Automation Engineering

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Detail Automation Engineering Elective Course Description


CEPN 631 System Optimization Methods

The system optimization methods course examines the optimization theory and techniques in the management of computer systems. Topics include classification of optimization problems, linear programming, unconstrained optimization, constrained optimization including inequality and equality constraints, Lagrange multiplier method, Kuhn-Tucker conditions, solution methods, dynamic programming, random search algorithms and search methods for optimization, convex optimization methods, and application examples from different engineering disciplines.

Reference books and materials [1] Chong, E. K. P., and Zak, S. H., An Introduction to Optimization, John Wiley & Sons, 1996 [2] Leunberger, D. G., Linear and Nonlinear Programming, 2nd Ed., Springer, 2003 [3] Bhatti, M. A., Practical Optimization Methods with Mathematica, Springer, 2000


CPEN 632 Industrial Controls

The industrial controls course present an overview of advanced controller design strategies for multivariable industrial processes, starting from PID control structure to the more advanced H-infinity design technique. Topics include feedback control systems, industrial processes and need for feedback, industrial control system components such as actuators, sensors, and controllers, classical control strategies, process modeling, PID control, state feedback, nominal feedback and performance of feedback control systems, H-optimal control uncertainty modeling for robust control, robust closed loop stability and performance, robust H control, controller technologies such as PLC, DCS, PC-based control, Matlab based control, Labview based control, and dedicated hardware for controller implementations.

Reference books and materials [1] Ellis, G., Control System Design Guide: Using Your Computer to Understand and Diagnose Feedback Controllers, 3rd Ed., Academic Press, 2003 [2] Franklin, G. F., Powell, J. D., and Naeini, A. E., Feedback Control of Dynamic Systems, 6th Ed., Prentice, 2009 [3] Strom, K. J., and Hagglund, T., Advanced PID Control, ISA, 2005 [4] Levine, W. S., The Control Handbook, CRC Press, 1996


CPEN 633 Control Systems Design

The control systems design course examines the basic issues and theoretical foundation in the analysis and design of computer control systems for industrial applications. Topics include signals and sampled data systems, zero-order hold equivalent, linear systems theory, control system modeling, dynamic systems analysis and feedback control, controller design methods, system stability analysis using Jury's test, Nyquist criterion, and Lyapunov method, observability, reachability and controllers, performance analysis, robust control, output feedback, separation theorem, optimal control, and adaptive control systems.

Reference books and materials [1] Dorf, R. C., and Bishop, R. H., Modern Control Systems, 12th Ed., Prentice Hall, 2010 [2] Nise, N. S., Control Systems Engineering, 5th Ed., Wiley, 2007 [3] Astrom, K. J., and Wittenmark, Computer Controlled System: Theory and Design, 3rd Ed., Prentice Hall, 1997 [4] Astrom, K. J., and Hagglund, T., PID Controllers: Theory, Design and Tuning, International Society for Measurement, 1995


CPEN 634 Programming for Industrial Automation

The programming for industrial automation course focuses on the programming mechanisms for modern devices in the control operations in industry. Topics include functional block diagrams, control system software for PLC and SCADA, programming techniques for PLC, computer based controllers, microcontroller, digital computer interface including isolated and non-isolated digital I/O, ADC, DAC, and engineering applications in selected industry in Ghana.

Reference books and materials [1] Karl-Heinz, J., and Tiegelkamp, M., IEC61131-3: Programming Industrial Automation Systems, Springer, 1995 [2] Lewsi, R. W., Programming Industrial Control Systems Using IEC 1131-3, IET, 1998 [3] Rehg, J. A., and Sartori,G. J., Programmable Logic Controllers, Prentice Hall, 2007 [4] Stenerson, J., Fundamentals of Programmable Logic Controllers, Sensors, and Communication, Prentice Hall, 1999 [5] Leunberger, D. G., Linear and Nonlinear Programming, 2nd Ed., Springer, 2003


CPEN 635 Robotics Systems

The robotic system course focuses on the design and programming of robotic systems and issues in current research in robotics. Topics include basic components of robotic systems, planning, control of realistic robotic systems, review of issues in robotics programming, robotic programming languages, classifications of robots, basic components, motion classification, control and sensing uncertainty, motion constraints, analysis of friction for assembly and grasping tasks, sensing systems for hands, environmental perception from sparse sensors for dexterous hands, grasp planning and manipulation.

Reference books and materials [1] Katic, D., and Vukobratovic, M., Intelligent of Robotic Systems, Kluwer, 2003 [2] Jacak, W., Intelligent Robotic Systems: Design, Planning, and Control, Kluwer, 1999 [3] Siciliano, B., Sciavicco, L., Villani, L., and Oriolo, G., Robotics: Modelling, Planning, and Control, Springer, 2010 [4] Nof, S. Y., Handbook of Industrial Robotics, Volume I, John Wiley & Sons, 1999


CPEN 636 Communication in Industrial Automation

The communication in automation course examines modern communication topology and architecture employed in industrial automation systems. Topics include communication components, principles of communication in industrial automation, architecture and topology of network communication, communication protocols as used in industrial automation in wired and wireless communication such as TCP/IP, RS-232, RS-485, Fieldbus, DNP3.0, Modbus, Zigbee, Bluetooth, and IDRA, error detection and control, troubleshooting, and introduction to security in industrial automation systems.

Reference books and materials [1] Merz, H., Hansemann, T., Hubner, C., Backer, J., Moser, V., and Greefer, L., Building Automation: Communication Systems with EIB/KNX, LON and BACnet, Springer, 2009 [2] NJATC, Building Automation: Control Devices and Applications, Amer Technical Publisher, 2008 [3] Tooley,M. H., PC Based Instrumentation and Control, Elsevier, 2005


CPEN 637 Human-Computer Interaction

The human computer interaction course provides key approaches to the design, development, and evaluation of human-computer interfaces, with an emphasis on usability, interaction paradigms, computer-mediated human activities, and implications to society. Topics include foundation of HCI and technologies, HCI paradigms and history, nature of human computer interaction, use and context of computers, human characteristics including human information processing, language and communication interaction, and ergonomics, computer system and interface architecture, development process including design approaches, implementation techniques and tools, evaluation techniques, user interface software and error handling, multimedia systems, interaction design for new environment.

Reference books and materials [1] Dix, A., Finlay, J., Abowd, G., and Beale, R., Human Computer Interaction, 3rd Ed., Prentice Hall, 2004 [2] Sears, A., and Jacko, J.A., The Human Computer Interaction Handbook: Fundamentals, Evolving Technologies and Emerging Applications, 2nd Ed., CRC Press, 2007 [3] Schneiderman, B., Plaisant, C., Cohen, M., and Jacobs, S., Designing the User Interface: Strategies for Effective Human Computer Interaction, 5th Ed., Wiley, 2007 [4] Sharp, H., Rogers, Y., and Preece, J., Interaction Design: Beyond Human Computer Interaction, Wiley, 2007


CPEN 638 Machine Learning

The machine learning course examines the field of machine learning with a focus on how to construct computer programs that automatically improve with experience. Topics include exponential family distributions, Bayesian networks, Bayesian inference, maximum likelihood, maximum entropy, mixture models, EM algorithm, graphical models, hidden Markov models, variational methods, linear classifiers, regression, generalization bounds, support vector machines, kernel methods and transduction, machine learning applications of detecting fraudulent card transactions, learning users reading preferences, and autonomous vehicles that learn to drive.

Reference books and materials [1] Mikkonen, T., Programming Mobile Devices: An Introduction to Practitioners, Wiley 2007 [2] Koller, D., and Friedman, N., Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning, MIT Press, 2009 [3] Marsland, S., Machine Learning: An Algorithmic Perspective, Chapman & Hall, 2009