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Course Criteria
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3.00 Credits
Introduction to applied nonlinear control and estimation. Nonlinear stability theory, Lyapunov analysis, Barbalat's lemma. Feedback linearization, internal dynamics. Sliding surfaces. Adaptive nonlinear control. Contraction analysis, differential stability theory. Nonlinear observers. Stable adaptive control using multiresolution bases. Stability of nonlinear partial differential systems. Asynchronous distributed computation. Concurrent Synchronization. Emphasis on applications to physical systems (robots, aircraft, spacecraft, underwater vehicles, reaction-diffusion processes, machine vision, oscillators, internet). Term projects.
Prerequisite:
Prereq: 2.151, 6.241, 16.31, or permission of instructor
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3.00 Credits
Maneuvering motions of surface and underwater vehicles. Derivation of equations of motion, hydrodynamic coefficients. Memory effects. Linear and nonlinear forms of the equations of motion. Control surfaces modeling and design. Engine, propulsor, and transmission systems modeling and simulation during maneuvering. Stability of motion. Principles of multivariable automatic control. Optimal control, Kalman filtering, loop transfer recovery. Term project: applications chosen from autopilots for surface vehicles; towing in open seas; remotely operated vehicles.
Prerequisite:
Prereq: 2.22
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3.00 Credits
Provides a broad theoretical basis for system identification, estimation, and learning. Least squares estimation and its convergence properties, Kalman filter and extended Kalman filter, noise dynamics and system representation, function approximation theory, neural nets, radial basis functions, wavelets, Volterra expansions, informative data sets, persistent excitation, asymptotic variance, central limit theorems, model structure selection, system order estimate, maximum likelihood, unbiased estimates, Cramer-Rao lower bound, Kullback-Leibler information distance, Akaike's information criterion, experiment design, and model validation.
Prerequisite:
Prereq: 2.151
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3.00 Credits
Provides a solid theoretical foundation for the analysis and processing of experimental data, and real-time experimental control methods. Includes spectral analysis, filter design, system identification, simulation in continuous and discrete-time domains. Emphasis on practical problems with laboratory exercises.
Prerequisite:
Prereq: Knowledge of system dynamics
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3.00 Credits
Dynamic analysis, design, and control of robots. Forward and inverse kinematics and dynamics of multi-input, multi-output rigid body systems. Computed torque control. Adaptive control. System identification. Force feedback, adaptive visual servoing. Task planning, teleoperation. Elements of biological planning and control. Motor primitives, entrainment, locomotion, active sensing, binding models. Term projects.
Prerequisite:
Prereq: 2.151 or permission of instructor
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3.00 Credits
Theory and application of probabilistic techniques for autonomous mobile robotics. Topics include probabilistic state estimation and decision making for mobile robots; stochastic representations of the environment; dynamic models and sensor models for mobile robots; algorithms for mapping and localization; planning and control in the presence of uncertainty; cooperative operation of multiple mobile robots; mobile sensor networks; application to autonomous marine (underwater and floating), ground, and air vehicles.
Prerequisite:
Prereq: 6.041 or permission of instructor
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0.00 - 6.00 Credits
Direct experience in developing marine robotic systems, from conceptualization and design through manufacture and testing. The class consists of a weekly seminar with readings and discussions, and significant outside work on student projects, culminating in a written report each term. Seminar topics include tools for unmanned marine work and their history, analysis of mission requirements, conceptual design and modeling of systems, experiments and proofs of concept, and project pacing and time management. A total of up to 12 hours credit may be taken over one or two terms; seminar topics repeat yearly.
Prerequisite:
Prereq: None
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3.00 Credits
A comprehensive introduction to digital control system design, reinforced with hands-on laboratory experiences. Major topics include discrete-time system theory and analytical tools; design of digital control systems via approximation from continuous time; direct discrete-time design; loop-shaping design for performance and robustness; state-space design; observers and state-feedback; quantization and other nonlinear effects; implementation issues. Laboratory experiences and design projects connect theory with practice.
Prerequisite:
Prereq: 2.14, 2.151, or permission of instructor
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3.00 Credits
No course description available.
Prerequisite:
Prereq: 18.03, Biology (GIR), or permission of instructor
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3.00 Credits
Comprehensive introduction to dynamics and control of biomolecular systems with emphasis on design/analysis techniques from control theory. Provides a review of biology concepts, regulation mechanisms, and models. Covers basic enabling technologies, engineering principles for designing biological functions, modular design techniques, and design limitations. Students taking graduate version complete additional assignments.
Prerequisite:
Prereq: 18.03, Biology (GIR), or permission of instructor
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