printer friendly pageAA 212: Advanced Feedback Control Design
Analysis and design techniques for multivariable feedback systems. State-space concepts, observability, controllability, eigenvalues, eigenvectors, stability, and canonical representations. Approaches for robust feedback control design, chiefly H2, H-infinity, and mu-synthesis. System identification and adaptive control design. Use of computer-aided design with MATLAB. Prerequisite:
ENGR 105,
ENGR 205. Recommended: Linear algebra (
EE 263 or equivalent).
Terms: Win
| Units: 3
AA 229: Advanced Topics in Sequential Decision Making (CS 239)
Survey of recent research advances in intelligent decision making for dynamic environments from a computational perspective. Efficient algorithms for single and multiagent planning in situations where a model of the environment may or may not be known. Partially observable Markov decision processes, approximate dynamic programming, and reinforcement learning. New approaches for overcoming challenges in generalization from experience, exploration of the environment, and model representation so that these methods can scale to real problems in a variety of domains including aerospace, air traffic control, and robotics. Students are expected to produce an original research paper on a relevant topic. Prerequisites:
AA 228/
CS 238 or
CS 221.
Terms: Win
| Units: 3-4
Instructors:
Kochenderfer, M. (PI)
;
Jamgochian, A. (TA)
AA 236B: Spacecraft Design Laboratory
Continuation of
AA 236A. This course focuses on the design and implementation of spacecraft deployable structure systems. Students will work in teams to propose a solution for a large deployable structure for small satellites. Students will demonstrate that their design meets functional, mechanical, and environmental requirements using analysis, prototyping, and testing. Prerequisite:
AA 236A or consent of instructor.
Terms: Win
| Units: 4
Instructors:
Sakovsky, M. (PI)
;
Ramos del Valle, A. (TA)
AA 240: Analysis of Structures
Analyses of solid and thin walled section beams, trusses, frames, rings, monocoque and semimonocoque structures. Determination of stresses, strains, and deformations, and failure in structures; structural stability and buckling; material behavior: plasticity and fracture. Emphasis on energy methods and introduction of finite element methods. Prerequisite:
ENGR 14 or equivalent.
Terms: Win
| Units: 3
AA 241A: Aircraft Design: Synthesis and Analysis
First part of a two-quarter course dealing with multidisciplinary aspects of new aircraft systems development, emphasizing commercial aircraft. AA241A focuses on the vehicle design requirements, fuselage layout, low and high-speed aerodynamics, wing design, and aircraft stability/control, while AA241B deals with propulsion, structures, loads, mission performance, and economics. Students choose a set of economic goals and environmental constraints, developing their own designs individually. To obtain the maximum benefit from this course sequence, students are encouraged to enroll in both courses in consecutive quarters. Prerequisites : undergraduate and/or graduate courses in aerodynamics, aircraft design, or equivalent.
Terms: Win
| Units: 3
Instructors:
Kroo, I. (PI)
;
Wang, R. (TA)
AA 244A: Introduction to Plasma Physics and Engineering
Physics and engineering of plasmas, including space and laboratory plasmas. Debye length and distribution functions. Single-particle motion and drifts. Plasmas as fluids and fluid drifts. Waves in plasmas, including electrostatic and electromagnetic. Diffusion and resistivity. Magnetohydrodynamics.
Terms: Win
| Units: 3
AA 274B: Principles of Robot Autonomy II (AA 174B, CS 237B, EE 260B)
This course teaches advanced principles for endowing mobile autonomous robots with capabilities to autonomously learn new skills and to physically interact with the environment and with humans. It also provides an overview of different robot system architectures. Concepts that will be covered in the course are: Reinforcement Learning and its relationship to optimal control, contact and dynamics models for prehensile and non-prehensile robot manipulation, imitation learning and human intent inference, as well as different system architectures and their verification. Students will earn the theoretical foundations for these concepts and implement them on mobile manipulation platforms. In homeworks, the Robot Operating System (ROS) will be used extensively for demonstrations and hands-on activities. Prerequisites: CS106A or equivalent,
CME 100 or equivalent (for linear algebra),
CME 106 or equivalent (for probability theory), and AA 171/274.
Terms: Win
| Units: 3-4
Instructors:
Bohg, J. (PI)
;
Pavone, M. (PI)
;
Sadigh, D. (PI)
;
Agarwal, T. (TA)
;
Chen, C. (TA)
;
Manhas, A. (TA)
AA 275: Navigation for Autonomous Systems
Navigation is a key element in many autonomous systems, from self-driving cars to aerial taxis and space robots. In this course you will learn about sensors and algorithms that enable autonomous localization and navigation. Topics include GPS system design and absolute positioning; fault robustness and navigation integrity; odometry and simultaneous localization and mapping (SLAM) using filtering and graph optimization; vision and LiDAR-based navigation; and deep learning for perception and scene representation. Prerequisites:
AA 222,
EE 278 and
EE 263. Recommended:
AA 272,
EE 261,
AA 273,
AA228.
Terms: Win
| Units: 3
Instructors:
Gao, G. (PI)
;
Dai, A. (TA)
AA 279A: Space Mechanics
Orbits of near-earth satellites and interplanetary probes; relative motion in orbit; transfer and rendezvous; orbit determination; influence of earth's oblateness; sun and moon effects on earth satellites; decay of satellite orbits; invited lectures from industry. Prerequisite:
ENGR 15 and familiarity with MatLab.
Terms: Win
| Units: 3
AA 289: Robotics and Autonomous Systems Seminar (CS 529)
Seminar talks by researchers and industry professionals on topics related to modern robotics and autonomous systems. Broadly, talks will cover robotic design, perception and navigation, planning and control, and learning for complex robotic systems. May be repeated for credit.
Terms: Aut, Win, Spr
| Units: 1
| Repeatable
for credit
(up to 99 units total)
Instructors:
Cutkosky, M. (PI)
;
Pavone, M. (PI)
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