Dynamic Systems Theory
Introduction to analytical concepts and examples of dynamic systems and control. Mathematical description and state space formation of dynamic systems; modeling, controllability, and observability. Eigenvector and transform analysis of linear systems including canonical forms. Performance specifications. State feedback: pole placement and the linear quadratic regulator. Introduction to MIMO design and system identification using computer tools and laboratory experiments. Meets with ENGEC501 and ENGME501; students may not receive credit for both.2016FALLENGSE501 A1, Sep 6th to Dec 8th 2016
|TR||12:00 pm||2:00 pm||PHO||201|
Optimization Theory and Methods
Introduction to optimization problems and algorithms emphasizing problem formulation, basic methodologies, and underlying mathematical structures. Classical optimization theory as well as recent advances in the field. Topics include modeling issues and formulations, simplex method, duality theory, sensitivity analysis, large-scale optimization, integer programming, interior-point methods, non-linear programming optimality conditions, gradient methods, and conjugate direction methods. Applications are considered; case studies included. Extensive paradigms from production planning and scheduling in manufacturing systems. Other illustrative applications include fleet management, air traffic flow management, optimal routing in communication networks, and optimal portfolio selection. Meets with ENGEC524. Students may not receive credit for both.2016FALLENGSE524 A1, Sep 6th to Dec 8th 2016
|TR||2:00 pm||4:00 pm||PHO||205|
Sustainable Power Systems: Planning, Operation and Markets
Breakthroughs in clean energy generation technologies and the advantage of exploiting efficiently the available work in fossil fuels will render electricity the dominant energy form in a sustainable environment future. We review the key characteristics of Electric Power Transmission and Distribution (T&D) networks and the associated planning and operation requirements that ensure supply adequacy, system security and stability. Capital asset investment and operation cost minimization is discussed in a systems engineering context where the assets as well as the dynamic behavior of generators, T&D networks, and loads interact. Recent developments in the formation of competitive wholesale markets at the High Voltage Transmission system level, the associated market participation and clearing rules and the market clearing optimization algorithms are presented and analyzed in terms of their effectiveness in fostering cost reflective price signals and competitive conditions that encourage optimal distributed/not-centralized investment and operating decisions. Finally, we present T&D congestion and supply-demand imbalance related barriers to the widespread adoption of environmentally friendly and economically efficient technological breakthroughs, and propose a systems engineering and real-time retail-market based coordination of centralized as well as decentralized generation, storage and load management resources that is able to achieve desirable synergies and mitigate these barriers. 4 cr.2017SPRGENGSE543 A1, Jan 23rd to May 3rd 2017
|MW||4:30 pm||6:15 pm|
Networking the Physical World
Considers the evolution of embedded network sensing systems with the introduction of wireless network connectivity. Key themes are computing optimized for resource constrained (cost, energy, memory and storage space) applications and sensing interfaces to connect to the physical world. Studies current technology for networked embedded network sensors including protocol standards. A laboratory component of the course introduces students to the unique characteristics of distributed sensor motes including programming, reliable communication, sensing modalities, calibration, and application development. Meets with ENGME544. Students may not receive credit for both. 4 cr.2016FALLENGSE544 A1, Sep 6th to Dec 9th 2016
|TR||2:00 pm||4:00 pm||PHO||117|
|F||12:00 pm||2:00 pm||PHO||117|
Advanced Special Topics
Advanced study of a specific research topic in systems engineering. Intended primarily for advanced graduate students. On Demand. Var cr.
Optimal and Robust Control
This course is aimed at an introduction (with rigorous treatment) to the fundamentals of optimal and robust control. It will be divided roughly into two parts. The first will cover aspects of robust control including model reduction, H_2 and H_ infinity control, and feedback control of uncertain systems. The second will delve into optimal control including topics such as the linear quadratic regulator, the calculus of variations, the maximum principle, and the Hamilton-Jacobi-Bellman equation. Meets with ENG EC701 and ME701. Only one of these courses may be taken for credit.
This course provides a theoretical foundation for developing adaptive controllers for dynamic systems. Topics include system identification, model reference adaptive control, adaptive pole placement control, and adaptive control of nonlinear systems. Meets with ENG ME 704. Students may not receive credit for both.
Dynamic Programming and Stochastic Control
Introduction to sequential decision making via dynamic programming. The principle of optimality as a unified approach to optimal control of dynamic systems and Markovian decision problems. Applications from control theory and operations research include linear-quadratic problems, the discrete Kalman Filter, inventory control, network, investment, and resource allocation models. Adaptive control and numerical solutions through successive approximation and policy iteration, suboptimal control, and neural network applications involving functional approximations and learning. Meets with ENGEC710 and ENGME710. Students may not receive credit for both.2017SPRGENGSE710 A1, Jan 23rd to May 3rd 2017
|MW||10:10 am||11:55 am|
Advanced Stochastic Modeling and Simulation
Introduction to Markov chains, point processes, diffusion processes as models of stochastic systems of practical interest. The course focuses on numerical and simulation methods for performance evaluation, optimization, and control of such systems. Meets with ENGME714. Students may not receive credit for both.2017SPRGENGSE714 A1, Jan 23rd to May 3rd 2017
|MW||2:30 pm||4:15 pm|
Advanced Optimization Theory and Methods
Complements ENGEC524 by introducing advanced optimization techniques. Emphasis on nonlinear optimization and recent developments in the field. Topics include: unconstrained optimization methods such as gradient and incremental gradient, conjugate direction, Newton and quasi-Newton methods; constrained optimization methods such as projection, feasible directions, barrier and interior point methods; duality; and stochastic approximation algorithms. Introduction to modern convex optimization including semi-definite programming, conic programming, and robust optimization. Applications drawn from control, production and capacity planning, resource allocation, communication and sensor networks, and bioinformatics. Meets with ENGEC724 and ENGME724. Students may not receive credit for both.2017SPRGENGSE724 A1, Jan 19th to May 2nd 2017
|TR||1:30 pm||3:15 pm|
Performance modeling using queueing networks, analysis of product form and non-product form networks, numerical methods for performance evaluation, approximate models of queueing systems, optimal design and control of queueing networks. Applications from manufacturing systems, computer systems and communication networks. Meets with ENGEC725 and ENGME725. Students may not receive credit for both.2016FALLENGSE725 A1, Sep 6th to Dec 8th 2016
|TR||12:00 pm||2:00 pm||PHO||205|
Combinatorial Optimization and Graph Algorithms
Design data structures and efficient algorithms for priority queues, minimum spanning trees, searching in graphs, strongly connected components, shortest paths, maximum matching, and maximum network flow. Some discussion of intractable problems and distributed network algorithms. Meets with ENGME732. Students may not receive credit for both.
Discrete Event and Hybrid Systems
Review of system theory fundamentals distinguishing between time-driven and event-driven dynamics. Modeling of Discrete Event and Hybrid Systems; Automata, Hybrid Automata, Petri Nets, basic queueing models, and stochastic flow models. Monte Carlo computer simulation: basic structure and output analysis. Analysis, control, and optimization techniques based on Markov Decision Process theory with applications to scheduling, resource allocation, and games of chance. Perturbation Analysis and Rapid Learning methods with applications to communication networks, manufacturing systems, and command-control. Meets with ENGEC733 and ENGME733. Students may not receive credit for both.2017SPRGENGSE733 A1, Jan 23rd to May 3rd 2017
|MW||12:20 pm||2:05 pm|
The course offers a detailed introduction to hybrid systems, which are dynamical systems combining continuous dynamics (modeled by differential equations) with discrete dynamics (modeled by automata). The covered topics include modeling, simulation, stability analysis, verification, and control of such systems. The course contains several applications from both natural and manmade environments, ranging from gene networks in biology, to networked embedded systems in avionics and automotive controls, and to motion planning and control in robotics. Same as ENG ME 734 and ENG SE 734. Students may receive credit for one. 4 cr. 1st sem.
Vision, Robotics, and Planning
Methodologies required for constructing and operating intelligent mechanisms. Comprehensive introduction to robot kinematics for motion planning. Dynamics and control of mechanical systems. Formal treatment of differential relationships for understanding the control of forces and torques at the end effector. Discussion of robot vision and sensing and advanced topics in robot mechanics, including elastic effects and kinematic redundancy. Meets with ENGME740. Students may not receive credit for both.2017SPRGENGSE740 A1, Jan 19th to May 2nd 2017
|TR||9:00 am||10:45 am|
Randomized Network Algorithms
Probabilistic techniques and paradigms in the design and evaluation of network algorithms. Review of basic concepts in probability, graph theory, and algorithms. Tail inequalities and Chernoff bounds. Ball and bins and random graph models. Markov chains and random walks. The probabilistic method. Monte Carlo methods. Introduction to martingales, networking applications: distributed content storage and look-up in P2P networks, IP traceback, fountain codes, universal hash functions, packet routing. Same as EC 741. Students may not receive credit for both.
Nonlinear Systems and Control
Introduction to the theory and design methods of non-linear control systems. Application to robotics, vibration and noise control, fluid control, manufacturing processes, and biomedical systems. Mathematical methods based on the theory of differentiable manifolds; non-linear control techniques include feedback linearization, back-stepping, forwarding, and sliding mode control. Additional course topics will include controllability and observability, Lyapunov stability and its applications, limit cycles, input-output stability, zero dynamics, center manifold theory, perturbation theory, and averaging.2017SPRGENGSE762 A1, Jan 19th to May 2nd 2017
|TR||1:30 pm||3:15 pm|
Production Systems Design
Theory and applications related to the design of complex production systems. Simulation theory, stochastic modeling and control, and mathematical decomposition techniques are developed and applied hierarchically to combine production statistics estimation, operations protocol design, and capacity selections in an integrated design of complex manufacturing systems. Meets with ENGME765. Students may nor receive credit for both.
Advanced Scheduling Models and Methods
Emphasizes basic methodological tools and recent advances for the solution of scheduling problems in both deterministic and stochastic settings. Models considered include classical scheduling models, DEDS, neural nets, queueing models, flow control models, and linear programming models. Methods of control and analysis include optimal control, dynamic programming, fuzzy control, adaptive control, hierarchical control, genetic algorithms, simulated annealing, Lagrangian relaxation, and heavy traffic approximations. Examples and case studies focus on applications from manufacturing systems, computer and communication networks, and transportation systems. Meets with ENGME766. Students may not receive credit for both.
Participation in a research project under the direction of a faculty advisor leading to the preparation and defense of a PhD prospectus.2016FALLENGSE900 G3, Sep 6th to Dec 12th 2016