- ECE 602 / CO 602: Introduction to Optimization
- ECE 604 / STAT 901: Introduction to Stochastic Processes
- ECE 682: Multivariable Control Systems
- ECE 683: System Identification
- ECE 686: Filtering and Control of Stochastic Linear Systems
- ECE 687: Robot Dynamics and Control
- ECE 688: Nonlinear Systems
- ECE 781: Adaptive Control
- ECE 784: Introduction to Stochastic Calculus
Note: ECE 686 is cross-listed with ECE 486
Special Topics in Control
- ECE 780 T01: Sampled Data Control Systems
- ECE 780 T02: Discrete Event Systems
- ECE 780 T06: Humanoid Robotics
- ECE 780 T08: Topics in Motion Coordination and Planning
- ECE 780 T09: Network Systems and Control
Rising levels of automation pose new challenges for control. Control theory has historically been based on differential- or difference-equation models, and its primary preoccupation has been control synthesis for individual, small-scale subsystems. But today's highly computerized and networked control systems are complex, and require the coordination of large numbers of interacting subsystems. Such large-scale coordination problems are often best framed in terms of more high-level discrete-event models, based on state machines (or automata), formal languages, formal logic, etc. Since the early eighties, control scientists have been developing comprehensive approaches to the control of discrete event systems (DES); this course provides an introduction to this now well established branch of control.
This course provides an overview of the fundamentals and the recent research in the field of humanoid robotics. The course will cover kinematics and dynamics, postural stability, control, gait and trajectory generation and inertial parameter estimation. Additional advanced topics in learning, human-robot interaction and manipulation and grasping and human motion modeling will be covered as time permits.
This course will cover aspects of path planning, dynamic vehicle routing, and coordination for mobile robots. Topics include:
- Path planning: graph search methods; traveling salesman problems
- Multi-robot coordination: the consensus and rendezvous problems; sensor coverage; workspace partitioning/load balancing
- Dynamic vehicle routing: overview of Poisson processes and birth-death processes; path planning for tasks arriving in real-time; relation to automated material handling, mobility-on-demand
Many large-scale natural and engineering systems can be modelled as collections of independent agents or subsystems which interact with one another through physical coupling, communication, or both. Examples include flocking birds, schooling fish, electric power systems, mobile robot teams, and sensor networks. The dynamic behaviour of these network systems depends on the nature of the agents, the nature of the inter-agent physical or communication-based coupling, and most interestingly on the global pattern of interaction between all agents. This course is concerned with analyzing and designing the dynamics of multi-agent network systems, and is intended primarily for graduate students in engineering and applied math interested in dynamics over networks, cooperative and distributed control, and distributed algorithms. Topics include:
theory of nonnegative matrices (Perron–Frobenius theory);
graph theory, with an emphasis on algebraic graph theory;
discrete-time and continuous-time distributed averaging (consensus) algorithms;
positive and compartmental linear systems;
circuit theory: graph models, dynamic stability, effective resistance, model reduction;
distributed optimization using multi-agent systems;
applications to networks of coupled oscillators, viral spread models, sensor networks, power system dynamics, resource allocation problems, and population dynamics.
Equivalence relations and congruences. Morphisms, semigroups and monoids. Groups: cyclic groups, subgroups and quotient groups. Rings: subrings, quotient rings, integral domains and fields. Partial orders, lattices and fixed-points of monotone operators.