EEE 481 Computer Controlled Systems
COURSE DESCRIPTION (EXTENDED)
The ever-increasing availability of reliable, low-cost, high-performance computing hardware has been one of the
key drivers behind the proliferation of computer-based control of processes. Such controllers can perform a
variety of complicated tasks that increase the overall process performance and repeatability, decrease the
operating cost, meet stringent safety and environmental constraints, and vastly improve the human user
interface. Typical examples of computer controlled systems range from the mundane to the exotic: Modern
washers use computer control to provide a cleaner laundry, using less energy and less water. Computers enable
car engines to deliver more horsepower, with lower fuel consumption and emissions, and allow sophisticated
diagnostic tests. Industrial controllers enable more efficient manufacturing, while state-of-the-art control in
aerospace applications is responsible for the extraordinary performance of fighter aircraft, and the cost
efficiency of commercial airliners.
In addition to the advances in hardware, the software evolution has played a key role in enabling these
applications. The complexity of the modern controllers is such that “controller evaluation, commissioning, and
maintenance” have taken new meaning. New concepts like “rapid prototyping” have emerged. A modern
process controller can deliver the computing power of an old workstation and occupy ¼ of the volume of a
notebook PC. Such controllers can now be “embedded” in a process tool and perform a variety of tasks to
ensure process reliability and accurate diagnostics. By doing so locally, independent of a central monitoring
computer, they free up resources and improve the fault-tolerance of the overall system. In return, they require
more elaborate communications and networking.
The design and implementation of a modern control system should still address the classic fundamental
problems of sensors and measurements, actuators, and feedback. But it must also account for the issues
associated with real-time operation, computer hardware and software, discretization and quantization,
interfacing and communications. State-of-the-art applications, for which a high degree of integration and
optimization of every aspect of the control system is critical, require significant investments and large groups of
experts. However, it is only in the recent years that complete control solutions of medium-large-size problems
are within the capabilities of a small R&D group or even an individual. There are primarily two reasons for this:
One is that the computing power (speed/memory) of general-purpose computer platforms now exceeds the needs
of typical control algorithms. The other is the ability of modern CAD software (e.g., MATLAB) to generate
stand-alone executables from codes written in a high-level language. The former obviates the need to use highly
specialized hardware and avoids the associated development overhead. The latter allows the programming of
sophisticated control algorithms in a compact form that can be easily debugged, improved and maintained. The
combination of both of these technology advancements has resulted in a quantum breakthrough giving practical
meaning to the “rapid prototyping” of sophisticated control algorithms.
This “Computer Controlled Systems” course aims to address the variety of issues arising in the implementation
of computer-based controlled systems in an integrated and simplified manner. By focusing on the use of PC104
boards and MATLAB xPC Target toolbox, it provides a global perspective of the entire process of implementing
and testing embedded controllers, but avoids time-consuming dwellings in less-often encountered details. Smallscale but representative projects form the core of the instruction and provide hands-on expertise.
Course Description (20 word, catalog):
Implementation of computer-based, embedded, control systems using MATLAB xPC Target
toolbox. Small-scale, representative projects demonstrate theoretical issues and provide hands-on
expertise.
Prerequisite: EEE 203, EEE 221(or equivalent)
Textbook: M. Chidambaram, Computer Control of Processes. Alpha Science Intl. Ltd,
Pangbourne, 2002. (ISBN 1-84265-063-7 or 0-8493-1010-5)
Franklin, Powell, Workman, Digital Control of Dynamic Systems. 3rd ed. Ellis-Kagle Press, Half
Moon Bay, Ca.
C.L. Phillips and H.T. Nagle, Digital System Control Analysis and Design
Prentice Hall, 3rd Ed.
Supplemental Materials: Class notes and software distributed by the instructor. (See course web
page for additional references.)
Coordinator: K. Tsakalis
Course Objectives:
1. Students are familiar with the most common elements of computer control: sensors,
sampling, control algorithms, actuators.
2. Students understand the basic problems in computer control of processes: principles of
feedback and feedforward control, sampling, quantization, real-time operation.
3. Students are familiar with computer software to implement embedded control systems (e.g.,
MATLAB, Real Time Workshop, xPC Target).
Course Outcomes:
1. Students can discuss the principles of operation of common sensors and actuators, A/D-D/A
converters.
2. Students can state and apply basic definitions in measurements.
3. Students can apply standard design techniques for common control algorithms (PID,
feedforward).
4. Students can discuss and analyze issues related to controller discretization and
signal/parameter quantization.
5. Students can use computer software to implement embedded controllers.
Course Topics:
1.
2.
3.
4.
Examples of computer controlled systems
 Open-loop (feed-forward) and closed-loop (feedback) control
 Position control. Velocity control. Temperature control. Flow control. Pressure control. Voltage,
current, power control. Light intensity control
Instrumentation
 Measurement basics: Error analysis and statistics. Validity, Repeatability, Accuracy, Precision,
Resolution. Static-Dynamic errors
 Electrical Measurements
 Voltmeters, Ammeters. Potentiometers, Position/Displacement Transducers, Accelerometers,
Tachometers. Bridge circuits. Thermocouples, Thermistors. Strain gauges, Force/Pressure
Transducers, Flow meters.
 Light/Optical sensors
 Digital Sensors. Counters, Trigger circuits.
 A/D, D/A converters
Real-Time and Discretization Issues
 Real-time operating systems (introduction), Interrupts.
 Timing diagrams, Event-sequence descriptions.
 Computer data acquisition.
 Discretization errors, Sampling and reconstruction. Anti-aliasing filters, Analog implementations.
 Quantization errors.
Software and Hardware Platforms
 MATLAB/SIMULINK and other high-level development environments.
 Embedded Controllers and stand-alone applications; xPC Target Toolbox, Auto-code generation.
 The PC104 platform.

5.
6.
7.
Digital communications principles (computer-sensor, computer-computer). RS232, GPIB, SECS,
Ethernet protocols.
Actuators
 Electrical, Pneumatic, Hydraulic actuators.
 Solenoids, Relays. SCRs, TRIACs. Motors (DC, AC, Stepping). Current-to-pressure actuators.
Valves, Mass-Flow Controllers.
Control Algorithms and Procedures
 Event-driven (discrete-state) control. Ladder diagrams, Programmable Logic Controllers
 Sampled-data (continuous-state) control.
 Dynamical Systems descriptions, state-space concepts, discretization.
 Feedforward controllers, dynamic inversion.
 Feedback Systems: Basic Properties, Stability, Sensitivity.
 On-Off (2-state) controllers. Chattering, Cycling.
 Classical simple dynamic feedback controllers. PID control principles. Discrete-time
implementations, quick tuning procedures.
What’s next? (Overview of advanced topics)
 Signal Processors. FPGAs.
 Smart Sensors. Data Mining. Data Fusion.
 Control system architectures: Distributed, Decentralized, Cascade, 2-DOF Controllers.
 Modeling and Approximations. System Identification, Neural-Networks.
 Optimal Control Systems (Feedback, Feed-forward).
 Adaptation and Learning. Intelligent Systems.
Computer Usage: Exercises and demonstrations using MATLAB/SIMULINK and embedded
controller hardware.
Laboratory Experiments:
1. Familiarization with basic hardware connections and procedures to create real-time executables.
2. RS-232 serial port asynchronous communication.
3. Target-Host communications and MATLAB programming.
4. A/D-D/A converters.
5. Modeling and implementation of a virtual heat transfer experiment; PID control.
6. Implementation of simple least-squares and Kalman filter estimators for system identification.
7. Modeling and implementation of a virtual water level control experiment.
8. Modeling and implementation of a virtual inverted pendulum experiment; more complicated controller
design and communication requirements.
Course Contribution to Engineering Science and Design:
EEE 481 emphasizes engineering design by using open-ended exercises and hardware/software
implementation of theoretically derived
Emphasis is placed on integrating verious components
Since there are many possible solutions to such a problem, students are able to consider design
tradeoffs and issues involved in practical implementation.
Course Relationship to Program Objectives:
A.1 [3] Students are exposed to modern computational techniques for designing feedback and
feedforward control systems. They are also exposed to modern CAD tools (e.g., MATLAB,
RTW, xPC-Target) that have been used extensively during the last decade in industry and
academia for modeling and implementation of control systems.
A.2 [2] The operation of basic sensor and actuators, analog-to-digital and digital-to-analog
conversions,and common computer communication protocols, are reviewed to enable their usage
in control system implementations.
A.3 [1] The students use modern CAD tools that reflect industrial trends in embedded systems
applications.
C.1 [2] Students receive hands-on experience in modern CAD, analysis, and implementation tools
that are extremely desirable in the constituent industry.
C.2 [1] Hands-on experience is a desirable quality in applying to graduate programs.
D.1 [2] Students learn the advantages and limitations of various hardware and software
components of control systems. They are using this knowledge to provide integrated control
system solutions for virtual properties.
D.2 [1] Students obtain models for various processes and create virtual (computer simulated)
experiments, for which they then design control systems.
D.3 [1] The translation of physical problems into an abstract but rigorous and quantitative
mathematical framework enhances the understanding of the physical phenomena. The creation of
virtual experiments also requires a deeper understanding of model limitations.
D.4 [3] Students use MATLAB extensively for simulation and real-time implementation.
D.5 [1] Through A.1, A.2, D.2, D.3
Person preparing this description and date of preparation: Kostas Tsakalis, April 2005.