Introduction To Digital Control Of Linear Time ...

DOWNLOAD >>>>> __https://ssurll.com/2tCYEp__

You can tune compensator parameters using interactive techniques such as Bode loop shaping and the root locus method. The toolbox automatically tunes both SISO and MIMO compensators, including PID controllers. Compensators can include multiple tunable blocks spanning several feedback loops. You can tune gain-scheduled controllers and specify multiple tuning objectives, such as reference tracking, disturbance rejection, and stability margins. You can validate your design by verifying rise time, overshoot, settling time, gain and phase margins, and other requirements.

Create linear time-invariant system models using transfer function or state-space representations. Manipulate PID controllers and frequency response data. Model systems that are SISO or MIMO, and continuous or discrete. Build complex block diagrams by connecting basic models in series, parallel, or feedback.

Use command-line functions or interactive Live Editor Tasks to resample dynamic system models and convert models between continuous-time and discrete-time domains. Use zero-order hold, bilinear (Tustin), zero-pole matching, and other rate conversion methods.

Compute gain margin, phase margin, and crossover frequencies. Examine pole and zero locations of dynamic systems graphically and numerically. Calculate the damping ratio, natural frequency, and time constant of the poles of a linear model.

Use the PID Tuner app, Live Editor Task, or command-line functions to automatically tune PID controller gains to balance performance and robustness. Specify tuning parameters, such as desired response time and phase margin. Tune continuous or discrete PID controllers.

Visualize closed-loop and open-loop responses with step response, Nyquist, and other plots that dynamically update as you tune your controller. Specify and evaluate time-domain and frequency-domain design requirements such as rise time, maximum overshoot, gain margin, and phase margin.

E E 205 Introduction to Signal Conditioning (4) RSNIntroduces analog circuits interfacing sensors to digital systems. /includes connection, attenuation, amplification, sampling, filtering, termination, controls, Kirchhoff's Laws, sources, resistors, op amps, capacitors, inductors, PSice, and MATLAB. Intended for non-EE majors. Prerequisite: either MATH 126 or MATH 136; and either PHYS 122 or PHYS 142. Offered: W.View course details in MyPlan: E E 205

E E 417 Modern Wireless Communications (4)Introduction to wireless networks as an application of basic communication theorems. Examines modulation techniques for digital communications, signal space, optimum receiver design, error performance, error control coding for high reliability, mulitpath fading and its effects, RF link budget analysis, WiFi and Wimax systems. Prerequisite: E E 416View course details in MyPlan: E E 417

E E 442 Digital Signals and Filtering (3)Methods and techniques for digital signal processing. Review of sampling theorems, A/D and D/A converters. Demodulation by quadrature sampling. Z-transform methods, system functions, linear shift-invariant systems, difference equations. Signal flow graphs for digital networks, canonical forms. Design of digital filters, practical considerations, IIR and FIR filters. Digital Fourier transforms and FFT techniques. Prerequisite: a minimum grade of 1.0 in either E E 341 or E E 342.View course details in MyPlan: E E 442

E E 445 Fundamentals of Optimization and Machine Learning (4)Introduction to optimization and machine learning models motivated by their application in areas including statistics, decision-making and control, and communication and signal processing. Topics include convex sets and functions, convex optimization problems and properties, convex modeling, duality, linear and quadratic programming, with emphasis on usage in machine learning problems including regularized linear regression and classification. Prerequisite: either MATH 224 or MATH 324; either MATH 136, MATH 208, MATH 308, or AMATH 352; and either E E 235, E E 242, or CSE 163.View course details in MyPlan: E E 445

E E 474 Introduction to Embedded Systems (4)Introduces the specification, design, development, and test of real time embedded system software. Use of a modern embedded microcomputer or microcontroller as a target environment for a series of laboratory projects and a comprehensive final project. Prerequisite: CSE 123 or CSE 143 Offered: jointly with CSE 474; AWSpS.View course details in MyPlan: E E 474

E E 476 Introduction to Very Large-Scale Integrated Design (5)Breadth-first introduction to digital VLSI design. Integrated CMOS logic design. CMOS logic delay and power analysis. Introduction to IC- mask-layout, gate-sizing, VLSI building blocks (adders, multipliers, counters, shifters etc.), design for testability, and memory. Projects involve some layout design, and mostly transistor and gate-level schematic design. Prerequisite: E E 215; and either E E 271 or CSE 369; recommended: basic circuit theory and basic digital design experience.View course details in MyPlan: E E 476

E E 478 Capstone Integrated Digital Design Projects (5)VLSI-capstone course. A more detailed examination of building high-performance or low-energy integrated circuits. Wire design, timing-elements, clock generation, distribution and control, dynamic-logic, low-power design. Cannot be taken for credit if credit received for E E 526. Prerequisite: E E 331; E E 332, which may be taken concurrently; E E 476; and E E 477; recommended: introduction to VLSI design and knowledge of ASIC design flows.View course details in MyPlan: E E 478

E E 508 Stochastic Processes in Engineering (3)Non-measure theoretic introduction to stochastic processes. Topics include Poisson processes, renewal processes, Markov and semi-Markov processes, Brownian motion, and martingales, with applications to problems in queuing, supply chain management, signal processing, control, and communications. Prerequisite: E E 505. Offered: jointly with IND E 508.View course details in MyPlan: E E 508

E E 526 Capstone Integrated Digital Design Projects (5)Very large-scale integration (VLSI) capstone course. A more detailed examination of building high-performance or low-energy integrated circuits. Wire design, timing-elements, clock generation, distribution and control, dynamic-logic, low-power design. Cannot be taken for credit if credit received for E E 478. Prerequisite: E E 331; E E 332, which may be taken concurrently; E E 476; and either E E 477 or E E 525; recommended: introduction to VLSI design and knowledge of application-specific integrated circuit (ASIC) design flows.View course details in MyPlan: E E 526

E E 534 Electric Drives (5)Analysis and design of dc-dc converters and dc-ac drives with closed-loop digital control; printed circuit board layout, component selection, circuit debugging, and programming of embedded control systems. Includes use of circuit simulators and application of circuit analysis methods. Prerequisite: a minimum grade of 1.0 in either E E 458 or E E 533.View course details in MyPlan: E E 534

E E 546 Advanced Topics in Control System Theory (1-5, max. 16)Topics of current interest in control system theory for advanced graduate students with adequate preparation in linear and nonlinear system theory. Prerequisite: permission of instructor. Offered when adequate enrollment develops prior to close of advance registration.View course details in MyPlan: E E 546

E E 557 Dynamics of Controlled Systems (4)Explores control techniques for high precision motion control. Covers sate variable feedback of linear and nonlinear, multivariable systems in depth. Uses physical system modeling, graphical analysis, and numerical analysis to describe system performance. Uses simulation mini-projects to emphasize the dynamics of controlled systems and their performance.View course details in MyPlan: E E 557

E E 581 Digital Control System Design (4)Digital control system design by classical methods. Discrete-time systems and the z-transform. Modeling sampled-data systems. Frequency response of discrete time systems and aliasing. Nyquist stability criterion and gain and phase margins. Discrete-time control law determination by direct z-plane root locus and loop shaping methods. Includes hands-on-with-hardware projects. Prerequisite: AA/EE 447 or ME 471. Offered: jointly with A A 581/M E 581; W.View course details in MyPlan: E E 581

E E 585 System Identification and Adaptive Control (3)Theory and methods of system identification and adaptive control. Identification of linear-in-parameter systems, using recursive LS and extended LS methods; model order selection. Indirect and direct adaptive control. Controller synthesis, transient and stability properties. Offered: jointly with A A 585/M E 585.View course details in MyPlan: E E 585

E E 594 Robust Control (3)Basic foundations of linear analysis and control theory, model realization and reduction, balanced realization and truncation, stabilization problem, coprime factorizations, Youla parameterization, matrix inequalities, H-infinity and H2 control, KYP lemma, uncertain systems, robust H2, integral quadratic constraints, linear parameter varying synthesis, applications of robust control. Prerequisite: A A 547/E E 547/M E 547. Offered: jointly with A A 594/M E 594; Sp, odd years.View course details in MyPlan: E E 594

Mathematics is the lingua franca in many of the graduate control courses. None of the control courses has a specific math course as prerequisite; instead, the relevant mathematics is taught in the course as needed. Thousands of students have made it through the control courses without taking supplemental mathematics courses. On the other hand, many students have found it helpful to sharpen their mathematical background when taking control courses as this allows them to concentrate on the application of the mathematics in an engineering setting, instead of having to learn a new mathematical concept, and how to apply it