Universal laws and architectures: Theory and lessons from grids, nets, brains, bugs, planes, docs, fire, bodies, fashion,
|
|
- Loren Simon
- 6 years ago
- Views:
Transcription
1 Universal laws and architectures: Theory and lessons from grids, nets, brains, bugs, planes, docs, fire, bodies, fashion, earthquakes, turbulence, music, buildings, cities, art, running, throwing, Synesthesia, spacecraft, statistical mechanics John Doyle 道陽 Jean-Lou Chameau Professor Control and Dynamical Systems, EE, & BioE Ca # 1 tech
2 Efficiency/instability/layers/feedback All create new efficiencies but also instabilities Needs new distributed/layered/complex/active control Sustainable infrastructure? (e.g. smartgrids) Money/finance/lobbyists/etc Industrialization Society/agriculture/weapons/etc Bipedalism Maternal care Warm blood Major transitions Flight Mitochondria Oxygen Translation (ribosomes) Glycolysis (2011 Science)
3 Compute Comms for Comp/Cntrl/Bio Info Thry Optimization Statistics Control, OR Orthophysics (Eng/Bio/Math) Physics
4 slow fragile efficient fast robust flexible inflexible waste
5 Compute Research progress Slow Fast Insight Decidable Pspace Flexible NP P analytic Inflexible From toys to real systems Control, OR
6 Compute Research progress Slow Fast Insight Decidable Pspace Flexible NP P analytic Inflexible Improved algorithms Control, OR
7 Control of cyberphysical systems? Physical: Efficient, therefore less stable Computing: Distributed with delays Communication: With latency Therefore Control: Distributed with sparse actuation (but add sensing) with delays in computing and communications but free memory/bandwidth (for now) Fundamental tradeoffs? Scalability?
8 Control of cyberphysical systems? Physical: Efficient, therefore less stable Computing: Distributed with delays Communication: With latency Therefore Control: Distributed with sparse actuation and sensing with delays in computing and communications but free memory/bandwidth (for now) Fundamental tradeoffs? Scalability?
9 Localized (distributed) control Localizable control: Wang, Matni, You and Doyle ACC 14 Localized LQR control: Wang, Matni, and Doyle CDC 14
10 Another extremely toy model Concretely illustrate important new ideas Minimal complexity otherwise Familiar, intuitive circuit dynamics Instability mechanism is artificial but challenging
11 LC circuit Each node = grounded capacitance Each link = inductance
12 System Model Assuming each L and each C has unit value, the dynamics of the system are where x(t) is states of node voltage and link current, M is the incidence matrix of the circuit graph. (Will reorder for plotting later.)
13 Discrete Time System Model A first order (Euler) approximation is With step = 0.2, the maximum eigenvalue of Ad is Artificially create a very unstable system
14 Simplified diagram (2 states per node) Actuated and sensed Only sensed Only sensed Actuated and sensed Only sensed
15 Simplified diagram (2 states per node) Actuated and sensed Only sensed Only sensed Actuated and sensed Only sensed
16 Actuated and sensed Only sensed Simplified diagram (2 states per node) Actuated and sensed Only sensed
17 Nominally each has delay 1. Expensive? 0. Physical 1. Actuation Simplified diagram (2 states per node) Actuated and sensed Only sensed
18 Controller Physical plant
19 Sense, comm/comp, act. Expensive? 0. Physical 1. Actuation 2. Comms speed 3. Comp speed 4. Sensing Actuated and sensed Only sensed
20 Controller Physical plant
21 Controller plane Data plane SDN/ODP
22 Controller plane Cyber Data plane Physical
23 Sense, comm/comp, act. Nominally each has delay 1. Actuated and sensed Only sensed
24 Expensive: physical plant passive stability actuation low delay (comms and comp) Cheap: comms bandwidth compute memory sensing True for cells, nets, grids, brains, but not in general Actuated and sensed Only sensed
25 System Model The discrete time system equation is Example: 30 C, 29 L
26 Simplified diagram Open loop dynamics
27 Open loop xt () t xt ()
28 Open loop xt () t
29 Threshold to x(t) < 1 xt () 1 0 v () t t
30 xt () 1 0 Open loop v () t 2 v () t 9 v () t 15 Disturbance propagation Threshold to x(t) < 1 t
31 xt () 1 0 Open loop v () t 2 v () t 9 v () t v () t 22 log xt ( ) v () t 29 t
32 log xt ( ) xt () Spacetime cone log xt ( ) Open loop plot log xt ( ) t
33 log xt ( ) log xt ( ) xt () 1 0 v () t 15 Threshold to x(t) < Open loop plot log xt ( ) t
34 log xt ( ) xt () Spacetime cone log xt ( ) Open loop log xt ( ) t
35 t xt () Space-time state cone State
36 Controller Design Critical Issues 1. Transient LQ (H2) cost: (x x+u u) 2. Actuator Density 3. Communication (vs plant) Speed 4. Locality/Scalability (Computation) 5. Time/space horizon
37 Actuator Density Standard (centralized) optimal H2 control No delay (initially) Defer other issues ( comm, comp, sense) Objective: min sum (x x+u u) Actuator density = # actuators / # states Trade-off: actuator density vs norm Example: 30 C, 29 L
38 Norm - Actuator Density (normalized) Opt H2 norm Artificially unstable system 1 3 Normalized by undelayed centralized.2.5 Actuator Density 1
39 Standard control (circa 1970) norm Comm speed = 0 delay Actuator Density 1 Actuated and sensed Only sensed
40 Opt undelay central state Optimal Controller + Norm H2 optimal Communication undelayed Design/model global/huge P Implementation local/huge P Sparse actuation Opt undelay central ctrl
41 xt () log xt ( ) log ut ( ) Color code? ut () t
42 Expensive? 0. Physical 1. Actuation 2. Comms speed 3. Comp speed 4. Sensing Actuated and sensed Only sensed
43 Nominally delay 1. Communication speed Expensive? 0. Physical 1. Actuation 2. Comms speed 3. Comp speed 4. Sensing Versus plant speed
44 undelay central ctrl undelay central state Communication speed =
45 undelay central ctrl undelay central state Communication speed =
46 Norm Distributed Localized Undelayed central Normalized by undelayed centralized Communication Speed
47 undelay central ctrl undelay central state delay distr ctrl delay distr state
48 undelay central ctrl central stat delay distr state delay distr ctrl
49 Distributed (QI) Controller + Norm (H2) small + Optimal for constraints + Communication delayed Design/model global/huge P Implementation local/huge P delay distr ctrl delay distr state
50 delay local ctrl delay local state delay distr ctrl delay distr state
51 delay distr state delay local delay distr ctrl
52 delay local ctrl delay local state Localized Controller + Norm (H2) small + Optimal for constraints + Communication delayed + Design/model local/small + Implementation local/small + State local Everything is scalable.
53 Norm Distributed Localized Undelayed central Normalized by undelayed centralized Communication Speed
54 Linear equations delay local ctrl delay local state xt [ ] 0 x X u U
55 t xt () Space-time state cone State
56 t xt () Communication and Control Cone Space-time state cone State finite impulse response (FIR)
57 Control t xt () xt [ ] 0 x X u U Space-time state cone finite impulse response (FIR) Local space-time controllability
58 Communications t xt () Past Past delayed state needed to compute control
59 Control t xt () Past Local space-time controllability xt [ ] 0 x X u U Space-time state cone finite impulse response (FIR) This can linearly constrain any optimization
60 State Optimal undelayed centralized state (old) State Optimal delayed distributed (newish) (but not scalable) Optimal delayed localized (very new, scalable)
61 AWGN in C2, L26, C29 undelay central ctrl undelay central state delay local ctrl delay local state
62 xt () Local space-time controllability Control Localized Controller t + Norm (H2) small + Past Optimal for constraints + Communication is delayed + Design/model local/small + Implementation local/small + State local xt [ ] 0 x X u U Space-time state cone finite impulse response (FIR) This can linearly constrain any optimization
63 Localized Controller + Norm (H2) small + Optimal for constraints + Design/model is local + Implementation is local + State stays local - Bandwidth is? Output feedback? Mostly good? Approximately local? news, but? Layering? incomplete? Nonlinear, MPC, etc?? Comms codesign? See also Javad s new relaxations
64 Extensions Scalable optimal control Localizable control: Y.-S. Wang, N. Matni, S. You and J. C. Doyle ACC 14 Localized LQR control: Y.-S. Wang, N. Matni, and J. C. Doyle CDC 14 Output feedback progress Dealing with varying-delays (jitter) Two player LQR with varying delays: N. Matni and J. C. Doyle CDC 13, N. Matni, A. Lamperski and J C. Doyle IFAC 14
65 More Nikolai Matni Distributed/scalable system identification Low-rank + Low-order decompositions: N. Matni and A. Rantzer, CDC 14 Structured Robustness Distributed Controllers Satisfying an H norm bound: N. Matni CDC 14 Regularization for Design Topology/interconnection design: N. Matni, CDC 13 (best student paper), TCNS 14 More broadly (including actuator/sensor placement): N. Matni and V. Chandrasekaran, CDC 14
66 More Extensions/Apps Apps: neuro, smartgrid, CPS, cells IMC/RHC, etc (all of centralized control theory) Cyber theory: Delay jitter (uncertainty) Cyber: Comms co-design (CDC student prize paper) Physical: Robustness (unmodeled dynamics, noise) Cyber-phys: System ID, ML, adaptive SDN (Software defined nets, OpenDaylight) Revisit layering as optimization? Poset causality (streamlining)? Quantization and network coding?
67 Revisit layering as optimization decomposition Chiang, Low, Calderbank, Doyle, 2007 Controller Physical plant
68 Controller plane Data plane SDN/ODP
69 Layered Architectures Controller plane Cyber Data plane Physical
70 Conjecture: Norm bad before method breaks Norm norm Centralized 1 3 Actuator Density Distributed Localized Undelayed central Tradeoffs Communication Speed
71 1.15 norm Delayed Centralized Decentralized Localized Error Local control theory Communication Speed 0 Speed (=1/delay)
72 Local control theory 1.15 norm Delayed Centralized Decentralized Localized Communication Speed Error 0 Speed (=1/delay)
73 Info theory Local control theory Error 0 Rate (BW) Error 0 Speed (=1/delay) Due to quantization, loss, noise Communications
74 Info theory Local control theory Error Error Speed (=1/delay) Rate (BW) 0 0 Error Communications
75 Local control theory Info theory Error Speed (=1/delay) Rate (BW) 0 0 Error Communications
76 Local control theory Info theory Error Speed Rate 0 0 Error Communications
77 Local control theory Error Info theory Error Error Rate 0 Speed Communications
78 Local control theory Error Info theory Error Error Rate ideal 0 Speed Communications
79 Error Error Error Rate ideal 0 Speed Local control
80 Error Error Error Resource 1 ideal 0 Resource 2 Tradeoffs
81 Control over limited channels (Martins et al) a) e=d-u - decode/ actuate d delay source/ disturbance 1 f d f d 0 Plant (P) decode/ control/ encode C A Channels C S sense/ encode b) c) S p 0 P p j E j D j d) e) log S d p C min 0,log f) Pz 0 S S d p C A z 1 z p p ln S j d ln if p z 2 2 z 2 z p z
82 Universal laws and architectures (Turing) Special Architecture (constraints that deconstrain) General ideal Fast Speed Slow
83 Memory is cheap, reusable, powerful. Time is not. Special Memory General Fast Speed Slow
84 Error Error Error Resource 1 ideal 0 Tradeoffs Resource 2
85 Cheap: memory, bandwidth, sensors Not : time (1/speed), actuators Brains/bodies, cells, CyberPhySys, Error Error Actuation ideal 0 Speed (comms, comp) Critical Tradeoffs
86 All costs are ultimately physical. Cost Actuation ideal 0 Speed (comms, comp) Critical Tradeoffs
Structured State Space Realizations for SLS Distributed Controllers
Structured State Space Realizations for SLS Distributed Controllers James Anderson and Nikolai Matni Abstract In recent work the system level synthesis (SLS) paradigm has been shown to provide a truly
More informationhttps://www.cds.caltech.edu/~doyle John Doyle 道陽 Jean-Lou Chameau Professor Control and Dynamical Systems, EE, & BioE
Universal las and architectures: Theory and lessons from brains, hearts, cells, grids, nets, bugs, fluids, bodies, planes, docs, fire, fashion, earthquakes, music, buildings, cities, art, running, cycling,
More informationCDS 270-2: Lecture 6-1 Towards a Packet-based Control Theory
Goals: CDS 270-2: Lecture 6-1 Towards a Packet-based Control Theory Ling Shi May 1 2006 - Describe main issues with a packet-based control system - Introduce common models for a packet-based control system
More informationK 1 K 2. System Level Synthesis: A Tutorial. John C. Doyle, Nikolai Matni, Yuh-Shyang Wang, James Anderson, and Steven Low
System Level Synthesis: A Tutorial John C. Doyle, Nikolai Matni, Yuh-Shyang Wang, James Anderson, and Steven Low Abstract This tutorial paper provides an overview of the System Level Approach to control
More informationControl Systems I. Lecture 2: Modeling. Suggested Readings: Åström & Murray Ch. 2-3, Guzzella Ch Emilio Frazzoli
Control Systems I Lecture 2: Modeling Suggested Readings: Åström & Murray Ch. 2-3, Guzzella Ch. 2-3 Emilio Frazzoli Institute for Dynamic Systems and Control D-MAVT ETH Zürich September 29, 2017 E. Frazzoli
More informationCommunication constraints and latency in Networked Control Systems
Communication constraints and latency in Networked Control Systems João P. Hespanha Center for Control Engineering and Computation University of California Santa Barbara In collaboration with Antonio Ortega
More informationFAULT-TOLERANT CONTROL OF CHEMICAL PROCESS SYSTEMS USING COMMUNICATION NETWORKS. Nael H. El-Farra, Adiwinata Gani & Panagiotis D.
FAULT-TOLERANT CONTROL OF CHEMICAL PROCESS SYSTEMS USING COMMUNICATION NETWORKS Nael H. El-Farra, Adiwinata Gani & Panagiotis D. Christofides Department of Chemical Engineering University of California,
More informationHere represents the impulse (or delta) function. is an diagonal matrix of intensities, and is an diagonal matrix of intensities.
19 KALMAN FILTER 19.1 Introduction In the previous section, we derived the linear quadratic regulator as an optimal solution for the fullstate feedback control problem. The inherent assumption was that
More informationQUANTIZED SYSTEMS AND CONTROL. Daniel Liberzon. DISC HS, June Dept. of Electrical & Computer Eng., Univ. of Illinois at Urbana-Champaign
QUANTIZED SYSTEMS AND CONTROL Daniel Liberzon Coordinated Science Laboratory and Dept. of Electrical & Computer Eng., Univ. of Illinois at Urbana-Champaign DISC HS, June 2003 HYBRID CONTROL Plant: u y
More informationQSR-Dissipativity and Passivity Analysis of Event-Triggered Networked Control Cyber-Physical Systems
QSR-Dissipativity and Passivity Analysis of Event-Triggered Networked Control Cyber-Physical Systems arxiv:1607.00553v1 [math.oc] 2 Jul 2016 Technical Report of the ISIS Group at the University of Notre
More informationControl System Design
ELEC ENG 4CL4: Control System Design Notes for Lecture #36 Dr. Ian C. Bruce Room: CRL-229 Phone ext.: 26984 Email: ibruce@mail.ece.mcmaster.ca Friday, April 4, 2003 3. Cascade Control Next we turn to an
More informationLARGE-SCALE networked systems have emerged in extremely
SUBMITTED TO IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. XX, NO. XX, JANUARY 2017 1 Separable and Localized System Level Synthesis for Large-Scale Systems Yuh-Shyang Wang, Member, IEEE, Nikolai Matni,
More informationHard Limits on Robust Control over Delayed and Quantized Communication Channels with Applications to Sensorimotor Control
Hard Limits on Robust Control over Delayed and Quantized Communication Channels with Applications to Sensorimotor Control Yorie Nakahira( 依恵 ), Nikolai Matni( 日光来 ), John C. Doyle( 道耀 ) Abstract The modern
More informationEL2520 Control Theory and Practice
EL2520 Control Theory and Practice Lecture 8: Linear quadratic control Mikael Johansson School of Electrical Engineering KTH, Stockholm, Sweden Linear quadratic control Allows to compute the controller
More informationUncertainty and Robustness for SISO Systems
Uncertainty and Robustness for SISO Systems ELEC 571L Robust Multivariable Control prepared by: Greg Stewart Outline Nature of uncertainty (models and signals). Physical sources of model uncertainty. Mathematical
More informationEncoder Decoder Design for Feedback Control over the Binary Symmetric Channel
Encoder Decoder Design for Feedback Control over the Binary Symmetric Channel Lei Bao, Mikael Skoglund and Karl Henrik Johansson School of Electrical Engineering, Royal Institute of Technology, Stockholm,
More informationCDS 101/110a: Lecture 10-1 Robust Performance
CDS 11/11a: Lecture 1-1 Robust Performance Richard M. Murray 1 December 28 Goals: Describe how to represent uncertainty in process dynamics Describe how to analyze a system in the presence of uncertainty
More informationNONLINEAR AND ADAPTIVE (INTELLIGENT) SYSTEMS MODELING, DESIGN, & CONTROL A Building Block Approach
NONLINEAR AND ADAPTIVE (INTELLIGENT) SYSTEMS MODELING, DESIGN, & CONTROL A Building Block Approach P.A. (Rama) Ramamoorthy Electrical & Computer Engineering and Comp. Science Dept., M.L. 30, University
More informationarxiv: v2 [math.oc] 18 Jan 2015
Regularization for Design Nikolai Matni and Venkat Chandrasekaran January 20, 2015 arxiv:1404.1972v2 [math.oc] 18 Jan 2015 bstr When designing controllers for large-scale systems, the architectural aspects
More informationSparsity-promoting wide-area control of power systems. F. Dörfler, Mihailo Jovanović, M. Chertkov, and F. Bullo American Control Conference
Sparsity-promoting wide-area control of power systems F. Dörfler, Mihailo Jovanović, M. Chertkov, and F. Bullo 203 American Control Conference Electro-mechanical oscillations in power systems Local oscillations
More informationI. D. Landau, A. Karimi: A Course on Adaptive Control Adaptive Control. Part 9: Adaptive Control with Multiple Models and Switching
I. D. Landau, A. Karimi: A Course on Adaptive Control - 5 1 Adaptive Control Part 9: Adaptive Control with Multiple Models and Switching I. D. Landau, A. Karimi: A Course on Adaptive Control - 5 2 Outline
More informationMS-E2133 Systems Analysis Laboratory II Assignment 2 Control of thermal power plant
MS-E2133 Systems Analysis Laboratory II Assignment 2 Control of thermal power plant How to control the thermal power plant in order to ensure the stable operation of the plant? In the assignment Production
More informationNONLINEAR CONTROL with LIMITED INFORMATION. Daniel Liberzon
NONLINEAR CONTROL with LIMITED INFORMATION Daniel Liberzon Coordinated Science Laboratory and Dept. of Electrical & Computer Eng., Univ. of Illinois at Urbana-Champaign Plenary talk, 2 nd Indian Control
More informationQuantifying Cyber Security for Networked Control Systems
Quantifying Cyber Security for Networked Control Systems Henrik Sandberg ACCESS Linnaeus Centre, KTH Royal Institute of Technology Joint work with: André Teixeira, György Dán, Karl H. Johansson (KTH) Kin
More informationFinite-Time Thermodynamics of Port-Hamiltonian Systems
Finite-Time Thermodynamics of Port-Hamiltonian Systems Henrik Sandberg Automatic Control Lab, ACCESS Linnaeus Centre, KTH (Currently on sabbatical leave at LIDS, MIT) Jean-Charles Delvenne CORE, UC Louvain
More informationDr Ian R. Manchester Dr Ian R. Manchester AMME 3500 : Review
Week Date Content Notes 1 6 Mar Introduction 2 13 Mar Frequency Domain Modelling 3 20 Mar Transient Performance and the s-plane 4 27 Mar Block Diagrams Assign 1 Due 5 3 Apr Feedback System Characteristics
More informationLoop-Shaping Controller Design from Input-Output Data
Loop-Shaping Controller Design from Input-Output Data Kostas Tsakalis, Sachi Dash, ASU Honeywell HTC Control-Oriented ID: Uncertainty description compatible with the controller design method. (Loop-Shaping
More informationELEC4631 s Lecture 2: Dynamic Control Systems 7 March Overview of dynamic control systems
ELEC4631 s Lecture 2: Dynamic Control Systems 7 March 2011 Overview of dynamic control systems Goals of Controller design Autonomous dynamic systems Linear Multi-input multi-output (MIMO) systems Bat flight
More informationTheory in Model Predictive Control :" Constraint Satisfaction and Stability!
Theory in Model Predictive Control :" Constraint Satisfaction and Stability Colin Jones, Melanie Zeilinger Automatic Control Laboratory, EPFL Example: Cessna Citation Aircraft Linearized continuous-time
More informationLinear State Feedback Controller Design
Assignment For EE5101 - Linear Systems Sem I AY2010/2011 Linear State Feedback Controller Design Phang Swee King A0033585A Email: king@nus.edu.sg NGS/ECE Dept. Faculty of Engineering National University
More informationEncoder Decoder Design for Event-Triggered Feedback Control over Bandlimited Channels
Encoder Decoder Design for Event-Triggered Feedback Control over Bandlimited Channels LEI BAO, MIKAEL SKOGLUND AND KARL HENRIK JOHANSSON IR-EE- 26: Stockholm 26 Signal Processing School of Electrical Engineering
More informationFeedback Control Theory: Architectures and Tools for Real-Time Decision Making
Feedback Control Theory: Architectures and Tools for Real-Time Decision Making Richard M. Murray California Institute of Technology Real-Time Decision Making Bootcamp Simons Institute for the Theory of
More informationA State-Space Approach to Control of Interconnected Systems
A State-Space Approach to Control of Interconnected Systems Part II: General Interconnections Cédric Langbort Center for the Mathematics of Information CALIFORNIA INSTITUTE OF TECHNOLOGY clangbort@ist.caltech.edu
More informationEE C128 / ME C134 Feedback Control Systems
EE C128 / ME C134 Feedback Control Systems Lecture Additional Material Introduction to Model Predictive Control Maximilian Balandat Department of Electrical Engineering & Computer Science University of
More informationControl Systems Design
ELEC4410 Control Systems Design Lecture 3, Part 2: Introduction to Affine Parametrisation School of Electrical Engineering and Computer Science Lecture 3, Part 2: Affine Parametrisation p. 1/29 Outline
More informationYiming Lou and Panagiotis D. Christofides. Department of Chemical Engineering University of California, Los Angeles
OPTIMAL ACTUATOR/SENSOR PLACEMENT FOR NONLINEAR CONTROL OF THE KURAMOTO-SIVASHINSKY EQUATION Yiming Lou and Panagiotis D. Christofides Department of Chemical Engineering University of California, Los Angeles
More informationAdvanced Adaptive Control for Unintended System Behavior
Advanced Adaptive Control for Unintended System Behavior Dr. Chengyu Cao Mechanical Engineering University of Connecticut ccao@engr.uconn.edu jtang@engr.uconn.edu Outline Part I: Challenges: Unintended
More informationSwitched Mode Power Conversion Prof. L. Umanand Department of Electronics Systems Engineering Indian Institute of Science, Bangalore
Switched Mode Power Conversion Prof. L. Umanand Department of Electronics Systems Engineering Indian Institute of Science, Bangalore Lecture - 19 Modeling DC-DC convertors Good day to all of you. Today,
More informationRobust multi objective H2/H Control of nonlinear uncertain systems using multiple linear model and ANFIS
Robust multi objective H2/H Control of nonlinear uncertain systems using multiple linear model and ANFIS Vahid Azimi, Member, IEEE, Peyman Akhlaghi, and Mohammad Hossein Kazemi Abstract This paper considers
More informationExam. 135 minutes, 15 minutes reading time
Exam August 6, 208 Control Systems II (5-0590-00) Dr. Jacopo Tani Exam Exam Duration: 35 minutes, 5 minutes reading time Number of Problems: 35 Number of Points: 47 Permitted aids: 0 pages (5 sheets) A4.
More informationContents. PART I METHODS AND CONCEPTS 2. Transfer Function Approach Frequency Domain Representations... 42
Contents Preface.............................................. xiii 1. Introduction......................................... 1 1.1 Continuous and Discrete Control Systems................. 4 1.2 Open-Loop
More informationIterative Learning Control Analysis and Design I
Iterative Learning Control Analysis and Design I Electronics and Computer Science University of Southampton Southampton, SO17 1BJ, UK etar@ecs.soton.ac.uk http://www.ecs.soton.ac.uk/ Contents Basics Representations
More informationLecture 10, ATIK. Data converters 3
Lecture, ATIK Data converters 3 What did we do last time? A quick glance at sigma-delta modulators Understanding how the noise is shaped to higher frequencies DACs A case study of the current-steering
More informationCentralized Supplementary Controller to Stabilize an Islanded AC Microgrid
Centralized Supplementary Controller to Stabilize an Islanded AC Microgrid ESNRajuP Research Scholar, Electrical Engineering IIT Indore Indore, India Email:pesnraju88@gmail.com Trapti Jain Assistant Professor,
More informationDesign of Nonlinear Control Systems with the Highest Derivative in Feedback
SERIES ON STAB1UTY, VIBRATION AND CONTROL OF SYSTEMS SeriesA Volume 16 Founder & Editor: Ardeshir Guran Co-Editors: M. Cloud & W. B. Zimmerman Design of Nonlinear Control Systems with the Highest Derivative
More informationControl System Design
ELEC ENG 4CL4: Control System Design Notes for Lecture #24 Wednesday, March 10, 2004 Dr. Ian C. Bruce Room: CRL-229 Phone ext.: 26984 Email: ibruce@mail.ece.mcmaster.ca Remedies We next turn to the question
More informationFEL3210 Multivariable Feedback Control
FEL3210 Multivariable Feedback Control Lecture 5: Uncertainty and Robustness in SISO Systems [Ch.7-(8)] Elling W. Jacobsen, Automatic Control Lab, KTH Lecture 5:Uncertainty and Robustness () FEL3210 MIMO
More informationTopic # Feedback Control Systems
Topic #19 16.31 Feedback Control Systems Stengel Chapter 6 Question: how well do the large gain and phase margins discussed for LQR map over to DOFB using LQR and LQE (called LQG)? Fall 2010 16.30/31 19
More informationLecture 12. Upcoming labs: Final Exam on 12/21/2015 (Monday)10:30-12:30
289 Upcoming labs: Lecture 12 Lab 20: Internal model control (finish up) Lab 22: Force or Torque control experiments [Integrative] (2-3 sessions) Final Exam on 12/21/2015 (Monday)10:30-12:30 Today: Recap
More informationAnalysis and Synthesis of Single-Input Single-Output Control Systems
Lino Guzzella Analysis and Synthesis of Single-Input Single-Output Control Systems l+kja» \Uja>)W2(ja»\ um Contents 1 Definitions and Problem Formulations 1 1.1 Introduction 1 1.2 Definitions 1 1.2.1 Systems
More informationEffects of time quantization and noise in level crossing sampling stabilization
Effects of time quantization and noise in level crossing sampling stabilization Julio H. Braslavsky Ernesto Kofman Flavia Felicioni ARC Centre for Complex Dynamic Systems and Control The University of
More informationIndex. Index. More information. in this web service Cambridge University Press
A-type elements, 4 7, 18, 31, 168, 198, 202, 219, 220, 222, 225 A-type variables. See Across variable ac current, 172, 251 ac induction motor, 251 Acceleration rotational, 30 translational, 16 Accumulator,
More informationRésonance et contrôle en cavité ouverte
Résonance et contrôle en cavité ouverte Jérôme Hœpffner KTH, Sweden Avec Espen Åkervik, Uwe Ehrenstein, Dan Henningson Outline The flow case Investigation tools resonance Reduced dynamic model for feedback
More informationD(s) G(s) A control system design definition
R E Compensation D(s) U Plant G(s) Y Figure 7. A control system design definition x x x 2 x 2 U 2 s s 7 2 Y Figure 7.2 A block diagram representing Eq. (7.) in control form z U 2 s z Y 4 z 2 s z 2 3 Figure
More informationControl Systems 2. Lecture 4: Sensitivity function limits. Roy Smith
Control Systems 2 Lecture 4: Sensitivity function limits Roy Smith 2017-3-14 4.1 Input-output controllability Control design questions: 1. How well can the plant be controlled? 2. What control structure
More information(Refer Slide Time: 1:42)
Control Engineering Prof. Madan Gopal Department of Electrical Engineering Indian Institute of Technology, Delhi Lecture - 21 Basic Principles of Feedback Control (Contd..) Friends, let me get started
More informationFall 線性系統 Linear Systems. Chapter 08 State Feedback & State Estimators (SISO) Feng-Li Lian. NTU-EE Sep07 Jan08
Fall 2007 線性系統 Linear Systems Chapter 08 State Feedback & State Estimators (SISO) Feng-Li Lian NTU-EE Sep07 Jan08 Materials used in these lecture notes are adopted from Linear System Theory & Design, 3rd.
More informationSTATE AND OUTPUT FEEDBACK CONTROL IN MODEL-BASED NETWORKED CONTROL SYSTEMS
SAE AND OUPU FEEDBACK CONROL IN MODEL-BASED NEWORKED CONROL SYSEMS Luis A Montestruque, Panos J Antsalis Abstract In this paper the control of a continuous linear plant where the sensor is connected to
More informationPresentation Topic 1: Feedback Control. Copyright 1998 DLMattern
Presentation Topic 1: Feedback Control Outline Feedback Terminology Purpose of Feedback Limitations of Feedback Linear Control Design Techniques Nonlinear Control Design Techniques Rapid Prototyping Environments
More informationNetworked Control System Protocols Modeling & Analysis using Stochastic Impulsive Systems
Networked Control System Protocols Modeling & Analysis using Stochastic Impulsive Systems João P. Hespanha Center for Control Dynamical Systems and Computation Talk outline Examples feedback over shared
More informationModeling and Analysis of Dynamic Systems
Modeling and Analysis of Dynamic Systems by Dr. Guillaume Ducard Fall 2016 Institute for Dynamic Systems and Control ETH Zurich, Switzerland based on script from: Prof. Dr. Lino Guzzella 1/33 Outline 1
More informationControl System. Contents
Contents Chapter Topic Page Chapter- Chapter- Chapter-3 Chapter-4 Introduction Transfer Function, Block Diagrams and Signal Flow Graphs Mathematical Modeling Control System 35 Time Response Analysis of
More informationCDS 101/110a: Lecture 8-1 Frequency Domain Design
CDS 11/11a: Lecture 8-1 Frequency Domain Design Richard M. Murray 17 November 28 Goals: Describe canonical control design problem and standard performance measures Show how to use loop shaping to achieve
More informationAN INTRODUCTION CONTROL +
AN INTRODUCTION CONTROL + QUANTUM CIRCUITS AND COHERENT QUANTUM FEEDBACK CONTROL Josh Combes The Center for Quantum Information and Control, University of New Mexico Quantum feedback control Parameter
More information9. Two-Degrees-of-Freedom Design
9. Two-Degrees-of-Freedom Design In some feedback schemes we have additional degrees-offreedom outside the feedback path. For example, feed forwarding known disturbance signals or reference signals. In
More informationH -Control of Acoustic Noise in a Duct with a Feedforward Configuration
H -Control of Acoustic Noise in a Duct with a Feedforward Configuration K.A. Morris Department of Applied Mathematics University of Waterloo Waterloo, Ontario N2L 3G1 CANADA Abstract A mathematical model
More informationIdentification and Control of Mechatronic Systems
Identification and Control of Mechatronic Systems Philadelphia University, Jordan NATO - ASI Advanced All-Terrain Autonomous Systems Workshop August 15 24, 2010 Cesme-Izmir, Turkey Overview Mechatronics
More informationAutonomous Mobile Robot Design
Autonomous Mobile Robot Design Topic: Guidance and Control Introduction and PID Loops Dr. Kostas Alexis (CSE) Autonomous Robot Challenges How do I control where to go? Autonomous Mobile Robot Design Topic:
More informationToday (10/23/01) Today. Reading Assignment: 6.3. Gain/phase margin lead/lag compensator Ref. 6.4, 6.7, 6.10
Today Today (10/23/01) Gain/phase margin lead/lag compensator Ref. 6.4, 6.7, 6.10 Reading Assignment: 6.3 Last Time In the last lecture, we discussed control design through shaping of the loop gain GK:
More informationArchitectural Issues in Control System Design. Graham C. Goodwin. University of Newcastle, Australia
Architectural Issues in Control System Design Graham C. Goodwin University of Newcastle, Australia Presented at the Nordic Process Control Workshop 26-27 August, 2010 Control has been an incredibly rewarding
More informationEncoder Decoder Design for Event-Triggered Feedback Control over Bandlimited Channels
Encoder Decoder Design for Event-Triggered Feedback Control over Bandlimited Channels Lei Bao, Mikael Skoglund and Karl Henrik Johansson Department of Signals, Sensors and Systems, Royal Institute of Technology,
More informationAPPPHYS 217 Tuesday 6 April 2010
APPPHYS 7 Tuesday 6 April Stability and input-output performance: second-order systems Here we present a detailed example to draw connections between today s topics and our prior review of linear algebra
More informationMOST control systems are designed under the assumption
2076 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 53, NO. 9, OCTOBER 2008 Lyapunov-Based Model Predictive Control of Nonlinear Systems Subject to Data Losses David Muñoz de la Peña and Panagiotis D. Christofides
More informationControl System Design
ELEC4410 Control System Design Lecture 19: Feedback from Estimated States and Discrete-Time Control Design Julio H. Braslavsky julio@ee.newcastle.edu.au School of Electrical Engineering and Computer Science
More informationCopyrighted Material. 1.1 Large-Scale Interconnected Dynamical Systems
Chapter One Introduction 1.1 Large-Scale Interconnected Dynamical Systems Modern complex dynamical systems 1 are highly interconnected and mutually interdependent, both physically and through a multitude
More informationLinear System Theory. Wonhee Kim Lecture 1. March 7, 2018
Linear System Theory Wonhee Kim Lecture 1 March 7, 2018 1 / 22 Overview Course Information Prerequisites Course Outline What is Control Engineering? Examples of Control Systems Structure of Control Systems
More informationScientific Computing with Case Studies SIAM Press, Lecture Notes for Unit VII Sparse Matrix
Scientific Computing with Case Studies SIAM Press, 2009 http://www.cs.umd.edu/users/oleary/sccswebpage Lecture Notes for Unit VII Sparse Matrix Computations Part 1: Direct Methods Dianne P. O Leary c 2008
More informationA Robust Controller for Scalar Autonomous Optimal Control Problems
A Robust Controller for Scalar Autonomous Optimal Control Problems S. H. Lam 1 Department of Mechanical and Aerospace Engineering Princeton University, Princeton, NJ 08544 lam@princeton.edu Abstract Is
More informationAustralian National University WORKSHOP ON SYSTEMS AND CONTROL
Australian National University WORKSHOP ON SYSTEMS AND CONTROL Canberra, AU December 7, 2017 Australian National University WORKSHOP ON SYSTEMS AND CONTROL A Distributed Algorithm for Finding a Common
More informationCBE507 LECTURE III Controller Design Using State-space Methods. Professor Dae Ryook Yang
CBE507 LECTURE III Controller Design Using State-space Methods Professor Dae Ryook Yang Fall 2013 Dept. of Chemical and Biological Engineering Korea University Korea University III -1 Overview States What
More informationControl Theory: Design and Analysis of Feedback Systems
Control Theory: Design and Analysis of Feedback Systems Richard M. Murray 21 April 2008 Goals: Provide an introduction to key concepts and tools from control theory Illustrate the use of feedback for design
More informationInformation Structures, the Witsenhausen Counterexample, and Communicating Using Actions
Information Structures, the Witsenhausen Counterexample, and Communicating Using Actions Pulkit Grover, Carnegie Mellon University Abstract The concept of information-structures in decentralized control
More informationModel predictive control of industrial processes. Vitali Vansovitš
Model predictive control of industrial processes Vitali Vansovitš Contents Industrial process (Iru Power Plant) Neural networ identification Process identification linear model Model predictive controller
More informationCBE495 LECTURE IV MODEL PREDICTIVE CONTROL
What is Model Predictive Control (MPC)? CBE495 LECTURE IV MODEL PREDICTIVE CONTROL Professor Dae Ryook Yang Fall 2013 Dept. of Chemical and Biological Engineering Korea University * Some parts are from
More informationLecture 25: Tue Nov 27, 2018
Lecture 25: Tue Nov 27, 2018 Reminder: Lab 3 moved to Tuesday Dec 4 Lecture: review time-domain characteristics of 2nd-order systems intro to control: feedback open-loop vs closed-loop control intro to
More informationDistributed and Real-time Predictive Control
Distributed and Real-time Predictive Control Melanie Zeilinger Christian Conte (ETH) Alexander Domahidi (ETH) Ye Pu (EPFL) Colin Jones (EPFL) Challenges in modern control systems Power system: - Frequency
More informationModel Identification and Attitude Control Scheme for a Micromechanical Flying Insect
Model Identification and Attitude Control Scheme for a Micromechanical Flying Insect Xinyan Deng, Luca Schenato and Shankar Sastry Department of Electrical Engineering and Computer Sciences University
More informationAdvanced Aerospace Control. Marco Lovera Dipartimento di Scienze e Tecnologie Aerospaziali, Politecnico di Milano
Advanced Aerospace Control Dipartimento di Scienze e Tecnologie Aerospaziali, Politecnico di Milano ICT for control systems engineering School of Industrial and Information Engineering Aeronautical Engineering
More informationUniversal laws and architectures. John Doyle John G Braun Professor Control and Dynamical Systems, EE, BE Caltech
Universal laws and architectures John Doyle John G Braun Professor Control and Dynamical Systems, EE, BE Caltech Turing on layering The 'skin of an onion' analogy is also helpful. In considering the functions
More informationExam. 135 minutes + 15 minutes reading time
Exam January 23, 27 Control Systems I (5-59-L) Prof. Emilio Frazzoli Exam Exam Duration: 35 minutes + 5 minutes reading time Number of Problems: 45 Number of Points: 53 Permitted aids: Important: 4 pages
More informationA model-based approach to control over packet-switching networks, with application to Industrial Ethernet
A model-based approach to control over packet-switching networks, with application to Industrial Ethernet Universitá di Pisa Centro di Ricerca Interdipartimentale E. Piaggio Laurea specialistica in Ingegneria
More informationStochastic dynamical modeling:
Stochastic dynamical modeling: Structured matrix completion of partially available statistics Armin Zare www-bcf.usc.edu/ arminzar Joint work with: Yongxin Chen Mihailo R. Jovanovic Tryphon T. Georgiou
More informationControlling Structured Spatially Interconnected Systems
Controlling tructured patially Interconnected ystems Raffaello D Andrea Mechanical & Aerospace Engineering Cornell University Recent Developments... Proliferation of actuators and sensors Moore s Law Embedded
More informationBipedal Locomotion on Small Feet. Bipedal Locomotion on Small Feet. Pop Quiz for Tony 6/26/2015. Jessy Grizzle. Jessy Grizzle.
Bipedal Locomotion on Small Feet Jessy Grizzle Elmer G. Gilbert Distinguished University Professor Levin Professor of Engineering ECE and ME Departments Pop Quiz for Tony Can you give the first name of
More informationReview: stability; Routh Hurwitz criterion Today s topic: basic properties and benefits of feedback control
Plan of the Lecture Review: stability; Routh Hurwitz criterion Today s topic: basic properties and benefits of feedback control Goal: understand the difference between open-loop and closed-loop (feedback)
More informationModeling and Control Overview
Modeling and Control Overview D R. T A R E K A. T U T U N J I A D V A N C E D C O N T R O L S Y S T E M S M E C H A T R O N I C S E N G I N E E R I N G D E P A R T M E N T P H I L A D E L P H I A U N I
More informationFat tails, hard limits, thin layers John Doyle, Caltech
Fat tails, hard limits, thin layers John Doyle, Caltech Rethinking fundamentals* Parameter estimation and goodness of fit measures for fat tail distributions Hard limits on robust, efficient networks integrating
More informationA Quantum Computing Approach to the Verification and Validation of Complex Cyber-Physical Systems
A Quantum Computing Approach to the Verification and Validation of Complex Cyber-Physical Systems Achieving Quality and Cost Control in the Development of Enormous Systems Safe and Secure Systems and Software
More informationarxiv: v1 [cs.sy] 5 May 2018
Event-triggering stabilization of real and complex linear systems with disturbances over digital channels Mohammad Javad Khojasteh, Mojtaba Hedayatpour, Jorge Cortés, Massimo Franceschetti arxiv:85969v
More informationPlan of the Lecture. Review: stability; Routh Hurwitz criterion Today s topic: basic properties and benefits of feedback control
Plan of the Lecture Review: stability; Routh Hurwitz criterion Today s topic: basic properties and benefits of feedback control Plan of the Lecture Review: stability; Routh Hurwitz criterion Today s topic:
More information