This course introduces students to Control and Instrumentation Systems Engineering concepts and methodology. The course also gives a broad picture of the career, curriculum, and engineering application in Control and Instrumentation Systems Engineering applications.
Pre-Requisites: MATH102
Binary arithmetic, Boolean algebra, Boolean functions and their simplification. Implementation of Boolean functions using Logic Gates, SSI, MSI, and LSI chips, Analysis and Design of Combinational circuits, Sequential Logic: Flip-Flops, Counters, and Registers, Analysis and Design of sequential circuits, Basic elements of digital Computers: Register-transfer, Micro operations, Instruction codes, Processor organization Arithmetic Logic Unit, ADC and DACS. Note: Not to be taken for credits with EE 200
Pre-Requisites: PHYS102
This course introduces fundamentals of information technology and systems; their structures and components and current trends, such as the Internet, wireless communication, pervasive computing and IT Enterprise applications to improve business performance. The course emphasizes on industrial automation applications of IT, process system and process control, protocol architectures with several case studies. Prerequisite: CIE 201 or approval of the department
Pre-Requisites: CIE201 Or CISE201
Roots of nonlinear equations. Solutions of systems of linear algebraic equations. Numerical differentiation and integration. Interpolation. Least squares and regression analysis. Numerical solution of ordinary and partial differential equations. Introduction to error analysis. Engineering case studies.
Pre-Requisites: (ICS104 Or ICS102 Or ICS103 Or ICS101) And MATH201
Linear systems, Modeling of physical systems, Modeling of Inventory Control, Production and Financial Systems, Ordinary Differential equations models, Laplace Transform, transfer functions, block diagram manipulation. Open loop and close loop systems, time domain analysis, response of systems to different test signals, steady state analysis, concept of stability, Routh-Hurwitz criteria, controller design, and simple root locus analysis and controller design.
Pre-Requisites: (MATH208 Or MATH202 Or MATH260) And (EE204 Or EE201)
This course consists of set of lab experiments for students to gain hands-on experience with modeling, analyzing and controlling linear control systems. They also develop proficiency in using MATLAB and SIMULINK software for simulating such systems.
Pre-Requisites: CIE305* Or CISE305*
General measurement systems; static and dynamic characteristics, two port networks and loading effects, signals and noise; error and uncertainty analysis, modeling of sensing elements such as resistive, inductive, electromagnetic, thermoelectric, elastic, piezo-electric, electromechanical, optical etc.; signal conditioning elements, D.C. and A.C. bridges, compensation by linearization, feedback, operational amplifiers, modulation/ demodulation; signal processing elements, microcomputer-based instrumentation, I/0 devices, interfaces, data display units, examples of measurement systems such as flow, pressure, level, temperature, etc.
Pre-Requisites: EE203
Basic models of continuous and discrete systems, Major characteristics of signals (energy, power and peak amplitude), Properties of LTI systems, Fourier analysis of continuous and discrete systems, Basic concept of signal modulation, signal sampling and reconstruction. Basic time and frequency characterization of signals and systems and basic concept of transfer function. Basic random signal analysis. Application of signal and system concepts to linear control system and digital signal processing. Note: Not to be taken for credits with EE 207
Transient and Steady State analysis and design specifications. Root locus, Design using Root locus. Frequency Response Techniques, Bode plot, Nyquist plot, principle of Specifications and controller Design in the Frequency domain. State-space model, analysis of the state-space model, Controllability and Observability, pole placement, and robust Control.
Pre-Requisites: CIE305 Or CISE305 Or CISE302
Elements of Computer Control Systems, A/D and D/A, Sampling theorem, signal conditioning, anti-alias filters, sensors, actuators. Discrete time systems, digital control design, digital PID control. Programmable logic controllers, computer control technology including distributed computer control, fieldbus technology, and OLE for process control.
Pre-Requisites: CIE315 Or CISE315
The purpose of this course is to raise students’ awareness of contemporary issues in their discipline and otherwise. The student has to attend a required number of seminars, workshops, professional societal meetings or governmental agency conferences; at least half of these should address issues in his discipline. The student has to attend a required number of industrial visits.
A 28-week program of industrial training approved by the department. The student must submit a comprehensive report on his work during that period. Prerequisites: Completion of 85 Credit Hours, Attainment of CGPA and major GPA of 2.0, Fulfillment of Departmental Requirements include ENGL214, CIE 312, CIE390 and at least two of CIE316, CIE314 and CIE418
Pre-Requisites: ENGL214 And (CIE312 Or CISE312) And (CIE390 Or CISE390) And ( ( (CIE316 Or CISE316) And (CIE318 Or CISE318 Or CIE418 Or CISE418) ) Or ( (CIE318 Or CISE318) And (CIE316 Or CISE316 Or CIE418 Or CISE418) ) Or ( (CIE418 Or CISE418) And (CIE316 Or CISE316 Or CIE318 Or CISE318) ) )
An 8-week program of industrial training approved by the department. The student must submit a report on his work during that period.
Pre-Requisites: ENGL214
Basic concept of switching, different input-output switching devices and designing automation systems using PLCs. Power-electronic switching devices, DC and AC power control using SCR, TRIAC, power transistors, etc. Concept of actuation, linear and rotary actuators of electrical and fluidic types. Principle of operation and modeling of electro-mechanical devices, and various types of DC, AC and Stepper motors, and their speed-control through power-electronic switching circuits.
Pre-Requisites: EE203
This is the first of two courses for the multidisciplinary, capstone project. Multidisciplinary teams will be formed, projects will be defined, and project management discussed. Not to be taken with CIE 490
Mechatronics is the synergistic integration of mechanism, electronics, and computer control to achieve a functional system. Fundamentals of interfacing of modern mixed electrical, mechanical, and computers systems. Sensors, Signal Conditioning, Electro-Mechanical Actuation, Basic System Modeling, Essentials of Dynamic Systems, Data Acquisition and Virtual Instrumentation, and PC-Based and Embedded Controllers. Physical properties, mathematical modeling for computer simulation. Applications illustrated by numerically and experimentally generated results.
Pre-Requisites: CIE410 Or CISE410 Or CISE313
This is the second of two courses for the multidisciplinary, capstone project. Multidisciplinary teams undertake product definition, generation of conceptual designs, product development, and presentation of final products. Students integrate knowledge acquired from prior courses into multidisciplinary projects with multiple constraints and use engineering standards while further developing their communication skills and life-long learning techniques.
Modeling of processes, Mass balance, and Energy balance, Models of representative processes, Dynamic response, and Linearization. Process identification using time and frequency domain techniques. Time delay, Smith predictor. Basic and advanced control strategies, e.g. PID, Feed forward, Internal model, and supervisory control. Time domain controller design, Controller tuning. Controller design in the frequency domain, Optimization Techniques and Supervisory Control. Case studies.
Pre-Requisites: CIE305 Or CISE305 Or CISE302
Review of the Fundamental laws, mathematical modeling; model and simulation of typical processes. Computer simulation tools, Virtual Instruments, MMI. Systems identification, IMC, Predictive control, DMC, Neural Network modeling and control. Students will work out simulation and control projects, using DYNSIM process dynamic simulation and Simulink, of typical processes, e.g., CSTR, Gas Surge Drum, Isothermal Chemical Reactor, Vaporizer, Binary Column, Heat Exchanger, etc.
The course offers an introductory material to advanced control strategies such as fuzzy and neural network based controllers. The need for model–free control, linguistic based control, foundations of fuzzy set theory. Main approaches of fuzzy control, design issues, fundamental of neural networks, neural networks architecture, neural networks based controller design, and hybrid fuzzy-neural control. Case studies in industrial intelligent control systems including process control, aerospace and robotics.
The course introduces the concept of model predictive control (MPC), their importance in process industry, implementation issues and application examples. The course covers: model based predictive control, generalized MPC, constrained MPC, some commercial MPC, issues in implementation in industrial control systems and case studies
Pre-Requisites: CIE316 Or CISE316
Dynamical systems and their mathematical models, random variables and signals, The system identification procedure. Guiding principles behind least-squares parameter estimation, statistical properties of estimates. Identification of the transfer function of linear systems in continuous time. Models for discrete-time linear systems: FIR, AR, ARX, ARMA. Various methods for recursive estimation. Experiments for data acquisition and their design.
Pre-Requisites: (CIE301 Or CISE301) And (CIE318 Or CISE318)
Industrial instrumentation: measurement techniques in industrial processes. Computer data acquisition. NC and CNC machine tools. Computer process interfacing and control. Feedback control systems. Group technology. Flexible manufacturing systems. Automated assembly. Industrial robots. Computer-aided inspection and testing. Automated factories. Case studies.
Need for, advantages and basic structure of DSP systems. Basic concepts of discrete-time signals and systems. Z-Transform, discrete Fourier Transform (DFT) and frequency analysis of signals and systems. Efficient implementation of DFT: Fast Fourier Transform (FFT) algorithms. Implementation issues of discrete-time systems. Digital filter design techniques. Applications of DSP systems.
Pre-Requisites: CIE315 Or CISE315
Condition-based maintenance process. Data collection and Analysis process. Decision making. Condition-based monitoring components sensors and software programs. CMMS. Hazard and reliability functions. Models for CBM. Reliability improvement. Integration of CBM into the control design and operation. Engineering case studies. Prerequisite: CIE 305 or approval of the department
Pre-Requisites: CIE305 Or CISE305
Review of DC motors, optical encoders, precision control of DC motors, Stepper motors, control of stepper motors, micro-step control, gearboxes, belts, motor torque and power sizing, programming motion using G-code. Basic structure and functions of milling machines and lathes. Motion simulation, CAD/CAM system. Robot arms construction, analysis, and motion programming. Case study of retrofitting conventional machines with Computer Numerical Control.
Pre-Requisites: CIE318 Or CISE318
Hierarchy of plant communication systems, field equipment, DCS systems, SCADA systems, Supervisory control and production control, Man-Machine Interface (MMI). Local area networks, OSI network architectures, serial communications, IEEE 802.xx standards, Local area networks for industrial applications, Field buses, Hart protocol, Foundation Field Bus, Profibus, CAN bus, etc. Smart instruments. Examples of industrial DCS systems.
Pre-Requisites: CIE312 Or CISE312
Process and Instrumentation diagrams. Signal conditioning: 4-20 mA circuits, E/I transducers, bridges (AC and DC), design of bridges, operational amplifier circuits, filters (LP & HP), power supplies, reference voltages. Instrumentation for temperature and flow measurement in process industry. Ultrasonic and Infrared measurements. Introduction to fieldbus, Plant network hierarchy and DCS systems. LABVIEW, virtual instrumentation, Visual programming, and Human Machine Interface. Prerequisite: CIE 312 or Consent of the Instructor
Pre-Requisites: CIE312 Or CISE312
A course in an area of instrumentation reflecting current theory and practice. Prerequisite: Approval of the Department
Review of state variable models, Review of basic matrix algebra, Static optimization, Formulation of optimal control problems, Principle of optimality. The linear quadratic regulator problem, properties of the algebraic Riccati equation (ARE) The minimum principle and time optimal control problems. Output feedback design. Homework assignments include design and simulation using MATLAB or other similar software packages.
Pre-Requisites: CIE316 Or CISE316
Probability, Random Variables and distributions, correlation, MA, AR, and ARMA systems, power spectrum, Spectral factorization, Weiner-Hopf filter. Stochastic control systems, Minimum variance control, State-variable forms, Kalman filter, LQG feedback systems. Cases studies from published work.
Pre-Requisites: CIE316 Or CISE316
Analysis and Design. Review the basic methods and tools of Classical Control. Robust stabilization, Loop shaping, Introduction to H∞ Optimal Control Analysis and Synthesis. Design examples.
Pre-Requisites: CIE316 Or CISE316
A course in an area of control reflecting current theory and practice. Prerequisite: Approval of the Department
Basics of anatomy and biological science. Fundamentals of engineering applications in biomedicine. Biomedical instrumentation and information technology, control and communication in biomedicine. eHealth and telemedicine.
Review of basic Probability, Statistical Independence, Conditional Expectation and Characteristic Function. Introduction to Stochastic Processes, Stationarity and Ergodicity. Markov Chains and Poisson Processes. Linear Models of Continuous and Discrete Stochastic Processes. Engineering Applications.
Pre-Requisites: ISE205
An overview of large-scale problems and the framework for Systems Engineering. Graphic tools for Systems Engineering. Interaction matrices and graphs, interpretive structure modeling. Spare matrix and decomposition techniques. Model reduction techniques. Case studies.
High volume discrete parts production systems. Fundamentals of CAD/CAM. Computers in manufacturing. Computer process monitoring. Systems for manufacturing support. Group technology and integrated manufacturing systems. Case studies for robots in industry. CAD/CAM using computer graphics laboratory.
Micro-machined sensors, Fiber optical sensors, Gas chromatography, Gas detectors, Environment monitoring systems, NMR, Soft-sensing techniques.
Pre-Requisites: CIE312 Or CISE312
DCS systems, Intrinsic safety, Emergency shutdown ESD systems, reliability of instruments and control systems, MTBF, Redundant systems, Safety standards, Classification of industrial process, Safety integrity levels (SIL), Quantitative risk assessment (QRA), Safety and control networks, Fieldbus for safety systems, Cost benefit analysis, Best practices
in a practical theme. The course starts by previewing the main topics in communications systems such as modulation and coding. The course then covers the main communication network standards used in industry. The course covers mainly all data layers from the field instruments to the TCP/IP and world-wide web and even latest wireless data exchange techniques. Case studies of industrial DCS and CIM and their integration with the enterprise networks.
Pre-Requisites: CIE318 Or CISE318
Fundamental laws, mathematical modeling; modeling and simulation of typical processes, e.g., CSTR, Gas phase CSTR, , binary column, multi-component distillation columns, heat exchangers, boilers, compressor-turbine units, etc., and model linearization. Review of time domain analysis, feedback control, PID tuning, feed- forward, cascade control, ratio control, process decoupling, discrete systems, systems identification, IMC, Model predictive control, DMC. Prerequisite: CIE 418 or Approval of the Department
A course in an area of automation reflecting current theory and practice. Prerequisite: Approval of the Department
Dynamic Systems models; FIR, AR, ARX, ARMA, State space, Multiple models, nonlinear models, System performance evaluation, abnormality / loss of performance detection. Detection techniques; Filtering, whiteness test, parity checks, residuals autocorrelation tests. Applications and case studies. Prerequisite: CIE 315 or Approval of the Department
Pre-Requisites: CIE315 Or CISE315
Principles of intelligent measurement devices. Signal conditioning; typical measurement systems; temperature, pressure, force, and motion sensors; Sensors for oil logging, Resistivity measurements, neutron absorption, gamma ray methods, photo electric methods, acoustic methods; sensors networking; sensor fusion, soft sensing, sensor communications; wireless sensors networks. Prerequisites: (CIE 209, CIE 312) or Approval of the Department
Pre-Requisites: (CIE209 Or CISE209) And (CIE312 Or CISE312)
Maintainability, fault trees and failure mode analysis. Combinatorial reliability; series, parallel and r-out-of-n configuration; general computation techniques. Catastrophic failure models: hazard rate models. System reliability: Safety Integrity Level (SIL). Safety standards IEC 61508, IEC 61511 & ISA 84.01, basic process control system (BPCS) and Safety Instrumented System (SIS), functional safety, analysis of safety integrity level (SIL), case studies of SIS design. Prerequisite: Approval of the Department
Dynamic equations of rigid bodies; missile dynamic equations; introduction to missiles aerodynamics; linearization of the equations of motion; gain scheduling techniques; longitudinal equations of motion, longitudinal autopilot; missiles lateral dynamics; lateral autopilot; inertia cross coupling; advanced control systems; measurement of missile motion, gyros, laser gyros; guidance systems techniques and design, UAV system components and control issues. Prerequisite: CIE 314 or Approval of the Department
Pre-Requisites: CIE314 Or CISE314
Internet of Things (IoT) technology and Industrial Control Systems (ICS) for Industry 4.0, IoT/IIoT reference architectures and data flow, industrial communication technologies and networking protocols, highly distributed system architectures and computing platforms, digital twins, ICS security, predictive analytics, maintenance, and system optimization. Embedded intelligence in end devices to perform local analytics and optimization. Applications of IIoT in various areas such as energy sector, manufacturing, and smart cities.
Pre-Requisites: CIE318 Or CISE318 Or COE344 Or ICS343 Or EE400
Foundations of optimization theory. Unconstrained and constrained optimization. Necessary and sufficient conditions of optimality. Interactive techniques to solve unconstrained and constrained convex optimization problems. Engineering applications of convex optimization, with a special emphasis on sensing, decision and control. Prerequisites: CIE 301 or equivalent or Approval of Instructor
Pre-Requisites: CIE301 Or CISE301
Introduction to soft computing for Control and Automation, Neural models and network architectures; basic and advanced architectures and algorithms. Neural networks for control and identification, Adaptive neuro-control. Fuzzy systems, Construction of fuzzy inference systems; Objective vs. subjective fuzzy modeling and fuzzy rule generation, examples, Fuzzy control and identification, Stability analysis and design of fuzzy control system, Hybrid soft computing, construction of a hybrid soft computing system, Application of hybrid soft computing to control systems and automation, Case studies and projects in control and automation. Prerequisite: CIE 305 or Approval of the Department
Pre-Requisites: CIE305 Or CISE305
Introduction to the fundamentals of mobile robots, spanning the mechanical, motor, sensory, perceptual, and cognitive layers the field comprises. Overview of the mechanisms for locomotion, dynamic modelling, forward and inverse dynamics, sensing. Concepts of localization and motion planning control theory, signal analysis, computer vision.
Pre-Requisites: CIE305 Or CISE305 Or AE313 Or EE380 Or ME410 Or CHE401
Interplay between control and robotics. Kinematic and dynamic models of robot manipulators, mobile robots, and multi-rotor drones, design intelligent controls for robotic systems and explore modeling analogies between these systems. Learn linear/nonlinear, single and multiple input/output closed loop control, stability theories, feedback linearization, and robust control design. Basic system identification techniques and the concept of autopilot design for aircrafts and UAVs.
Pre-Requisites: CIE480 Or CISE480
Key concepts, algorithms and design of robot motion and navigation in the presence of obstacles and static and dynamic environments with uncertainty. Real-time feedback control to track the planned motion, C-space obstacles, grid-based motion planning, randomized sampling-based planners, and virtual potential fields. Motion and force control, flying robot trajectory design, UAV’s trajectory.
Pre-Requisites: CIE480 Or CISE480 Or AE449
Application of Artificial Intelligence (AI) and Machine Learning (ML) for robotic systems. Intelligent Agents (IA), blind/uninformed and informed search algorithms for path planning. Relational and associative navigation, behavior coordination, uncertainty, and probabilistic reasoning. Knowledge representation methods. Different types of IA architectures (operational, systems and technical) and layers (behavioral, deliberative, interface) within a canonical operational architecture of an intelligent robot. Logical agents, deductive and practical reasoning agents, reactive and hybrid agents, rational agents and how to use such techniques for creating autonomous robots/agents. Fundamentals and practical usage of Machine Learning (ML) algorithms, including supervised, unsupervised, reinforcement and evolutionary learning paradigms for implementing autonomous robots/agents.
This course introduces decision making under uncertainty from a computational perspective and provides an overview of the necessary tools for building autonomous and decision-support systems. Following an introduction to probabilistic models and decision theory, the course will cover computational methods for solving decision problems with stochastic dynamics, model uncertainty, and imperfect state information. Topics include Bayesian networks, influence diagrams, dynamic programming, reinforcement learning, and partially observable Markov decision processes. Prerequisites: (ISE 205 or STAT 211 or STAT 319), Senior Standing or Approval of the Department
Pre-Requisites: ISE205 Or STAT211 Or STAT319
A design course that should be taken by all coop and non-coop students, which draws upon various components of the undergraduate curriculum. The project; typically contains problem definition, analysis, evaluation and selection of alternatives. Real life applications are emphasized where appropriate constraints are considered. Oral presentation and a report are essential for course completion. The work should be supervised by faculty member(s). Team projects are acceptable wherever appropriate
Pre-Requisites: CIE390 Or CISE390