueing Systems (Vol. 1 & 2) John Wiley & Sons, 1975.
    • E. Lazowska et alli. Quantitative Systems Performance. Prentice-Hall, l984.
    • K. Trivedi. Probability, Statistics with Reliability, Queuing, and Computer Science Applications. Prentice-Hall, 1982.
    • A. Neely. Business Performance Measurement: Unifying Theory and Integrating Practice, Cambridge Press, 2011.

  • Introduction to Algorithms (2 credits)

    Syllabus: Introduction to algorithms. Analysis of algorithms. Recurrences. Inductive and recursive approaches. Basic data structure such as queues, stacks, binary heaps and binary trees. Graphs. Algorithms in graphs: shortest paths and minimum spanning trees. Notions of computational complexity and problem reduction.


    • Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms, Third Edition. MIT Press, 2009.
    • Jon Kleinberg and Éva Tardos. Algorithm Design. Addison-Wesley, 2005.

    Remarks: this is basic course for those with background in computer science, but specialized for candidates in control and automation.

  • Convex Optimization (2 credits)

    Syllabus: Introduction to mathematical programming and optimization. Convex sets. Convex functions. Lagrangean Dual. Minimization of unconstrained convex function. Minimization of convex function in affine spaces. Convex minimization in convex sets. Applications.


    • Stephen Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
    • Dimitri Bertsekas, Angelina Nedić, and Asuman Ozdaglar. Convex Analysis and Optimization. Athena Scientific, 2003.

    Remarks: basic course in control, specialized for computer science and automation.

  • Integer Programming (2 credits)

    Syllabus: Introduction to mathematical programming. Review of linear programming. Linear programming duality. Problem formulation. Relaxations. Branch and bound algorithm. Theory of valid inequalities. Cutting-plane algorithm. Applications.


    • Lawrence Wolsey. Integer Programming. Addison-Wesley, 1998.
    • Robert J. Vanderbei. Linear Programming: Foundations and Extensions. Springer, Second Edition. 2001.

    Remarks: specialized/advanced for computer science and automation.

  • Advanced Topics in Optimization (2 credits)

    Syllabus: Advanced topics in optimization modeling and algorithms, such as global optimization, robust optimization, stochastic optimization and decomposition methods. Case studies and applications.

  • Real Time Systems I (2 credits)

    Syllabus: Definition, characterization, and examples of applications. Scheduling approaches, scheduling guarantees, and best effort scheduling. Schedulability tests base on utilization and response time. Scheduling of aperiodic and sporadic tasks. Resource access control. Adaptive scheduling. Communication protocols and real-time operating systems.


    • J.-M. Farines, J. da S. Fraga, R. S. de Oliveira. Sistemas de Tempo Real. Escola de Computação 2000, IME-USP, São Paulo-SP, julho/2000.
    • J. Liu. Real-Time Systems. Prentice-Hall, 2000.
    • A. Burns, A. Wellings. Real-Time Systems and Programming Languages. Addison-Wesley, 4th edition, 2009.
    • G. Buttazzo. Hard Real-Time Computing Systems – Predictable Scheduling Algorithms and Applications. Kluwer Academic Publishers, 1997.
    • Selected articles.

  • Real Time Systems II (2 credits)

    Syllabus: Methods and tools for computing worst-case execution time. Real time scheduling in multiprocessor systems: partitioning and global scheduling. Clock synchronization. Schedulability analysis of computer networks.


    • J.-M. Farines, J. da S. Fraga, R. S. de Oliveira. Sistemas de Tempo Real. Escola de Computação 2000, IME-USP, São Paulo-SP, julho/2000.
    • J. Liu. Real-Time Systems. Prentice-Hall, 2000.
    • A. Burns, A. Wellings. Real-Time Systems and Programming Languages. Addison-Wesley, 4th edition, 2009.
    • G. Buttazzo. Hard Real-Time Computing Systems – Predictable Scheduling Algorithms and Applications. Kluwer Academic Publishers, 1997.
    • Selected articles.

  • Distributed Systems I (2 credits)

    Syllabus: Introduction. Characterization of Distributed Systems. Distributed Systems Programming. Client-Server Model.  RPC. Dynamic binding. Middleware. Distributed objects. Case Studies: Java/RMI and CORBA. Sequencing and Synchronization. Partial Order: Causal Order and Distributed Systems. Total Order. Global State. Snapshot algorithms.  Coordination problems (mutual exclusion and leader assignment in distributed systems). Fault Semantics in Distributed Systems. Synchronous and Asynchronous Systems.


    • G. Coulouris, J.Dollimore and Tim Kindberg. Distributed Systems Concepts and Design. Addison–Wisley, 2011
    • Ajay Kshemkalayani and Mukesh Singhal. Distributed Computing: Principles, Algorithms and Systems. Cambridge Press- 2008.
    • A.S.Tanenbaum, M.V.Steen. Distributed Systems: Principles and Paradigms. 2002

  • Distributed Systems II (2 credits)

    Syllabus: Consensus Problems: Byzantine Consensus; Interactive Consensus with Signed Messages. Group Communication. Diffusion Protocols. Protocols with FIFO and Causal Sequencing. Protocols with Atomic Diffusion. Persistent Protocols. Multiple Copy Management. Management Strategies for Replicated Copies. Primary/Secondary  Models. Service Oriented Mechanisms: Webservices and SOA Architecture. Grid Computing: OGSA and OGSI. Cloud Computing. Management of IDs in Large Scale Systems. Dynamic Distributed Systems. P2P Networks.


    • Ajay Kshemkalayani and Mukesh Singhal. Distributed Computing: Principles, Algorithms and Systems. Cambridge Press- 2008.
    • Gerard Tel. Distributes Algorithms. Second Edition, 2000, Cambridge University Press.
    • G. Coulouris, J.Dollimore and Tim Kindberg. Distributed Systems Concepts and Design. Addison–Wisley, 2011.

  • Security and Fault Tolerance (2 credits)

    Syllabus: Security Implementation (Dependability): Attributes, Forms and Threats. Security Measures. Faults and Tolerance. Classification of Faults According with Fault Semantics. Fundamentals of Faults in Distributed Systems. Security Threats, Attacks and Violations.  Fundamentals of Security in Computer Networks. Cryptosystems: Symmetric Keys (Private Keys); Asymmetric Keys (Public Keys); Integrity/Authenticity Verification. Algorithms for Message Summarization. Access Control. Models of Access Control Policies. Matrix Access Model, RBAC, Multilevel Policy Model (Bell and LaPadula). Dynamic Models (UCON). Authentication. Autorization in Distributed Systems. Kerberos, SPKI, PGP. Security in Middleware. Case Studies. Web Security.


    • Matt Bishop. Computer Security Art and Science. Addison-Wesley,2003.
    • Anirban Chakrabati. Grid Computing Security. Springer Verlag, 2007.
    • Charlie Kaufman, Radia Perlman, Mike Speciner. Network Security: Private Communication in a Public World. Practice Hall, 2002.
    • Edward Amoroso. Fundamentals of Computer Security Technology. Prentice Hall 1994.

  • Design and Implementation of Embedded Systems (2 credits)

    Syllabus: Characterization, classification and applications of embedded systems. Methodologies for development of embedded systems. Characterization of computing systems. Languages and techniques for modeling embedded systems. Simulation and Verification of Embedded Systems. Coding. Applications in cyberphysical and critical systems. Case Estudy.


    • Lee & Seshia: Introduction to Embedded Systems – A Cyber-Physical Systems Approach.
    • Marwedel, P. Embedded System Design – Ed. Springer
    • P. Feiler and D. Gluch. Model-Based Engineering with AADL: An Introduction to the SAE Architecture Analysis and Design Language.
    • Bozzano, M., Villafiorita, A. Design and Safety Assessment of Critical Systems. CRC Press, 2011.
    • Koopman, P. Better Embedded System Software.

    Remarks: Specialized for automation and computing areas.

  • Artificial Intelligence (2 credits)

    Syllabus: Introduction to AI and its foundations. Search heuristics and constraint satisfaction in CSP. Knowledge representation (Logic and Ontologies). Inference and automated reasoning. Knowledge-based systems (decision-support systems and expert systems). Logic and fuzzy control.


    • Stuart J. Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. 3rd Ed. Prentice Hall, 2012.
    • Guilherme Bittencourt. Inteligência Artificial: Ferramentas e Teorias. 3a Ed. Editora da UFSC, 2006.
    • Patrick Henry Winston. Artificial Intelligence. 3rd Ed. Addison-Wesley, 1992.
    • R. Brachman and H. Levesque. Knowledge Representation and Reasoning. Elsevier, 2004.
    • Michael R. Genesereth and Nils J. Nilsson. Logical Foundations of Artificial Intelligence. Morgan Kaufmann, 1987.

  • Machine Learning (2 credits)

    Syllabus: Concepts on inductive learning. Data mining. Stochastic decision theory (Bayesian Networks). Neural networks. Reinforcement Learning. Genetic algorithm.


    • T. M. Mitchell. Machine Learning. McGraw-Hill, 1997.
    • Ethem Alpaydim. Introduction to Machine Learning. 2nd. Ed. MIT Press, 2009.
    • R. O. Duda, P. E. Hart, D. G. Stork. Pattern Classification. 2nd Ed. John Wiley, 2001.
    • S. Haykin. Redes Neurais: princípios e prática. 2a ed. 2001.
    • R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. MIT Press, 1998.
    • Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd Ed. Springer, 2011.

  • Multiagent Systems (2 credits)

    Syllabus: Introductions and fundamentals of multiagent systems (MAS). Dimensions of MAS: agent, organization, environment and interaction. Cooperation, coordination, negotiation and conflict resolution in MAS. Agent architectures. Development of MAS (methodologies and platforms).


    • M Wooldridge. An Introduction to Multiagent Systems. 2nd Ed. John Wiley, 2009.
    • G Weiss, editor. Multiagent Systems. 2nd Ed. The MIT Press, 2013.
    • Jacques Ferber. Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison-Wesley, 1999.
    • Y. Shoham and K. Leyton-Brown. Multiagent Systems: Algorithmic, Game-theoretic, and Logical Foundations. Cambridge University Press, 2009.
    • Rafael Bordini et al. Multi-Agent Programming: Languages, Platforms, and Applications. Springer, 2005.

    Remarks: this course integrates knowledge from other courses: each agent embeds a technique that was presented in another course.

  • Advanced Topics in Artificial Intelligence (2 credits)

    Syllabus: Advanced topics in artificial intelligence applied to automation and systems engineering, such as Support Vector Machines and Clustering. Case studies and applications.

  • Service-Oriented Software Engineering (2 credits)

    Syllabus: Service-oriented computing (SOC), Service-oriented architecture (SOA), lifecycle in SOA, methodologies for SOA developments, software quality in SOA, SOA governance, software a service (SaaS), web services, discovery and composition.


    • N. M. Josuttis, SOA in Practice – The Art of Distributed System Design. O’Reilly, 2007
    • W. Brown. SOA Governanc., IBM Press, 2009.
    • R. Daigneau. Service Design Patterns, Addison Wesley, 2012.
    • T. Erl. Web Service Contract, Design & Versioning for SO., Prentice Hall, 2008.
    • G. Alonso. Web Services – Concepts, Architectures and Applications. Springer, 2010.
    • T. Erl. SOA Design Patterns. Prentice Hall, 2009.
    • M. P. Sing, M. N. Huhns. Service-Oriented Computing – semantics, processes, agents. Wiley, 2005.
    • M. P. Papazoglou, Web Services & SOA – Principles and Technology, Pearson, 2012.
    • M. Fiammante. Dynamic SOA and BPM. IBM Press, 2010.
    • T. Chou. The End of Software. Pearson, 2005.
    • M. B. Greer. Software as a Service – Inflection Point. iUniverse Press, 2009.

  • Special Topics in Automation (2 credits)

    Syllabus: To be defined in the semester that the course is offered.

  • Fundamentals in Analysis and Design of Control Systems (4 credits)

    Syllabus: Definition of the control problem. Control in continuous and discrete time. Controllers with 1 and 2 degrees of freedom. Laplace and Z transform. Transfer function, poles and zeros, stability. Temporal response: transient and steady state. Frequency response. Reference response under perturbation. Robustness and performance specification. Analysis and design of control systems in continuous and discrete time using the root locus, frequency and pole placement methods. Basic notions about delay compensation, feed-forward and filtering in control. Practical aspects: PID control, implementation of digital controllers and applications.


    • W.A. Wolovich, Automatic Control Systems, Saunders Col. Publ., 1994.
    • G.F. Franklin, J. D Powel and A. Emami-Naeini, Feedback Control of Dynamic Systems – Third Edition, Addison-Wesley, 1994.
    • G.F. Franklin, J. D Powel and M.L. Workman, Digital Control of Dynamic Systems – Third Edition, Addison-Wesley, 1990.
    • K. Astrom and Hagglund, PID Controllers: Theory, Design and Tuning – 2nd Edition, 1995.

  • Linear Dynamic Systems (4 credits)

    Syllabus: Introduction to dynamics systems and control systems.  Mathematical description of dynamic systems in continuous and discrete time (transfer function, state variables, SISO and MIMO). Review of linear algebra. Similarity transformation. Solution of state equations (continuous and discrete case). Input-output, internal and Lyapunov stability in continuous and discrete time. Relation between poles and Eigenvalues. Concept of zeros in MIMO. Controllability, observability, canonical representation, stabilization and detectability. Transfer function matrix realization and minimal realization. State feedback (SISO and MIMO). Regulation problems, reference tracking, rejection and perturbations (principle of internal model). LQR model (Riccati Equation). State observer (full and reduced order) and principle of separation (SISO and MIMO). Kalman filtering and LQG control.


    • C.-T. Chen. Linear System: Theory and Design. Oxford, 1999.
    • S. Skogestad and I. Postlethwaite. Multivariable Feedback Control: Analysis and Design. John Wiley & Sons, 2001.
    • P. Albertos and A. Sala. Multivariable Control Systems: An Engineering Approach. Springer, 2004.

  • Mobile Robotics (2 credits)

    Syllabus: Introduction, locomotion, sensing, kinematics, navigation, mapping and cooperation.


    • Roland Siegwart, Illah R. Nourbakhsh, and Davide Scaramuzza. Introduction to Autonomous Mobile Robots, 2a. ed., The MIT Press.
    • Sebastian Thrun, Wolfram Burgard, Dieter Fox. Probabilistic Robotics. MIT Press.
    • Howie Choset et al. Principles of Robot Motion: Theory, Algorithms, and Implementations. MIT Press.

  • Predictive Control (2 credits)

    Syllabus: Introduction to prediction. Basic predictor and controllers.  Concepts in predictive control (model predictive control – MPC). Review of the GPC controller (Generalized Predictive Control). Representation of GPC without constraints as a classic controller.  Implementation. GPC for delayed systems. GPC representation as a DTC (dead-time compensator). Review of concepts of delay, Smith predictor and filtered Smith predictor. Robustness analysis and perturbation rejection. DTC-GPC controller. Feed-forward control in GPC. GPC with measurable perturbations. GPC with constraints. Control problem formulation and constraint treatment. Algorithms for problem solving with quadratic control. Simulated case studies and experimentation. Nonlinear predictive control. Problem formulation. Nonlinear optimization for problem solving. Approximate solution using quadratic programming. Multivariable predictive control (MIMO). General MPC problem formulation. Constraint treatment, robustness, and analysis of delayed systems. Simulated and experimental case studies.


    • Camacho and Bordons. Model Predictive Control. Spinger 2004.
    • Normey-Rico and Camacho. Control of Dead-Time Processes, 2007.

  • Robust Control (2 credits)

    Syllabus: Review of convex analysis; Definition and properties of LMIs; Schur complement; Finsler Lemma; S-Procedure; Elimination Lemma; D-G scalings; Uncertain systems and quadratic stability; Stability based on Eigenvalues in convex regions; System norms; state feedback optimal control via system norm;  Pole allocation in convex regions; Generalization to uncertain systems; Optimal H2 and H-infinity with dynamic output; Robust filtering.


    • A.Trofino, Apostila com as notas de aula do professor.
    • U. Mackenroth. Robust control systems. Springer Verlag, 2004.
    • L.El Ghaoui, S. Niculescu (Editors). Advances in Linear Matrix Inequality Methods in Control. SIAM Advances in Design and Control, 2000.
    • S. Boyd, L. El Ghaoui, E. Feron, V. Balakrishnan. Linear Matrix Inequalities in System and Control Theory. SIAM Studies in Applied Mathematics, 1994.

  • Process Control Techniques (2 credits)

    Syllabus: RST control structure: stability aspects, rejection to input and output perturbation, trajectory tracking, robustness by the small gain theorem. Control design by direct and indirect pole location. Implementation based on experimental and numerical simulation. Direct and indirect PID control design. Structures, parameter tuning, RST canonical form, anti-windup, performance evaluation. Internal model control design (IMC-Internal Model Control). Hybridization with PID control structure. Optimal design of digital controllers with quadratic functions. Control design via minimum variance generalized minimum variance with the direct and indirect approach. Performance evaluation. Design and tuning of MAC controllers (Model Algorithm Control) and DMC (Dynamic Matrix Control). Nonlinear DMC control. Design and tuning of GPC controllers (Generalized Predictive Control).


    • P. E. Wellstead & M. B. Zarrop. Self-Tuning Systems: Control and Signal Processing. 1991.
    • C. C. Hang; T. H. Lee & W. K. Ho. Adaptive Control. 1993.
    • K. J. Åström & B. Wittenmark. Adaptive Control. 1995.
    • W. S. Levine. The Control Handbook. 1996.
    • B. Coleman & B. Joseph. Techniques of Model-Based Control. 2002.
    • K. M. Moudgalya. Digital Control. 2007.
    • R. Isermann; K. H. Lachmann & D. Matko. Adaptive Control Systems. 1992.
    • K. Åström & T. Hägglund. PID Controllers: Theory, Design, and Tuning. 1995.
    • D. E. Seborg; T. F. Edgar & D. A. Mellichamp. Process Dynamics and Control. 2004.
    • G. F. Franklin; J. D. Powell & M. Workman. Digital Control of Dynamic Systems. 1997.
    • A. Visioli. Practical PID Control. 2006.
    • V. Bobál; J. Böhm; J. Fessl & J. Machácek. Digital Self-Tuning Controllers. 2005.
    • Ramon Vilanova & Antonio Visioli. PID Control in the Third Millennium. 2012.

  • Industrial Process Control (2 credits)

    Syllabus: Introduction to industrial process control. Motivational examples. SISO control structures for industrial processes. General concepts. Control of delayed systems. Smith predictor and modifications. Tuning, robustness analysis, perturbation rejection and noise treatment. Discrete implementation. Feed-forward control. Feed-forward actuation for set-point with measurable perturbations. Ideal problem solution, realization. Adjustment techniques for practical cases. Robustness and performance in closed loop. Cascading control. Adjustment techniques for cascade control. Applications to delayed systems. Case studies. Other process control techniques: control via relation, override control, control by media, etc. Multivariable process control (MIMO). Concepts and problems in MIMO control systems: choice of input-output pair, variable normalization, RGA, etc. MICO control systems and decentralized PID. Tuning methods. Multivariable process control via decoupling. Techniques for decoupling. Simulated case studies. Control of delayed multivariable processes. Generalizations and modifications of the Smith predictor. Simulated study cases.


    • Shinskey. Process Control Systems. Mc Graw Hill, 1996.
    • Skogestad and Postlethwaite. Multivariable Feedback Control. Wiley, 2007.
    • Normey-Rico and Camacho. Control of Dead-Time Processes. 2007.

  • System Identification (2 credits)

    Syllabus: Identification of first and second order models based on the impulse and step response. Identification of transfer-function models based on the frequency response. Identification of models represented as difference equations. Families of models and their properties.  Metodos de mínimos quadrados. Identificação por variável instrumental. Metodos recursivos. Modelagem via rele. Modelos LT (Look-Up Table). Identificação de modelos em variáveis de estado. Modelos de Wiener e Hammerstain. Series de Volterra. Redes neurais. Sinais de excitação. Metodos numéricos para identificação de modelos não lineares. Complexidade do modelo. Aplicações.


    • Coelho, A.A.R e Coelho, L.S. Identificação de sistemas lineares. Ed. UFSC, 2004.
    • Ljung, L. System Identification: Theory for the user. Prentice Hall, 1999.
    • Nelles,O. Nonlinear System Identification. Springer, 2001.

  • Nonlinear Systems (2 credits)

    Syllabus: Introduction. Review of linear systems. Nonlinear problems in engineering and nonlinear dynamic systems. Typical nonlinearities. Differential equations: existence and solution uniqueness. Qualitative analysis of continuous- and discrete-time dynamic systems. Autonomous and forced systems. Analysis on the phase plane. Attractors: equilibrium, limit cycles and aperiodic behavior. Linearization and equilibrium points (hyperbolic and nonhyperbolic). Hartman-Grobman Theorem. Analysis of bifurcation in continuous-time and discrete-time dynamic systems. Applications of Poincaré. Characteristic multipliers. Computational tools for numerical continuation and determination of bifurcations. Lyapunov method. Lasalle theorem. Theorem of central varieties. Analysis of feedback systems with constrained control input. Piecewise linear systems. Switched systems.


    • Monteiro, L. H. A. Sistemas Dinâmicos, Editora Livraria da Física, 3a edição, 2011.
    • Khalil, H. Nonlinear Systems. Prentice Hall, 3nd edition, 2002.
    • D. W. Jordan and P. Smith. Nonlinear ordinary differential equations: an introduction for scientist and engineers. 4th edition. Oxford Press, 2007.
    • M. di Bernardo, C.J. Budd, A.R. Champneys, P. Kowalczyk. Piecewise-smooth Dynamical Systems: Theory and Applications. Springer. Applied Mathematical Sciences 163, 2008.

  • Stochastic Control (2 credits)

    Syllabus: Stochastic linear systems in discrete-time. Control synthesis. Estimation, filtering and Kalman filters. Linear quadratic regulator, LQG and the separation principle.


    • Astrom, K. J. Introduction to Stochastic Control Theory. Academic Press, 1970.
    • Davis, M. H. A. Linear Estimation and Stochastic Control. Chapman and Hall, 1977.
    • Kay, S. M. Fundamentals of Statistical Signal Processing – Estimation Theory, Vol. 1. Prentice Hall, 1993.
    • Jazwinski, A. H. Stochastic Processes and Filtering Theory. Dover, 2007.
    • Kailath, T., Sayed, A. H., Hassibi, B. Linear Estimation. Prentice Hall, 2000.

  • Nonlinear Control (2 credits)

    Syllabus: Introduction and applications. Review of concepts in nonlinear control systems, stability and Lyapunov functions. Decoupling. Exact linearization. Norlam form. Zero dynamics and stabilization of nonlinear systems. Differential platitude. Output tracking of nonlinear dynamic systems. Design based on backstepping. Analysis and synthesis via absolute stability. Passivity in dynamic systems and Energy Shapping. Applications in Fuzzy modeling of Takagi-Sugeno. Application examples.


    • Isidori, A. Nonlinear Control Systems, 3a Edição. Springer, 1995.
    • Nijmeijer, H. van der Schaft, A. J., Nonlinear Dynamical Control Systems. Springer, 1990.
    • Khalil, H. Nonlinear Systems, 3a Edição. Prentice Hall, 2002.
    • Sepulcre, R. Jankovic, M., Kokotovic, P. Constructive Nonlinear Control. Springer, 1997.
    • van der Schaft, A. J. L2-Gain and Passivity Techniques in Nonlinear Control. Springer, 2000.

  • Dynamic System Modeling (2 credits)

    Syllabus: Introduction. Integration systems. Integration of processes and electronics. Information processing. Design methodology. Fundamentals of theoretical modeling of processes. Classification of process elements. Fundamental equations for processes of mass and energy flow. Balance equations for parameter-based systems. Elements of process connection. Modeling of mechanical systems. Leis de Newton. Principle of D’Alembert. Lagrange equation. Modeling of electrical systems. Process modeling.


    • Isermann, R. Mechatronic Systems: Fundamentals, Springer, 2005
    • Ljung, L., Glad, T. Modeling of dynamic systems, Prentice – Hall, 1994
    • Garcia, C. Modelagem e Simulação. 2a. ed. Edusp, 2005.
    • Pelz,G. Mechatronic Systems: Modelling and Simulation with HDLs. John Wiley & Sons, 2003.
    • Chiasson. J. Modeling and High-Performance Control of Electric Machines. John Wiley & Sons, 2005.
    • Luyben, W.L. Process Modeling, Simulation and Control for Chemical Engineers. 2a. ed, McGraw-Hil, 1996.

  • Automatio Applied to Oil and Gas Industry (2 credits)

    Syllabus: Introduction to the oil and gas industry. Upstream processes (exploration, production) and downstream (refining, transporting). Instrumentation in the oil and gas industry. Sensors and actuators use in extraction, production, transportation and refining plants. Inteligent transmitors. Control and safety valves. Industrial controllers. Industrial fieldbus networks for oil and gas systems. Networks for safety critical areas (areas subjet to risk of explotion and fire). Supervisory systems (SCADA). Specialized software-engineering and real-time control techniques for critical control systems found in oil and gas plants.  Control and supervision for oil and gas installations. Control systems based on industrial networks and fieldbus.


    • Thomas, José Eduardo. Fundamentos de Engenharia de Petróleo. Editora Interciência. 2001.
    • Berge, J. Fieldbuses for Process Control: Engineering, Operation and Maintenance. ISA – The Instrumentation, Systems and Automation Society. 2002.
    • Bentley, J. Principles of Measurement Systems. Third edition, Logman Scientific & Technical.1995.

  • Special Topics in Control (2 credits)

    Syllabus: Defined when offered.