General Information on PGEAS Courses:

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

Courses Offered in each Phase (in Portuguese)

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

  • Scientific Methodology (2 credits)

    Description: Science, ethics and the society. Graduate level research. Research documentation. Research topic: problem formulation, research hypothesis, objectives, and theoretical framework. 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)

    Description: Architecture and programming of microprocessor 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.

    Note: this course is suitable mainly for students who do not have a background in engineering.

  • Fundamentals of Control and Automation (2 credits)

    Description: Concepts about the automation process: measurement, actuation and control. Stability and performance of feedback systems. Introduction to 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.

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

  • Stochastic Processes (2 credits)

    Description: Review of probability. Random vectors. Gaussian random vectors and the Central Limit Theorem. Discrete-time stochastic processes.


    • 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)

    Description: Introduction to discrete mathematics. Relations: definitions and properties. Partial order relations. 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)

    Description: Automata: regular languages and regular expressions, finite state automata, non-deterministic automata, Myhill-Nerode theorem, minimization of automata. Petri nets: definitions, modeling, properties, analysis, simulation, implementation 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)

    Description: 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 nonconflict test. Formal verification: principles, temporal logic, model checking, equivalence, property specification, verification-oriented languages (Fiacre) 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)

    Description: Advanced control: hierarchical control, timed control, automaton game. Modeling and analysis of reactive systems: synchronous approach, imperative languages (Esterel) and data-flow languages (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)

    Description: 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 multimedia 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 Local Area Networks (2 credits)

    Description: 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)

    Description: Introduction. Performance índices. Stochastic processes. Discrete and continuous time Markov chains. Queuing 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)

    Description: Introduction to algorithms. Analysis of algorithms. Recurrences. Inductive and recursive approaches. Ordering. 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.

    Note: this is a basic course for those with background in computer science, but specialized for students in the control and automation area.

  • Convex Optimization (2 credits)

    Description: Introduction to mathematical programming and optimization. Convex sets. Convex functions. Convex Problems. Lagrangeian Dual. Minimization of unconstrained convex function. Unconstrained minimization of convex functions. Minimization of convex functions in affine spaces. Minimization of convex functions 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.

    Note: this is a basic course for those with a background in control, but specialized for students in the computer science and automation area.

  • Integer Programming (2 credits)

    Description: Introduction to mathematical programming. Linear programming. Linear programming duality. Integer 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.

    Note: this is a specialized/advanced course for those in the computer science and automation area.

  • Advanced Topics in Systems Optimization (2 credits)

    Description: 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)

    Description: Definition, characterization, and examples of applications. Scheduling approaches, scheduling guarantees, and best-effort scheduling. Cyclic executive. 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)

    Description: Methods and tools for computing worst-case execution time. Real-time scheduling in multiprocessor systems: partitioning and global scheduling. Multiprocessor resource allocation protocols. 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)

    Description: Introduction. Characterization of Distributed Systems. Distributed Systems Programming. Client-Server Model. Name Services. RPC. Reliability Semantics in RPC. Dynamic binding. Middleware. Distributed objects. Case Studies: Java/RMI and CORBA. Ordering and Synchronization. Partial Order: Causal Order and Distributed Systems. Total Order. Logical Clocks. Physical Clocks. Global State. Snapshot algorithms.  Coordination problems (mutual exclusion and leader election 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)

    Description: Agreement Problems: Byzantine Agreement; Consensus; Interactive Consistency. Interactive Consistency with Signed Messages. Group Communication. Reliable Diffusion Protocols. FIFO and Causal Ordering Protocols. Atomic Diffusion Protocols. Relevant Protocols. Multiple Copy Management. Management Strategies for Replicated Copies. Primary/Secondary Models. Active Replication. Replica Determinism. Service-Oriented Mechanisms: Webservices and SOA Architecture. Grid Computing: OGSA and OGSI. Cloud Computing. Identity Management 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)

    Description: Dependability: attributes, forms and threats. Correct services. Dependability measures. Faults tolerance. Classification of faults according to failure semantics. Fundamentals of faults tolerance 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. Text summarization algorithms. Text sealing mechanism. Access Control. Models of Access Control Policies. Access Matrix Model, RBAC, Multilevel Policy Models (Bell and LaPadula). Dynamic Models (UCON). Authentication. Autorization in Distributed Systems. Kerberos, SPKI, PGP. Middleware Security. 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)

    Description: Characterization, classification and applications of embedded systems. Methodologies of embedded systems development. Characterization of computing models. Languages and techniques for embedded systems modeling. 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.

    Note: this is a specialized course for those in the automation and computing areas.

  • Artificial Intelligence (2 credits)

    Description: Introduction to AI and its foundations. Heuristic search and CSP constraint satisfaction problems. Knowledge representation (Logic and Ontologies). Automated reasoning and inference. 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)

    Description: 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)

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

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

  • Advanced Topics in Artificial Intelligence (2 credits)

    Description: 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)

    Description: Service-oriented computing (SOC), Service-oriented architecture (SOA), SOA lifecycle, methodologies for SOA development, integration and interoperation aspects, 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)

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

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

    Description: Definition of the control problem. Continuous and discrete-time control. One and two degrees of freedom controllers. Laplace Transform and Z-Transform. Transfer function, poles and zeros, stability. Temporal response: transient and steady-state. Frequency response. Reference  and perturbation response. Robustness and performance specification. Analysis and design of continuous and discrete-time control systems 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)

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

    Description: 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)

    Description: 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 unconstrained GPC as a classic controller. Code implementation. GPC for delayed systems. GPC representation as a DTC (dead-time compensator). Review of concepts of delay compesation, Smith predictor and filtered Smith predictor. Robustness analysis and perturbation rejection. DTC-GPC controller. Feed-forward control in GPC. GPC with measurable perturbations. Constrained GPC. Control problem formulation and constraint treatment. Algorithms for optimization problem solving using quadratic programming. Simulated and experimentation case studies. Nonlinear predictive control. Problem formulation. Nonlinear optimization for problem solving. Approximate solution using quadratic programming. Multivariable predictive control (MIMO). General MPC MIMO 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)

    Description: Review of convex analysis; Definition and properties of LMIs; Basic tools: Schur complement; Finsler’s 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 placement in convex regions; Generalization to uncertain systems; H2 and H-infinity optimal control with dynamic output feedback; 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)

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


    • 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)

    Description: 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 setpoint and measurable perturbations. Ideal problem solution, realization. Adjustment techniques for practical cases. Closed loop robustness and performance. Cascad control. Concept. Adjustment techniques for cascade loop. 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 systems control: choice of input-output pairs, variable normalization, RGA, etc. MICO systems control and decentralized PID controllers. Tuning methods. Multivariable process control via decoupling. Decoupling techniques. Simulated case studies. Control of delayed multivariable processes. Generalizations of the Smith predictor and modifications. 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)

    Description: 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 by difference equations. Families of models and their properties. Least-squares methods. Identification by instrumental variable. Recursive methods. Relay modeling. LT (Look-Up Table) models. Identification of state variable models. Wiener and Hammerstain models. Volterra Series. Neural networks. Excitation signs. Numerical methods for identification of nonlinear models. Model complexity. Applications.


    • 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)

    Description: Introduction. Review of linear systems. Nonlinear problems in engineering and nonlinear dynamic systems. Typical nonlinearities. Differential equations: solution existence and uniqueness. Qualitative analysis of continuous- and discrete-time dynamic systems. Autonomous and forced systems. Phase plane analysis. Attractors: equilibrium, limit cycles and aperiodic behavior. Linearization and equilibrium points (hyperbolic and nonhyperbolic). Hartman-Grobman Theorem. Analysis of bifurcations in continuous-time and discrete-time dynamic systems. Poincaré map. 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)

    Description: Discrete-time stochastic linear systems. Controller synthesis. Estimation, filtering and Kalman filters. Linear quadratic regulator (LQR), linear quadratic Gaussian 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)

    Description: Introduction and applications. Review of concepts in nonlinear control systems, stability and Lyapunov functions. Decoupling. Exact linearization. Normal form. Zero dynamics and stabilization of nonlinear systems. Differential flatness. Examples of planar systems Output tracking of nonlinear systems. Backstepping design. Analysis and synthesis via absolute stability. Passivity in dynamic systems and Energy Shapping. Applications of the Takagi-Sugeno fuzzy modeling. 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)

    Description: Introduction. Integration systems. Integration of processes and electronics. Information processing. Design methodology. Fundamentals of theoretical process modeling. Classification of process elements. Fundamental equations for processes of mass and energy flow. Balance equations systems with concentrated parameters. Elements of process connection. Modeling of mechanical systems. Newton’s Laws. D’Alembert Principle. Lagrange’s equations. Modeling of electrical systems. Modeling of machines 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)

    Description: Introduction to the oil and gas industry. Upstream (exploration, production) and downstream (refining, transporting) processes. Instrumentation in the oil and gas industry. Sensors and actuators used in extraction, production, transportation and refining plants. Intelligent transmitters. Control and safety valves. Industrial controllers. Industrial fieldbus networks for oil and gas systems. Networks for intrinsic safety areas (areas subject to risk of explosion and fire). Telemetry and Remote Control. Supervisory systems (SCADA). Specialized software-engineering and real-time control techniques for critical control systems found in oil and gas plants.  Control and supervision of oil and gas installations. Control systems based on industrial fieldbus networks.


    • 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)

    Description: Defined when offered.