Realated Courses

044191 – Control Systems 1

Review of the representation of systems by transfer functions and state equations in continuous and discrete time. Controllability and observability, minimality, canonical forms. Methods for system stability analysis. Systems specifications, system types. The control problem, feedback properties. Control system design using compensation networks. Introduction to discrete time control. The Root-Locus method.


044192 – Control Systems 2

Pole shifting by state feedback. State estimator. Extensions of the concepts of controllability and observability. Lyapunov stability. Polynomial design. Control in the presence of stochastic disturbances. Introduction to LQG control. Special topics in discrete control, delays, controller realization, finite word length. Introduction to time-varying systems.


044193 – Linear Control Laboratory

DC motor tracking system; Digital simulation of a tracking system; P.I.D. controller; Flexible Joint controller; Rotational Inverted Pendulum; Linear Inverted Pendulum.


046195 -Machine Learning

An introductory course on learning systems in the context of signal processing, artificial intelligence and control. Problems of classification, regression and clustering. Neural networks: multi-level perceptrons and radial basis functions. Decision trees. Elements of the learning theory: the Bayesian approach, hypothesis spaces. Dimensionality reduction using principal components. Classification using support vector machines. Reinforcement learning.


046196 – Non-Linear Control Systems

Representation of nonlinear systems by differential equations, equilibrium states and their stability. State plane and describing function analysis of nonlinear control systems. Effect of nonlinear elements such as Coulomb friction and relays, nonlinear phenomena such as limit cycles and sliding modes. Lyapunov stability theory. The Popov and circle stability criteria. Design of robust control systems by sliding-mode and Lyapunov control, application to robot control. Introduction to adaptive control.


236756 – Introduction to Machine Learning

Automatic Acquisition of Knowledge Bases: Inductive and Performance of Problem Solvers. Discovery. Genetic Algorithms.


236760 – Computational Learning Theory

BOolean Functions. the Composition Theorem. Negative Results in Occam Theorem. Vc-Dimension. Boosting.


236927 – Introduction to Robotics

HOmogeneous Transformations, Kinematic Equations, Inverse Kinematics, Dynamics, Servo Control, Trajectory Planning, Compliance, Robot Navigation, Sensor-Based Robot Navigation. Robot Vision, Visual Servoing, Control Languages, Robot Intelligence and Task Pl


086755 -Dynamics and Control of Flight Vehicles 

EQuation of Motion in Six Degrees of Freedom. Linearizations and Approximations. Longitudinal and Lateral Models and Transfer Functions. Overview of Aerospace Sensors, Design of Longitudinal Autopilots, Lateral and Directional Autopilots, Coupling, Control.