Basic-
- Linear Systems
Introduction to the fundamental theory of finite-dimensional
linear systems with emphasis on the state-space representation. The topics
to be covered include but are not limited to 1)mathematical representations
of systems, 2) linear dynamic solutions, 3) controllability, observability,
and stability, 4) linearization and realization theory, and 5) state feedback
and state observer.
- Nonlinear Systems
Failure of superposition of effects; phase-plane analysis;
limit-cycles; Lyapunov stability; hyperstability and input-output stability;
controllability and observability of nonliear systems; feedback linearization;
robust nonlinear control system design.
- Stochastic Systems
Theory and applications involving probability, random variables,
functions of random variables, and stochastic processes, including Gaussian
and Markov processes. Correlation, power spectral density, and nonstationary
random processes. Response of linear systems to stochastic processes. State-space
formulation and covariance analysis.
- Neural Networks
Introduction to mathematical analysis of networks and learning
rules, and on the application of neural networks to certain engineering
problems inimage and signal processing and control systems.
- Intelligent Systems
Introduction to the state-of-the art intelligent control
and system successfully deployed to industrial and defense applications.
The topics to be covered include, but are not limited to, 1) emerging intelligent
algorithms (e.g., NN, FS, GA, EP, DES), 2) intelligent control architecture
(e.g., bottom-up, top-down, seminotics), 3 reinforcement learning and hybrid
systems, and 4) case studies and design projects.
- Optimization
The concepts, issues, and most practicable methods for linear
and nonlinear, unconstrained and constrained, multivariable optimiation.
This applications oriented course is intended for all engineering and science
disciplines.
- Digital Control Systems
Input-output and state-space representation of linear discrete-time
systems. Approximate methods in discrete-time representation. Stability
methods. Controllability, observability, state estimation, and parameter
identification. Design and analysis of feedback control system using frequency-domain
and state-space methods. Introduction to optimal control.
- Digital Data Acquisition
Use of microcomputers operating in realtime applied to engineering
systems for data acquisition and control, use of analog to digital, digital
to analog, and digital input/output, synchronous and asynchronous programming.
Competence in the engineering use of microcomputers through lectures and
laboratory applications.
- Discrete Event Simulation
- Production Control
Principles and practice of industrial control. Modern quality
philosophy, including a process improvement strategy incorporating charter,
documentation of knowledge and improvement cycle. Theory and use of statistical
process control (SPC) tools for problem solving and continuous improvement.
Variables and attributes control charts for both discrete and continuous
flow/batch processes. Process capability and performance analysis including
strengths and weaknesses of Cpk and Ppk indices. Introduction to acceptance
sampling, including ANSI/ASQC Z1.4 standards.
Advanced-
- Robotics
Design and performance analysis of robots and manipulators
as applied in flexible manufacturing and automation. Structural synthesis,
kinematic and dynamic analysis, dexterity analysis, motion programming,
and control system analysis and synthesis.
- System Identification
Linear and nonlinear system modeling of random systems.
Models of linear time-invariant systems, nonparametric methods and preliminary
model development, parameter estimation methods, convergence and consistency,
asymptotic distributions of parameter estimates. Nonlinear modeling.
- Adaptive Control
Analysis and design of control techniques which modify their
performance to adapt to changes in system operation. Review of systems
analysis techniques, including state variable representations, linearization,
discretization, covariance analysis, stability, and linear quadratic Gaussian
design. On-line parameter estimation, model reference adaptive systems,
self-tuning regulators, stable adaptive systems.
- Optimal Control
Optimal control theory for modern
systems design. Specification of optimum performance indices. Dynamic programming,
calculus of variations and Pontryagin's minimum principle. Iterative numerical
techniques for trajectory optimization.
- Multivariable Control
Introduction to multivariable sysems: SISO robustness vs.
MIMO robustness; multivariable system poles and zeros; MIMO transfer functions;
multivariable frequency response analysis; multivariable Nyquist theorem;
performance specifications; stability of feedback systems; linear fractional
transformations (LFT's); parameterization of all stabilizing controllers;
structured singular value; algebraic ricatti equations; H2 optimal control;
H-infinity controller design.
- Intelligent Control
- Real-time Distributed Systems
Fundamental concepts associated with the design of software
for implementation on distributed computer systems using real-time operating
systems. Parallel computing in a real-time environment and control algorithm
design. State-of-the-art boards including analog-to-digital and digital-to-analog
equipment and newest computer-aided software engineering tools.
- Estimation Theory
Optimal estimation theory including linear and nonlinear
estimation of discrete and continuous random functions. Wiener and Kalman
filter theory included.
- Queuing Theory
- Advanced Systems Modeling
- Advanced Nonlinear Control
Introduction to vector fields and Lie algebra; controllability
and observability of nonlinear systems; local decompositions; input-output
and state-space representation of nonlinear systems; feedback linearization;
controlled invariance and distribution; control of Hamiltonian systems.
- Advanced Process Control
- Advanced Production Control
Modern quality philosophy and application. Theory and application
of traditional and nontraditional control charting techniques. Special
emphasis on underlying assumptions such as normality and error-free inspection.
Oriented toward economically-based statistical monitoring of processes,
including optimization of decision variables such as sample size, frequency,
and control limit spread.
- Reliability and Maintainability
