Linear systems, modeling of physical systems, Ordinary Differential equations models, Laplace Transform, transfer functions, block diagram manipulation. Open loop and closed loop systems, time domain analysis, response of systems to different test signals, Steady state analysis. Transient and Steady-State analysis and design specifications. Root locus, Concept of stability, Design using Root locus. Frequency Response Techniques, Bode plot, Nyquist plot, Specifications and controller Design in the Frequency domain. State-space model, analysis of the state-space model, Controllability and Observability, state feedback control systems. Note: This course is intended to be a deficiency makeup course for students with inadequate background in the SCE field. The course will not be counted for credit in the SCE undergraduate, Master, or Ph.D. programs.

An integrated treatment of linear continuous-time control theory; Input/output representations and state space realizations; Canonical forms, Transformations: Nerode equivalence, Geometric interpretations; Matrix Fraction Descriptions: observability, controllability matrices, minimal realizations, polynomial matrices; Concepts and structural properties: stability/stabilizability, controllability/ reachability, and observability/detectability; State feedback and compensator design; full and reduced order state-observer; Output feedback; Time-variant state equations: controllability and observability Gramians.

This course gives theoretical and practical knowledge of methods to develop mathematical models from experimental data. Parametric and non-parametric methods; Review of modeling principles; Process identification from step response; Frequency response identification; Commonly used Signals, spectral Properties, persistent excitation; Correlation methods; Least squares identification; Recursive LS techniques; determining model orders; model validation; AR, MA modeling of system, linear prediction; Multidimensional systems; Application and case studies. Prerequisite Graduate Standing (Not be taken for credit with EE551)

Performance measures for dynamic control problems: minimum time, regulator, servo mechanisms, minimum energy; System norms and transformation; Dynamic programming; Linear quadratic (LQ) and Linear quadratic Gaussian (LQG) regulators; Hardy spaces; Uncertainty and robustness; H2 Optimal control; Hoo control; Tracking and Set-point optimal Regulator; Discrete-time systems; Case Studies.

Review of multivariable probability distributions, stochastic systems, Correlation and power spectral density functions, Conditional probability, Baye's theorem, Statistical independence, Expectation and conditional expectation. Stochastic processes, ergodicity, stationarity, 1st, 2nd and higher order statistics (HOS), Spectral factorization, Minimum variance control problem, LQG, optimal control, Bellman equation.

Stochastic state space model; properties of Wiener process; stochastic differential equation; linear optimal filtering and prediction; Kalman filter and Wiener-Hopf filter; fixed lag smoothing and fixed point smoothing; filtering and prediction using stochastic ARMA model; extended Kalman filter; parameter estimation for stochastic dynamic systems; adaptive filtering and prediction.

Introduction to nonlinear models; Second-order systems: phase portraits, equilibrium, limit cycles, bifurcation; Stability of equilibrium points; Liapunov Stability; Passivity: models and criteria; IS and IO Stability: ultimate bounds, L2 gain and small-gain theorems; Nonlinear forms; Stabilization; Tracking; Observers and integral control; Engineering case studies.

Fundamental laws, mathematical modeling; modeling and simulation of typical processes, e.g., CSTR, Gas phase CSTR, Vaporizers, Batch reactors, 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, Neural Network modeling and control. Prerequisites: Graduate Standing, can not be taken for credit with CHE 562

Overview of the control hierarchy from business systems to basic control loops. Network architecture, Smart sensors and Data Collection. Design of control structures and strategies for large plants. Modelling and System Identification, Commonly used controller functions in basic control loops. Analysis and compensation of interactions in multivariable systems. Observability Controllability and Stability analysis of MIMO systems. Analysis of limitations in achievable performance. Nonlinear control Strategies. Applied model-based estimation and predictive control (MPC). Control performance monitoring and diagnosis. Prerequisite: CISE 305 or ME 413, or EE 380 or CHE 401 or equivalent

General approach to controller design; Adaptive control methods; Model reference adaptive systems, parametric optimization methods; Lyapunov function method; Hyperstability and positivity concepts; Self-tuning controllers; minimum variance self-tuner; explicit and implicit algorithms; pole assignment regulators; variable structure systems; sliding motion; choice of control function; control of phase canonic models; Applications. Prerequisites: SCE 507 or equivalent (cross-listed with EE 651)

Characteristic of large scale systems; Analysis and design procedures; Model aggregation and perturbations; Concepts of decentralized control; Time and frequency domain techniques; Interconnected linear regulator problem; System decomposition and multilevel coordination; Hierarchical control methods; Singularly-perturbed systems; Overlapping techniques. Not to be taken for credit with EE 654

Dynamic Systems models; FIR, AR, ARX, ARMA, State space, Multiple models, nonlinear models, System performance evaluation, abnormality / loss of performance detection. Detection techniques; Filtering, CUSUM, Likelihood tests, change point estimation, whiteness test, parity checks, residuals autocorrelation tests. Applications and case studies.

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

Review of 1-D time- and frequency-domain representation of signals and systems; Transformation representation of LTI systems; Digital filter (FIR and IIR) design and structures; Analysis of finite-length effects in Digital filters; Spectral Analysis; Introduction to multi-rate DSP; DSP applications and hardware. Pre-requisite: Graduate Standing (cross listed with EE 562).

Distributed control systems configuration, Plant control hierarchy; Control networks; Internet SCADA systems; Field buses; OPC; Reliability and Safety Instrumented Systems; Function blocks and Software components in DCS systems, Future trends in distributed computer control.

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, softsensing, sensor communications; wireless sensors networks.

The objective of the course is to provide students with the latest developments in the area of condition-based maintenance. The course will emphasize modeling, diagnosis and use of CBM in industries such as petrochemical, electrical power and aerospace.

Intelligent maintenance components. System Operation in a lifecycle perspective. Type of failures in equipment and machinery during operation, and failure causes. The influence of these failures on efficiency, safety and the environment. Condition monitoring and inspection methods. Sensing technology, sensor network and IoT. Artificial Intelligence (AI) and Machine learning algorithms such as Neural Networks (Deep), Support Vector Machine, reinforcement learning and others for the detection of precursor for failure and their root causes; and for preventive and predicative maintenance. Case studies process control actuators, Available Industrial tools strength and limitations.

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.

Plant instrumentation and control hierarchy, SCADA and DCS Systems; industrial computer buses and interfaces, LAN transmission media and network types, OSI model, Industrial Ethernet; TCP/IP model and internet-based SCADA; Industrial Fieldbuses, HART communication protocol, Foundation Fieldbus, PROFIBUS, MODBUS, CAN bus; Industrial wireless networks, key wireless communication concepts, Wireless LAN, Wireless HART, ISA100.11, ZigBee network; OPC, OPC-UA protocols. Applications in process control (Downstream and Upstream processes). Prerequisite: Graduate Standing

Remote control systems and SCADA architecture of heterogamous systems; introduction to network layers structure; transmission media; Internet; effect of time delay and packet loss; Radio propagation fundamentals; Signal modulation and coding, communication protocols, radio transmitter/receivers, PWM, DSSS, FHSS, OFDM; Wireless networks for Automation; determinism and reliability; Cluster Tree and Mesh networks; Standards for WL in automation; GPRS, RFID; IEE802.15.4; Wireless HART; ISA 100.11; Security measures for Wireless and Internet in SCADA and control applications. communication, command, and control systems; unmanned air vehicles.

Securing the Industrial Control System (ICS) architecture. ICS cybersecurity, relevant topics in security controls the hardware, network and software levels; Attacks methods, Available security solutions for ICS including Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control Systems (DCS), Programmable Logic Controllers (PLC), Industrial Internet of Things (IIoT) and other process control systems.

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.

The course explores in detail the interrelationships between the architecture and systems software of a modern minicomputer: configuration; real-time operating systems; memory management; interactive editor, program scheduling; priority levels; swapping; input/output control; resource management. Real time programming languages.

SCADA systems; industrial computer systems; Computer buses; Signal conditioning; ultrasonic measurement; vibration measurements; Special purpose sensors; MEMs; gas chromatography; mass spectroscopy; infrared Systems, Fiber optics sensors.

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.

Introduction to Intelligent systems, model and knowledge based systems, Adaptive systems and learning systems, Modeling using dynamic neuron-fuzzy networks, Expert and Fuzzy systems. Evolutionary programming and design, Hybrid neural networks with Bayesian belief networks and HMMs techniques, Hierarchical evolutionary neuron-fuzzy systems, Application of neuro-fuzzy systems to control and optimization of large scale systems, Hybrid neuro-fuzzy systems for smart machine design, examples, Multi-objective control system and optimization, Neuron-fuzzy predictive control systems. Intelligent systems in real world applications Prerequisite: Graduate standing (cross listed with EE556)

Application of soft computing in the field of system identification. Main steps of identification, parametric and non-parametric identification, Neural network modelling, Fuzzy systems and fuzzy-neural approaches to identification, model classification and validation, data driven models using neural network. Both theoretical and practical questions are discussed. Some real world case studies are covered.

Introduction to optimal control solution techniques for systems with known and unknown dynamics. Dynamic programming, Hamilton-Jacobi reachability, and direct and indirect methods for trajectory optimization. Introduction to model predictive control. Model-based and model-free reinforcement learning, and connections between modern reinforcement learning and fundamental optimal control ideas

Control using fuzzy expert system, neural network and evolutionary strategies; hybrid intelligent controllers – model-free and model-based control, evolutionary-fuzzy, neuro-fuzzy, evolutionary neuro-fuzzy and adaptive neuro-fuzzy control; Stability analysis; Applications of AI to process control.

Basic concepts in robotics. Mathematical description of multi-joint robots. Homogeneous transformation. Forward and reverse kinematics. Mathematical modeling of multi-joint Dynamics. Newton-Euler and Lagrange Formulation. Generalized D’Alembert Equations of Motion. Robot Dynamic Control.

Dynamic and Kinematic analysis of robot manipulators; sensors (position, velocity, force, vision, tactile) actuators and power transmission; direct drive and indirect drive; point to point control; straight and curved path following; industrial practice in servo control; application of optimal linear quadratic control; nonlinear control and compliance control; collision avoidance; modeling and control of robots in the manufacture environment. Guided vehicles and their industrial applications.

Modeling of mechatronic system composed of mechanical, electronic, fluid, and thermal components. Sensors, IR, optical encoders, acoustic/ultrasonic, light/color sensor, thermal sensors; robotics vision; Motors DC/AC, stepper, motor modeling and control; actuators; embedded systems and realtime systems; Mobile robot platform; motion planning and guidance; mission management; Guided and autonomous systems;

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 basic linear/nonlinear, single and multiple input/output closed loop control, Introduction to stability theories, feedback linearization, and robust control design. Basic system identification techniques and the concept of autopilot design for aircrafts and UAVS. Prerequisites: AE 581, cannot be taken for credit with CISE 481

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, Cspace obstacles, grid-based motion planning, randomized sampling-based planners, and virtual potential fields. Motion and force control, flying robot trajectory design, UAV’s trajectory.

Principles, methodologies, and algorithms in Human-Robot-Interaction (HRI) such as HRI design methods, cognition and visual perception, thinking and actions, autonomy, shared control, remote presence, robot learning, task planning, attention, priming, trust, acceptance, motion control for HRI. Verbal and nonverbal interaction (i.e. speech recognition, natural language understanding, robot gaze, eye movement, touch, gesture and facial recognition, emotion and intention recognition, etc.). Sources of uncertainty in HRI, and ethical considerations for HRI by bringing together knowledge from robotics, artificial intelligence, human-computer interaction, cognitive psychology, etc. Computational models of social intelligence, physical embodiment, mixed-initiative interaction, multi-modal interfaces, human-robot teamwork, robot learning from human, aspects of social cognition, and long-term interaction.

Advanced topics are selected from the broad area of Systems and Control Theory. The contents of the course are given in detail one semester in advance of that in which it is to be offered. The approval of the Graduate Council will be necessary for offering this course.

Advanced topics are selected from the broad area of Instrumentation and DSP. The contents of the course are given in detail one semester in advance of that in which it is to be offered. The approval of the Graduate Council will be necessary for offering this course

Advanced topics are selected from the broad area of Control Applications. The contents of the course are given in detail one semester in advance of that in which it is to be offered. The approval of the Graduate Council will be necessary for offering this course.

Advanced topics are selected from the broad area of Intelligent Automation and Robotics. The contents of the course are given in detail one semester in advance of that in which it is to be offered. The approval of the Graduate Council will be necessary for offering this course.

Graduate students working towards their M.S. or Ph.D. degrees are required to attend the seminars given by faculty, visiting scholars, and fellow graduate students. Additionally each student must present at least one seminar on a timely research topic. Among other things, this course is designed to give the student an overview of research in the department, and a familiarity with the research methodology, journals and professional societies in his discipline. Graded on a Pass or Fail basis.

This advanced project course is arranged between a faculty member and a student to train students in undertaking implementation projects and to explore new technologies in their fields. Students are asked to prepare a study and submit a report on a feasible application of advanced knowledge in the SCE field. This report should include an introduction to the topic, literature review, research methodology, analysis of data, conclusions and recommendations, appendices and references. The report will be presented and orally examined by a faculty committee. (Must be taken by all M.Eng., open for M.Eng. option only).

Pre-Requisites: SCE599*

Co-Requisites: SCE 599

The Advanced project courses are arranged between a faculty member and a student to train students in undertaking implementation projects and to explore new technologies in their fields. In these courses students are asked to prepare a feasible application of advanced knowledge in the SCE field. The work will be evaluated based on a report, a seminar and/or an oral examination. (Open to M.Eng. option only)

This course is intended to allow the student to conduct research in advanced problems in his MS research area. The faculty offering the course should submit a research plan to be approved by the graduate program committee at the academic department. The student is expected to deliver a public seminar and a report on his research outcomes at the end of the course. Prerequisite: prior arrangement with an instructor

None

Pre-Requisites: SCE599 Or SE599

Advanced Methods for Control Systems : Introduction to Hilbert Spaces; Banach Spaces; and Hardy Spaces; Laurent, Hankel, and Toeplitz Operators; parameterization of all stabilizing controllers (Youla’s parameterization); factorization theory; model matching problem; Nehari’s Theorem; Wiener–Hopf optimal controllers; H∞ optimization problem; model reduction; l1-optimal control and other state of the art control system synthesis methods.

Argument principle; Rouche’s Theorem; chordal metric; Concepts of uncertainty and robustness in control systems design; unstructured uncertainty; structured uncertainty; real parameter uncertainty; necessary and sufficient conditions for robust stability; structured singular value (µ, time varying uncertainty, etc.); H2, Hoo and H2/Hoo design methods; Engineering Applications Prerequisites:, SCE 514 or equivalent

Theory of nonlinear filtering, propagation of the conditional probability density function, moment closure problem, nonlinear filtering approximations, EKF, Gaussian sum approximation, higher order approximations. Particle filtering and UKF techniques. Computational aspects of nonlinear filtering

A graduate student will arrange with a faculty member to conduct an industrial research project related to the cybersecurity as the field of the study. Subsequently the students shall acquire skills and gain experiences in developing and running actual industry-based project. This project culminates in the writing of a technical report, and an oral technical presentation in front of a board of professors and industry experts.

Synthesis and implementation of digital control systems for complex systems; control configurations; process modeling and identification; dynamic matrix control and internal model control; adaptive control systems; Supervisory and optimizing control; applications and case studies for distillation, combustion, heat exchangers, and flow reactors; recent developments in computer process control.

Pre-Requisites: SCE518 Or SCE518

2-D time- and frequency-domain representation of signals and systems, discrete random process. Linear prediction. Least squares (LS) and Recursive Least (RLS) Techniques with applications to Filter Design, System Modeling and array signal processing. Power spectrum Estimation. Cepstral Analysis, Selective Coverage of latest tools used in signal processing such as Neural nets, Higher-Order Statistics and Wavelets. Applications.

Speech production models; acoustical properties of vocal tract; classification of speech sounds, application to Arabic speech; time and frequency domain models for speech production; linear prediction methods; pitch detection algorithms; formant frequency trajectories; homomorphic speech processing; acoustic properties of Arabic sounds; allophone and Diphone techniques for speech synthesis; speech coding techniques; vector quantization; vocoders; speech recognition; distance measures; dynamic programming for template matching; Hidden Markov Model HMM techniques, application to phonetics based Arabic speech recognition. Prerequisite: Graduate standing (Not to be taken for credit with EE563).

Basic problem and methods; pattern classification; feature extraction and learning methods; heuristic search techniques; goal directed and ordered search; representation techniques; production systems; semantic networks and frames; input/output systems; problem solving and expert systems; expert systems in automation systems, CAD/CAM, material handling, scheduling, and process control.

Computer processing and recognition of pictorial data; mathematical description of images and human perception picture digitization and encoding; image processing hardware; unitary transforms and image compression; image enhancement, restoration, and segmentation; shape description and pattern recognition; application to motion estimation. Robot automatic guidance, image tracking systems, feature extraction similarity measures, clustering techniques, syntactic methods in pattern recognition and applications.

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

Intelligent robots, Sensor-based Estimation, Vision and Image Analysis, Probabilistic Robotics, localization, navigation, and mapping, SLAM Problem, Principles of Decision-Making, Neuro-Fuzzy and soft computing systems in Robotics, Special Topics in Advanced Robotics, Autonomous systems, Hybrid architectures, Complex robotic systems, Multi-Robot Systems, Intelligent learning and control of multi-robot systems, case studies and projects

The objective of this course is to select a specific area in Systems & Control and study cases and research papers in it to enable the student to conduct research at the frontier of the area. The specific contents of the special topic will be given in detail at least one semester in advance of that in which it will be offered. The approval of the Graduate Council will be necessary for offering this course.

The objective of this course is to select a specific area in Instrumentation or Digital Signal Processing and study cases and research papers in it to enable the student to conduct research at the frontier of the area. The specific contents of the special topic will be given in detail at least one semester in advance of that in which it will be offered. The approval of the Graduate Council will be necessary for offering this course.

The objective of this course is to select a specific area in Control Applications and study cases and research papers in it to enable the student to conduct research at the frontier of the area. The specific contents of the special topic will be given in detail at least one semester in advance of that in which it will be offered. The approval of the Graduate Council will be necessary for offering this course.

The objective of this course is to select a specific area in Automation, Robotics and Intelligent System, and study cases and research papers in it to enable the student to conduct research at the frontier of the area. The specific contents of the special topic will be given in detail at least one semester in advance of that in which it will be offered. The approval of the Graduate Council will be necessary for offering this course.

PhD students are required to attend Departmental seminars delivered by faculty, visiting scholars and graduate students. Additionally, each Ph.D. student should present at least one seminar on a timely research topic. Ph.D. students should pass the comprehensive examination as part of this course. This course is a pre-requisite to registering the PhD Dissertation SCE-710. The course is graded as pass or fail.

This course is intended to allow the student to conduct research in advanced problems in his PhD research area. The faculty offering the course should submit a research plan to be approved by the graduate program committee at the academic department. The student is expected to deliver a public seminar and a report on his research outcomes at the end of the course. Prerequisite: prior arrangement with an instructor

This course is intended to allow the student to conduct research in advanced problems in his PhD research area. The faculty offering the course should submit a research plan to be approved by the graduate program committee at the academic department. The student is expected to deliver a public seminar and a report on his research outcomes at the end of the course. Prerequisite: prior arrangement with an instructor

This course enables the students to submit his PhD Dissertation proposal and defend it in public. The student passes the course if the PhD Dissertation committee accepts the submitted dissertation report and upon successfully passing the Dissertation proposal public defense. The course grade can be NP, NF.

This course enables the students to work on his PhD Dissertation as per the submitted dissertation proposal, submit its final report and defend it in public. The student passes the course if the PhD Dissertation committee accepts the submitted final dissertation report and upon successfully passing the Dissertation public defense. The course grade can be NP, NF or IP.