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16.31 Feedback Control Systems (MIT) 16.31 Feedback Control Systems (MIT)

Description

This course covers the fundamentals of control design and analysis using state-space methods. This includes both the practical and theoretical aspects of the topic. By the end of the course, the student should be able to design controllers using state-space methods and evaluate whether these controllers are robust. This course covers the fundamentals of control design and analysis using state-space methods. This includes both the practical and theoretical aspects of the topic. By the end of the course, the student should be able to design controllers using state-space methods and evaluate whether these controllers are robust.Subjects

linear system response | linear system response | aircraft control | aircraft control | frequency response methods | frequency response methods | Nyquist stability theorem | Nyquist stability theorem | bode plots | bode plots | state-space systems | state-space systems | full-state feedback control | full-state feedback control | open-loop estimators | open-loop estimators | closed-loop estimators | closed-loop estimators | robustness analysis | robustness analysis | small gain theorem | small gain theoremLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata16.31 Feedback Control Systems (MIT) 16.31 Feedback Control Systems (MIT)

Description

The goal of this subject is to teach the fundamentals of control design and analysis using state-space methods. This includes both the practical and theoretical aspects of the topic. By the end of the course, students should be able to design controllers using state-space methods and evaluate whether these controllers are "robust," that is, if they are likely to work well in practice. The goal of this subject is to teach the fundamentals of control design and analysis using state-space methods. This includes both the practical and theoretical aspects of the topic. By the end of the course, students should be able to design controllers using state-space methods and evaluate whether these controllers are "robust," that is, if they are likely to work well in practice.Subjects

feedback control | feedback control | feedback control system | feedback control system | state-space | state-space | controllability | controllability | observability | observability | transfer functions | transfer functions | canonical forms | canonical forms | controllers | controllers | pole-placement | pole-placement | optimal control | optimal control | Kalman filter | Kalman filterLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This course deals with modeling multi-domain engineering systems at a level of detail suitable for design and control system implementation. Topics covered include network representation, state-space models; multi-port energy storage and dissipation, Legendre transforms, nonlinear mechanics, transformation theory, Lagrangian and Hamiltonian forms and control-relevant properties. Application examples may include electro-mechanical transducers, mechanisms, electronics, fluid and thermal systems, compressible flow, chemical processes, diffusion, and wave transmission. This course deals with modeling multi-domain engineering systems at a level of detail suitable for design and control system implementation. Topics covered include network representation, state-space models; multi-port energy storage and dissipation, Legendre transforms, nonlinear mechanics, transformation theory, Lagrangian and Hamiltonian forms and control-relevant properties. Application examples may include electro-mechanical transducers, mechanisms, electronics, fluid and thermal systems, compressible flow, chemical processes, diffusion, and wave transmission.Subjects

Modeling multi-domain engineering systems | Modeling multi-domain engineering systems | design and control system implementation | design and control system implementation | Network representation | Network representation | state-space models | state-space models | dissipation | dissipation | Legendre transforms | Legendre transforms | Nonlinear mechanics | Nonlinear mechanics | transformation theory | transformation theory | Hamiltonian forms | Hamiltonian forms | Control-relevant properties | Control-relevant properties | electro-mechanical transducers | electro-mechanical transducers | mechanisms | mechanisms | electronics | electronics | thermal systems | thermal systems | compressible flow | compressible flow | chemical processes | chemical processes | diffusion | diffusion | wave transmission | wave transmissionLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This course examines signals, systems and inference as unifying themes in communication, control and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time; group delay; state feedback and observers; probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization; least-mean square error estimation; Wiener filtering; hypothesis testing; detection; matched filters. This course examines signals, systems and inference as unifying themes in communication, control and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time; group delay; state feedback and observers; probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization; least-mean square error estimation; Wiener filtering; hypothesis testing; detection; matched filters.Subjects

signals and systems | signals and systems | transform representation | transform representation | state-space models | state-space models | state observers | state observers | state feedback | state feedback | probabilistic models | probabilistic models | random processes | random processes | power spectral density | power spectral density | hypothesis testing | hypothesis testing | signal detection | signal detectionLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This course is taken mainly by undergraduates, and explores ideas involving signals, systems and probabilistic models in the context of communication, control and signal processing applications. The material expands out from the basics in 6.003 and 6.041. The treatment involves aspects of analysis, synthesis, and optimization. Topics covered differ somewhat from semester to semester, but typically include: random processes, correlations, spectral densities, state-space modeling, multirate processing, signal estimation and detection. This course is taken mainly by undergraduates, and explores ideas involving signals, systems and probabilistic models in the context of communication, control and signal processing applications. The material expands out from the basics in 6.003 and 6.041. The treatment involves aspects of analysis, synthesis, and optimization. Topics covered differ somewhat from semester to semester, but typically include: random processes, correlations, spectral densities, state-space modeling, multirate processing, signal estimation and detection.Subjects

Input-output | Input-output | state-space models | state-space models | linear systems | linear systems | deterministic and random signals | deterministic and random signals | time- and transform-domain representations | time- and transform-domain representations | sampling | sampling | discrete-time processing | discrete-time processing | continuous-time signals | continuous-time signals | state feedback | state feedback | observers | observers | probabilistic models | probabilistic models | stochastic processes | stochastic processes | correlation functions | correlation functions | power spectra | power spectra | whitening filters | whitening filters | Detection | Detection | matched filters | matched filters | Least-mean square error estimation | Least-mean square error estimation | Wiener filtering | Wiener filteringLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.435 System Identification (MIT) 6.435 System Identification (MIT)

Description

This course is offered to graduates and includes topics such as mathematical models of systems from observations of their behavior; time series, state-space, and input-output models; model structures, parametrization, and identifiability; non-parametric methods; prediction error methods for parameter estimation, convergence, consistency, and asymptotic distribution; relations to maximum likelihood estimation; recursive estimation; relation to Kalman filters; structure determination; order estimation; Akaike criterion; bounded but unknown noise model; and robustness and practical issues. This course is offered to graduates and includes topics such as mathematical models of systems from observations of their behavior; time series, state-space, and input-output models; model structures, parametrization, and identifiability; non-parametric methods; prediction error methods for parameter estimation, convergence, consistency, and asymptotic distribution; relations to maximum likelihood estimation; recursive estimation; relation to Kalman filters; structure determination; order estimation; Akaike criterion; bounded but unknown noise model; and robustness and practical issues.Subjects

mathematical models | mathematical models | time series | time series | state-space | state-space | input-output models | input-output models | model structures | model structures | parametrization | parametrization | identifiability | identifiability | non-parametric methods | non-parametric methods | prediction error | prediction error | parameter estimation | parameter estimation | convergence | convergence | consistency | consistency | andasymptotic distribution | andasymptotic distribution | maximum likelihood estimation | maximum likelihood estimation | recursive estimation | recursive estimation | Kalman filters | Kalman filters | structure determination | structure determination | order estimation | order estimation | Akaike criterion | Akaike criterion | bounded noise models | bounded noise models | robustness | robustnessLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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6.241 examines linear, discrete- and continuous-time, and multi-input-output systems in control and related areas. Least squares and matrix perturbation problems are considered. Topics covered include: state-space models, modes, stability, controllability, observability, transfer function matrices, poles and zeros, minimality, internal stability of interconnected systems, feedback compensators, state feedback, optimal regulation, observers, observer-based compensators, measures of control performance, and robustness issues using singular values of transfer functions. Nonlinear systems are also introduced. 6.241 examines linear, discrete- and continuous-time, and multi-input-output systems in control and related areas. Least squares and matrix perturbation problems are considered. Topics covered include: state-space models, modes, stability, controllability, observability, transfer function matrices, poles and zeros, minimality, internal stability of interconnected systems, feedback compensators, state feedback, optimal regulation, observers, observer-based compensators, measures of control performance, and robustness issues using singular values of transfer functions. Nonlinear systems are also introduced.Subjects

control | control | linear | linear | discrete | discrete | continuous-time | continuous-time | multi-input-output | multi-input-output | least squares | least squares | matrix perturbation | matrix perturbation | state-space models | stability | controllability | observability | transfer function matrices | poles | state-space models | stability | controllability | observability | transfer function matrices | poles | zeros | zeros | minimality | minimality | feedback | feedback | compensators | compensators | state feedback | state feedback | optimal regulation | optimal regulation | observers | transfer functions | observers | transfer functions | nonlinear systems | nonlinear systems | linear systems | linear systemsLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.243J Dynamics of Nonlinear Systems (MIT) 6.243J Dynamics of Nonlinear Systems (MIT)

Description

This course provides an introduction to nonlinear deterministic dynamical systems. Topics covered include: nonlinear ordinary differential equations; planar autonomous systems; fundamental theory: Picard iteration, contraction mapping theorem, and Bellman-Gronwall lemma; stability of equilibria by Lyapunov's first and second methods; feedback linearization; and application to nonlinear circuits and control systems. This course provides an introduction to nonlinear deterministic dynamical systems. Topics covered include: nonlinear ordinary differential equations; planar autonomous systems; fundamental theory: Picard iteration, contraction mapping theorem, and Bellman-Gronwall lemma; stability of equilibria by Lyapunov's first and second methods; feedback linearization; and application to nonlinear circuits and control systems.Subjects

nonlinear systems | nonlinear systems | deterministic dynamical systems | deterministic dynamical systems | ordinary differential equations | ordinary differential equations | planar autonomous systems | planar autonomous systems | Picard iteration | Picard iteration | contraction mapping theorem | contraction mapping theorem | Bellman-Gronwall lemma | Bellman-Gronwall lemma | Lyapunov methods | Lyapunov methods | feedback linearization | feedback linearization | nonlinear circuits | nonlinear circuits | control systems | control systems | local controllability | local controllability | volume evolution | volume evolution | system analysis | system analysis | singular perturbations | singular perturbations | averaging | averaging | local behavior | local behavior | trajectories | trajectories | equilibria | equilibria | storage functions | storage functions | stability analysis | stability analysis | continuity | continuity | differential equations | differential equations | system models | system models | parameters | parameters | input/output | input/output | state-space | state-space | 16.337 | 16.337License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This course is a comprehensive introduction to control system synthesis in which the digital computer plays a major role, reinforced with hands-on laboratory experience. The course covers elements of real-time computer architecture; input-output interfaces and data converters; analysis and synthesis of sampled-data control systems using classical and modern (state-space) methods; analysis of trade-offs in control algorithms for computation speed and quantization effects. Laboratory projects emphasize practical digital servo interfacing and implementation problems with timing, noise, and nonlinear devices. This course is a comprehensive introduction to control system synthesis in which the digital computer plays a major role, reinforced with hands-on laboratory experience. The course covers elements of real-time computer architecture; input-output interfaces and data converters; analysis and synthesis of sampled-data control systems using classical and modern (state-space) methods; analysis of trade-offs in control algorithms for computation speed and quantization effects. Laboratory projects emphasize practical digital servo interfacing and implementation problems with timing, noise, and nonlinear devices.Subjects

digital computer | digital computer | computation | computation | real-time computer | real-time computer | input-output | input-output | I/O | I/O | interface | interface | data converter | data converter | A/D converter | A/D converter | sampling | sampling | state-space | state-space | algorithm | algorithm | quantization | quantization | servo | servo | timing | timing | noise | noise | nonlinear | nonlinear | nonlinearity | nonlinearity | non-linear | non-linearLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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This course models multi-domain engineering systems at a level of detail suitable for design and control system implementation. Topics include network representation, state-space models; multi-port energy storage and dissipation, Legendre transforms; nonlinear mechanics, transformation theory, Lagrangian and Hamiltonian forms; and control-relevant properties. Application examples may include electro-mechanical transducers, mechanisms, electronics, fluid and thermal systems, compressible flow, chemical processes, diffusion, and wave transmission. This course models multi-domain engineering systems at a level of detail suitable for design and control system implementation. Topics include network representation, state-space models; multi-port energy storage and dissipation, Legendre transforms; nonlinear mechanics, transformation theory, Lagrangian and Hamiltonian forms; and control-relevant properties. Application examples may include electro-mechanical transducers, mechanisms, electronics, fluid and thermal systems, compressible flow, chemical processes, diffusion, and wave transmission.Subjects

Modeling multi-domain engineering systems | Modeling multi-domain engineering systems | design and control system implementation | design and control system implementation | Network representation | Network representation | state-space models | state-space models | dissipation | dissipation | Legendre transforms | Legendre transforms | Nonlinear mechanics | Nonlinear mechanics | transformation theory | transformation theory | Hamiltonian forms | Hamiltonian forms | Control-relevant properties | Control-relevant properties | electro-mechanical transducers | electro-mechanical transducers | mechanisms | mechanisms | electronics | electronics | thermal systems | thermal systems | compressible flow | compressible flow | chemical processes | chemical processes | diffusion | diffusion | wave transmission | wave transmissionLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata16.30 Feedback Control Systems (MIT) 16.30 Feedback Control Systems (MIT)

Description

This course will teach fundamentals of control design and analysis using state-space methods. This includes both the practical and theoretical aspects of the topic. By the end of the course, you should be able to design controllers using state-space methods and evaluate whether these controllers are robust to some types of modeling errors and nonlinearities. You will learn to: Design controllers using state-space methods and analyze using classical tools. Understand impact of implementation issues (nonlinearity, delay). Indicate the robustness of your control design. Linearize a nonlinear system, and analyze stability. This course will teach fundamentals of control design and analysis using state-space methods. This includes both the practical and theoretical aspects of the topic. By the end of the course, you should be able to design controllers using state-space methods and evaluate whether these controllers are robust to some types of modeling errors and nonlinearities. You will learn to: Design controllers using state-space methods and analyze using classical tools. Understand impact of implementation issues (nonlinearity, delay). Indicate the robustness of your control design. Linearize a nonlinear system, and analyze stability.Subjects

control design | control design | control analysis | control analysis | state-space methods | state-space methods | linear systems | linear systems | estimation filters | estimation filters | dynamic output feedback | dynamic output feedback | full state feedback | full state feedback | state estimation | state estimation | output feedback | output feedback | nonlinear analysis | nonlinear analysis | model uncertainty | model uncertainty | robustness | robustnessLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata2.171 Analysis and Design of Digital Control Systems (MIT)

Description

This course is a comprehensive introduction to control system synthesis in which the digital computer plays a major role, reinforced with hands-on laboratory experience. The course covers elements of real-time computer architecture; input-output interfaces and data converters; analysis and synthesis of sampled-data control systems using classical and modern (state-space) methods; analysis of trade-offs in control algorithms for computation speed and quantization effects. Laboratory projects emphasize practical digital servo interfacing and implementation problems with timing, noise, and nonlinear devices.Subjects

digital computer | computation | real-time computer | input-output | I/O | interface | data converter | A/D converter | sampling | state-space | algorithm | quantization | servo | timing | noise | nonlinear | nonlinearity | non-linearLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata2.141 Modeling and Simulation of Dynamic Systems (MIT)

Description

This course models multi-domain engineering systems at a level of detail suitable for design and control system implementation. Topics include network representation, state-space models; multi-port energy storage and dissipation, Legendre transforms; nonlinear mechanics, transformation theory, Lagrangian and Hamiltonian forms; and control-relevant properties. Application examples may include electro-mechanical transducers, mechanisms, electronics, fluid and thermal systems, compressible flow, chemical processes, diffusion, and wave transmission.Subjects

Modeling multi-domain engineering systems | design and control system implementation | Network representation | state-space models | dissipation | Legendre transforms | Nonlinear mechanics | transformation theory | Hamiltonian forms | Control-relevant properties | electro-mechanical transducers | mechanisms | electronics | thermal systems | compressible flow | chemical processes | diffusion | wave transmissionLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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6.241 examines linear, discrete- and continuous-time, and multi-input-output systems in control and related areas. Least squares and matrix perturbation problems are considered. Topics covered include: state-space models, modes, stability, controllability, observability, transfer function matrices, poles and zeros, minimality, internal stability of interconnected systems, feedback compensators, state feedback, optimal regulation, observers, observer-based compensators, measures of control performance, and robustness issues using singular values of transfer functions. Nonlinear systems are also introduced.Subjects

control | linear | discrete | continuous-time | multi-input-output | least squares | matrix perturbation | state-space models | stability | controllability | observability | transfer function matrices | poles | zeros | minimality | feedback | compensators | state feedback | optimal regulation | observers | transfer functions | nonlinear systems | linear systemsLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.243J Dynamics of Nonlinear Systems (MIT)

Description

This course provides an introduction to nonlinear deterministic dynamical systems. Topics covered include: nonlinear ordinary differential equations; planar autonomous systems; fundamental theory: Picard iteration, contraction mapping theorem, and Bellman-Gronwall lemma; stability of equilibria by Lyapunov's first and second methods; feedback linearization; and application to nonlinear circuits and control systems.Subjects

nonlinear systems | deterministic dynamical systems | ordinary differential equations | planar autonomous systems | Picard iteration | contraction mapping theorem | Bellman-Gronwall lemma | Lyapunov methods | feedback linearization | nonlinear circuits | control systems | local controllability | volume evolution | system analysis | singular perturbations | averaging | local behavior | trajectories | equilibria | storage functions | stability analysis | continuity | differential equations | system models | parameters | input/output | state-space | 16.337License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata16.31 Feedback Control Systems (MIT)

Description

This course covers the fundamentals of control design and analysis using state-space methods. This includes both the practical and theoretical aspects of the topic. By the end of the course, the student should be able to design controllers using state-space methods and evaluate whether these controllers are robust.Subjects

linear system response | aircraft control | frequency response methods | Nyquist stability theorem | bode plots | state-space systems | full-state feedback control | open-loop estimators | closed-loop estimators | robustness analysis | small gain theoremLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata16.31 Feedback Control Systems (MIT)

Description

The goal of this subject is to teach the fundamentals of control design and analysis using state-space methods. This includes both the practical and theoretical aspects of the topic. By the end of the course, students should be able to design controllers using state-space methods and evaluate whether these controllers are "robust," that is, if they are likely to work well in practice.Subjects

feedback control | feedback control system | state-space | controllability | observability | transfer functions | canonical forms | controllers | pole-placement | optimal control | Kalman filterLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.435 System Identification (MIT)

Description

This course is offered to graduates and includes topics such as mathematical models of systems from observations of their behavior; time series, state-space, and input-output models; model structures, parametrization, and identifiability; non-parametric methods; prediction error methods for parameter estimation, convergence, consistency, and asymptotic distribution; relations to maximum likelihood estimation; recursive estimation; relation to Kalman filters; structure determination; order estimation; Akaike criterion; bounded but unknown noise model; and robustness and practical issues.Subjects

mathematical models | time series | state-space | input-output models | model structures | parametrization | identifiability | non-parametric methods | prediction error | parameter estimation | convergence | consistency | andasymptotic distribution | maximum likelihood estimation | recursive estimation | Kalman filters | structure determination | order estimation | Akaike criterion | bounded noise models | robustnessLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.243J Dynamics of Nonlinear Systems (MIT)

Description

This course provides an introduction to nonlinear deterministic dynamical systems. Topics covered include: nonlinear ordinary differential equations; planar autonomous systems; fundamental theory: Picard iteration, contraction mapping theorem, and Bellman-Gronwall lemma; stability of equilibria by Lyapunov's first and second methods; feedback linearization; and application to nonlinear circuits and control systems.Subjects

nonlinear systems | deterministic dynamical systems | ordinary differential equations | planar autonomous systems | Picard iteration | contraction mapping theorem | Bellman-Gronwall lemma | Lyapunov methods | feedback linearization | nonlinear circuits | control systems | local controllability | volume evolution | system analysis | singular perturbations | averaging | local behavior | trajectories | equilibria | storage functions | stability analysis | continuity | differential equations | system models | parameters | input/output | state-space | 16.337License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.011 Introduction to Communication, Control, and Signal Processing (MIT)

Description

This course is taken mainly by undergraduates, and explores ideas involving signals, systems and probabilistic models in the context of communication, control and signal processing applications. The material expands out from the basics in 6.003 and 6.041. The treatment involves aspects of analysis, synthesis, and optimization. Topics covered differ somewhat from semester to semester, but typically include: random processes, correlations, spectral densities, state-space modeling, multirate processing, signal estimation and detection.Subjects

Input-output | state-space models | linear systems | deterministic and random signals | time- and transform-domain representations | sampling | discrete-time processing | continuous-time signals | state feedback | observers | probabilistic models | stochastic processes | correlation functions | power spectra | whitening filters | Detection | matched filters | Least-mean square error estimation | Wiener filteringLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata2.141 Modeling and Simulation of Dynamic Systems (MIT)

Description

This course deals with modeling multi-domain engineering systems at a level of detail suitable for design and control system implementation. Topics covered include network representation, state-space models; multi-port energy storage and dissipation, Legendre transforms, nonlinear mechanics, transformation theory, Lagrangian and Hamiltonian forms and control-relevant properties. Application examples may include electro-mechanical transducers, mechanisms, electronics, fluid and thermal systems, compressible flow, chemical processes, diffusion, and wave transmission.Subjects

Modeling multi-domain engineering systems | design and control system implementation | Network representation | state-space models | dissipation | Legendre transforms | Nonlinear mechanics | transformation theory | Hamiltonian forms | Control-relevant properties | electro-mechanical transducers | mechanisms | electronics | thermal systems | compressible flow | chemical processes | diffusion | wave transmissionLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata16.30 Feedback Control Systems (MIT)

Description

This course will teach fundamentals of control design and analysis using state-space methods. This includes both the practical and theoretical aspects of the topic. By the end of the course, you should be able to design controllers using state-space methods and evaluate whether these controllers are robust to some types of modeling errors and nonlinearities. You will learn to: Design controllers using state-space methods and analyze using classical tools. Understand impact of implementation issues (nonlinearity, delay). Indicate the robustness of your control design. Linearize a nonlinear system, and analyze stability.Subjects

control design | control analysis | state-space methods | linear systems | estimation filters | dynamic output feedback | full state feedback | state estimation | output feedback | nonlinear analysis | model uncertainty | robustnessLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.011 Introduction to Communication, Control, and Signal Processing (MIT)

Description

This course is taken mainly by undergraduates, and explores ideas involving signals, systems and probabilistic models in the context of communication, control and signal processing applications. The material expands out from the basics in 6.003 and 6.041. The treatment involves aspects of analysis, synthesis, and optimization. Topics covered differ somewhat from semester to semester, but typically include: random processes, correlations, spectral densities, state-space modeling, multirate processing, signal estimation and detection.Subjects

Input-output | state-space models | linear systems | deterministic and random signals | time- and transform-domain representations | sampling | discrete-time processing | continuous-time signals | state feedback | observers | probabilistic models | stochastic processes | correlation functions | power spectra | whitening filters | Detection | matched filters | Least-mean square error estimation | Wiener filteringLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.011 Introduction to Communication, Control, and Signal Processing (MIT)

Description

This course examines signals, systems and inference as unifying themes in communication, control and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time; group delay; state feedback and observers; probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization; least-mean square error estimation; Wiener filtering; hypothesis testing; detection; matched filters.Subjects

signals and systems | transform representation | state-space models | state observers | state feedback | probabilistic models | random processes | power spectral density | hypothesis testing | signal detectionLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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