AFRL MACCCS Review. Adaptive Control of the Generic Hypersonic Vehicle
|
|
- Tracy Morton
- 5 years ago
- Views:
Transcription
1 AFRL MACCCS Review of the Generic Hypersonic Vehicle PI: Active- Laboratory Department of Mechanical Engineering Massachusetts Institute of Technology September 19, 2012, MIT AACL 1/38
2 Our Team MIT Team PI:, Director, MIT Active Laboratory Daniel Wiese (MS Student) Spent the summer at AFRL as an intern Travis Gibson, Megumi Matsutani, Benjamin Jenkins, Heather Hussain, Max Qu (PhD students; not supported on this project) AFRL Team Dr. Mike Bolender (Team Lead) Dr. Jonathan Muse UM Team Prof. Jim Driscoll Sean Torrez, Derek Dalle Advisor Dr. Eugene Lavretsky, Senior Technical Fellow, Boeing Research & Technology Project started October 2011, biweekly telecons held with AFRL, MIT AACL 2/38
3 Advanced Architectures Goal Develop algorithms, methods, and models for the control of hypersonic vehicle configurations that include combinations of control surfaces, morphing and other configuration changes, response to uncertain or ambiguous environments, and recovery from subsystem failure., MIT AACL 3/38
4 Need for x-15 x-43 Limited wind tunnel data Harsh uncertain environments Poorly known physical models Actuator anomalies Largely varying operating conditions GHV From Doman s slides, MIT AACL 4/38
5 , MIT AACL 5/38
6 Execute desired maneuvers at various points on the flight envelope: Mach 4-8, q = psf High bank angle turns Large angle of attack climbs Nominal flight condition is Mach 6 at 80,000 ft altitude, MIT AACL 6/38
7 AFRL 6 DOF GHV (Generic Hypersonic Vehicle) Aircraft assumed to be rigid Earth centered, inertial reference frame Spherical, rotating Earth Aerodynamic forces and moments determined using look-up table Aerodynamic table values based on α, β, M, and control surface deflections Engine data tabulated as a function of M, α, ϕ, q Sideslip β not accounted for in thrust tables Data tables are interpolated over based on input for given flight condition, MIT AACL 7/38
8 State Space Representation The GHV has four aerodynamic control surfaces and throttle Left and right rudder Left and right elevon Input vector U accomplished by mixing effective elevator and aileron commands to generate right and left elevon deflections U = [ δ th δ elv δ ail δ rud ] T The GHV is maneuvered by commanding V T, φ, α Assuming small linearization error, the equations of motion when linearized about a trim point X eq and U eq are given by ẋ p = A p x p + B p u where x p and u are the perturbed state and input about trimmed flight condition, MIT AACL 8/38
9 Linear : Flight Modes An open-loop analysis of the linear equations about the nominal flight condition was performed, yielding the following modes of flight. Irregular Short Period (λ 1,3 ) - an unstable mode dominated by α and q. Fast, purely real poles, with λ 1,3 ±2. Rolling (λ 2 ) - a stable mode, dominated by β and p. Fast, purely real pole at λ 2 = 4.87 Dutch-Roll (λ 4,5 ) - an unstable mode, which is a combination of a rolling, pitching, and yawing motion in flight. Phugoid (λ 6,7 ) - a neutrally stable, slow phugoid mode. Velocity (λ 8 ) - neutrally stable. Spiral (λ 9 ) - a slow, but stable mode., MIT AACL 9/38
10 Linear : Open-Loop Poles Imaginary Open Loop Poles Dutch Roll Phugoid Spiral 0.4 Short Period 0.6 Rolling Real, MIT AACL 10/38
11 noise xcmd 100 Hz Controller 600 Hz τ delay Actuator Dynamics Equations of Motion Simulation block diagram Assume full state accessible for feedback control Sensors measuring at 600 Hz with sample and hold Low-pass first order discrete sensor output filter Gaussian white noise injected into each sensor channel Controller operating at 100 Hz using zero order hold Input delay can be added to control input signal, MIT AACL 11/38
12 Actuators and Sensors Actuator Model (Control Surface) Second order actuators drive each aerodynamic control surface Saturation included on deflection angle and rate 30 deg deflection limit 100 deg/s rate limit δ cmd deflection saturation Σ ω n 2 δ Σ 1 s δ 2ζω n rate saturation 2nd order actuator model: dynamics and saturation 1 s δ deflection saturation Actuator Model (Throttle) Engine modeled as first order system with cutoff τ = 10 Sensor Model First order output filters defined in continuous time and implemented discrete time Velocity output filter with τ = 20 All other output filters τ = 150, MIT AACL 12/38 δ act
13 X = [ V T α q θ h β p r φ ] T U = [ δ th δ elv δ ail δ rud ] T Baseline design consists of three linear controllers Modal analysis allows the velocity, longitudinal, and lateral subsystems to be considered independently for linear control design about nominal flight condition Timescale separation between phugoid and irregular short period allows feedback of fast states only An LQR-PI control was implemented on each of the three subsystems V T,cmd α cmd φ cmd β cmd = 0 Velocity Controller Longitudinal Controller Lateral Controller δ th V T δ e α, q δ a, δ r β, p, r, φ GHV, MIT AACL 13/38
14 r 0 Σ e K(s) d n u o u i y o y i Σ G(s) Σ Frequency domain analysis block diagram The LQR-PI control architecture can be represented using the block diagram above Bode and singular value plots of the transfer function matrix were used to guide selection of state and input weighting matrices Q and R, changing the feedback gains Quadratic cost function dictates feedback gains J = [x(t) T Qx(t) + u(t) T Ru(t)]dt 0, MIT AACL 14/38
15 : Velocity and Longitudinal Subsystems Magnitude [db] Velocity Controller Bode Plot GM:Inf db PM:65.56 Gain Grossover:0.31 Hz Magnitude [db] Longitudinal Controller Bode Plot GM: db PM:49.24 Gain Grossover:1.98 Hz Phase [deg] Frequency ω [rad/s] Phase Frequency ω [rad/s] Phase [deg] Frequency ω [rad/s] Phase Frequency ω [rad/s] allowed for sufficient margins while maintaining desirable crossover frequencies, MIT AACL 15/38
16 : Lateral Controller Magnitude [db] Magnitude [db] Magnitude [db] Control Loop: σ(lu) Frequency ω [rad/s] Stability Robustness: σ(1+l 1 u ) at Plant Input Frequency ω [rad/s] Complementary Output Sensitivity: σ(t(s)): r to y Frequency ω [rad/s] Magnitude [db] Magnitude [db] Magnitude [db] Return Difference: σ(1+lu) at Plant Input Frequency ω [rad/s] Output Sensitivity: σ(s(s)): n to y Frequency ω [rad/s] Noise to Control: σ(k) Frequency ω [rad/s] Lateral subsystem singular value plots, MIT AACL 16/38
17 Inner loop flight Controller Subsystem Order Subsystem Order Integrators Augmented Order Velocity 1 V T 2 Longitudinal 2 α 3 Lateral 4 φ, β 6 Loop transfer function crossover frequencies Subsystem Crossover [rad] Crossover [Hz] Delay Margin [ms] Velocity Longitudinal Lateral V T,cmd Velocity Controller δ th V T α cmd Longitudinal Controller δ e α, q GHV φ cmd β cmd = 0 Lateral Controller δ a, δ r β, p, r, φ, MIT AACL 17/38
18 (a) Control surface effectiveness (b) CG movement (c) Stability derivatives (d) Actuator anomalies Failure Saturation (e) Time delay Control surface failure CG shift Saturation Time delay, MIT AACL 18/38
19 How do lers Work? GHV, MIT AACL 19/38
20 How do lers Work? GHV, MIT AACL 19/38
21 How do lers Work? Controller GHV θ Adaptive System, MIT AACL 19/38
22 How do lers Work? Controller GHV e Adaptive System θ, MIT AACL 19/38
23 How do lers Work? Controller GHV Adaptive System Adapt θ so that e(t) 0 θ e (error), MIT AACL 19/38
24 How do lers Work? Controller GHV Adaptive System Adapt θ so that e(t) 0 θ Construct a suitable e Find an adaptive law for θ e (error), MIT AACL 19/38
25 Choice of Adaptive Law Makes a Difference! 1 x-15 MH-96 AFCS x-15 Provably Correct AFCS 1 Z.T. Dydek, A.M., and E. Lavretsky, and the NASA X-15 Program: A Concise History, Lessons Learned, and a Provably Correct," IEEE Control Systems Magazine, June (Best Paper Award Winner), MIT AACL 20/38
26 A Closer Look MH-96 AFCS, MIT AACL 21/38
27 A Closer Look Provably Correct AFCS MH-96 AFCS, MIT AACL 21/38
28 A Closer Look Provably Correct AFCS MH-96 AFCS, MIT AACL 21/38
29 A Closer Look 24 Adaptive Parameters 3 Adaptive Parameters, MIT AACL 21/38
30 A Closer Look 8 Adaptive Parameters (Coupling Removed), MIT AACL 21/38
31 A Closer Look 8 Adaptive Parameters (Coupling Removed), MIT AACL 21/38
32 A Closer Look 8 Adaptive Parameters (Coupling Removed), MIT AACL 21/38
33 A Closer Look 3 Adaptive Parameters (α, β integral states removed; e u replaced with e), MIT AACL 21/38
34 A Closer Look 3 Adaptive Parameters (α, β integral states removed; e u replaced with e), MIT AACL 21/38
35 A Closer Look 3 Adaptive Parameters (α, β integral states removed; e u replaced with e), MIT AACL 21/38
36 A Closer Look 3 Adaptive Parameters (PC Adaptive law replaced with MH-96 logic), MIT AACL 21/38
37 A Closer Look 3 Adaptive Parameters (PC Adaptive law replaced with MH-96 logic) PC Adaptive law is key!, MIT AACL 21/38
38 x cmd Baseline Controller Adaptive Controller u nom u ad Σ u Plant : (a) Control Effectiveness (b) CG movement (c) Stability derivatives (d) Saturation x Adaptive controller added around baseline controller (a-d), after linearization, lead to ẋ p = A pλ x p + B p Λu The matrix Λ is diagonal with entries of known sign With an integral state, the underlying plant dynamics become ẋ = A λ x + B 1 Λu + B 2 x cmd, MIT AACL 22/38
39 Reference Model Linearize Ẋ = f (X, U) about equilibrium: f (X,U) A pλ = B pλ = X eq f (X,U) U Augment the plant matrices with x e = x x cmd [ ] [ ][ ] [ ] [ d xp Apλ 0 xp Bpλ 0 = + u + x dt x e H 0 x e 0 I] cmd Nominal plant dynamics: set Λ = I; A λ = A; B λ = B 1 Λ = B 1 Baseline control law ẋ = Ax + B 1 u + B 2 x cmd u = K T x Reference model: nominal plant + baseline controller ẋ m = A m x m + B m x cmd eq A m = A + B 1 K T and B m = B 2, MIT AACL 23/38
40 x cmd Baseline Controller u nom Σ u Reference Model Plant x Σ e Adaptive Controller u ad Plant: ẋ = A λ x + B 1 Λu + B 2 x cmd Reference model: ẋ m = (A + B 1 K T )x m + B 2 x cmd Error: e = x x m Control law: u = (θ + K) T x Adaptive gain update law: θ = Proj Γ (θ, Γxe T PB 1 sign(λ)), MIT AACL 24/38
41 Adaptive Update Law A Projection algorithm is used to ensure parameter boundedness. θ = Proj Γ (θ, Γxe T PB 1 sign(λ)) Proj(θ,y) y f (θ) m = θ θ b θ θ b Ω 0 Ω A {θ f (θ) = 0} {θ f (θ) = 1} Projection operator in θ space, MIT AACL 25/38
42 Stability and Convergence θ : True control parameter Assumption: θ satisfies θ T = Λ 1 (I Λ)K T W T A λ + B 1 Λ(θ + K) T = A m Global stability and convergence ( θ = θ θ ) V = e T Pe + tr ( θ T Γ 1 θ Λ ) V 0 lim e(t) = 0 t x cmd Baseline Controller θ Adaptive Controller u nom u ad Σ u Plant x, MIT AACL 26/38
43 noise xcmd 100 Hz Baseline 600 Hz τ delay Adaptive Actuator Dynamics Equations of Motion Simulation block diagram The following simulation results demonstrate adaptive controller performance for two commanded tasks, M = 6, h = 80, 000 ft 2 : 3 deg angle of attack doublet : 80 deg roll step Command tasks performed in the presence of uncertainties (a) Control ineffectiveness (b) CG shift (c) Stability derivative uncertainty The time delay is set to zero 2 GNC Paper in Progress of the Generic Hypersonic Vehicle in Presence of Actuator, CG, and Aerodynamic, MIT AACL 27/38
44 with Uncertainty (a) α [deg] 5 0 Angle of Attack Command Baseline Reference Adaptive az [ft/s 2 ] δe [deg] time [s] Elevator Deflection Angle time [s] Normal Acceleration time [s] Reduced control surface effectiveness: 50% on all surfaces For this uncertainty and command, the adaptive controller can tolerate a time delay of up to τ delay = 28 ms, MIT AACL 28/38
45 with Uncertainty (b) α [deg] 5 0 Angle of Attack Command Baseline Reference Adaptive az [ft/s 2 ] δe [deg] time [s] Elevator Deflection Angle time [s] Normal Acceleration time [s] Longitudinal CG shift: -0.7 ft rearward CG shift of -0.7 ft is 5% of the vehicle length For this uncertainty and command, the adaptive controller can tolerate a time delay of up to τ delay = 20 ms, MIT AACL 29/38
46 with Uncertainty (c) α [deg] 5 0 Angle of Attack Command Baseline Reference Adaptive az [ft/s 2 ] δe [deg] time [s] Elevator Deflection Angle time [s] Normal Acceleration time [s] Pitching moment coefficient scaled 350% For this uncertainty and command, the adaptive controller can tolerate a time delay of up to τ delay = 18 ms, MIT AACL 30/38
47 with Uncertainty (a) φ [deg] δa [deg] δr [deg] Roll Angle Command Baseline Reference Adaptive time [s] Aileron Deflection Angle time [s] Rudder Deflection Angle time [s] Reduced control surface effectiveness: 50% on all surfaces Tolerable time delay τ delay = 56 ms, MIT AACL 31/38
48 with Uncertainty (b) φ [deg] δa [deg] δr [deg] Roll Angle Command Baseline Reference Adaptive time [s] Aileron Deflection Angle time [s] Rudder Deflection Angle time [s] Longitudinal CG shift: -1.7 ft rearward CG shift of -1.7 ft is 12% of the vehicle length Tolerable time delay τ delay = 35 ms, MIT AACL 32/38
49 Adaptive controller implemented on 6-DOF nonlinear sim, operating at 100 Hz, with sensor input delay and output noise Adaptive controller performance evaluated when performing two tasks : 3 deg angle of attack doublet : 80 deg roll step in the presence of uncertainties (a) Control ineffectiveness up to 50% (b) CG shift -0.7 to -1.7 ft (5-12% vehicle length) (c) Stability derivative uncertainty up to 350% Adaptive MRAC architecture with projection showed increased tracking performance and stability over baseline controller for each task with uncertainty Input time delay of ms tolerated, MIT AACL 33/38
50 Collaboration and Tasks : Develop adaptive controllers for high performance maneuvers through unstart Simple model proposed by AFRL to capture essential effect of unstart, a function of: α, β, M When unstart occurs on the GHV All thrust forces and moments become zero Drag increases by 20% Lift decreases by 20% Pitching and yawing moment coefficient curves shifted and scaled: C M (α) = C Mnom (α) α C N (β) = C Nnom (β) β continued, MIT AACL 34/38
51 Collaboration and Tasks : (continued) : Same adaptive controller as in earlier slides was implemented Preliminary results showed successful maneuver through unstart for a 5 deg doublet in α Collaboration with Prof. Driscoll to include more detailed use of engine data and unstart model to guide control design Incorporation of unstart features in the nonlinear GHV model M α ϕ q Nonlinear GHV model unstart Simulation block diagram, MIT AACL 35/38
52 Collaboration and Tasks Task 3 Adaptive control with improved transient response Investigate the use of methods in 3 to overcome unstart Task 4 Improved transient response using CRM-adaptive 2 Quantify delay margins for the adaptive controller using 4,5 3 Gibson, T.E.,, A.M. and Lavretsky, E. Adaptive Systems with Closed-loop Reference Models, Part I: Transient Performance, ACC13 4 Matsutani, M.,, A.M. and Lavretsky, E. Guaranteed Delay Margins for of Scalar Plants, CDC12 5 Matsutani, M.,, A.M. and Lavretsky, E. Guaranteed Delay Margins for Adaptive Systems with State Variables Accessible, ACC13, MIT AACL 36/38
53 Collaboration and Tasks Task 5 Task 6 Task 7 Investigate the use of rate-saturation for improving the adaptive control performance 6 Investigate the use of state constraints for improving the adaptive control performance 7,8 Develop adaptive control based on output feedback Reliable incidence measurements not available on hypersonic vehicles Instead of angle of attack and sideslip, measure accelerations A x A y A z Control using outputs C and D C = A z + k q q and D = A y + k p p 6 Matsutani, M.,, A.M. and Crespo, L. in the Presence of Rate Saturation with Application to GTM, GNC10 7 Muse, J. A Method For Enforcing State Constraints in 8 Lavretsky, E., and Gradient, R. Robust Adaptive for Aerial Vehicles with State-Limiting Constraints, JGCD10, MIT AACL 37/38
54 Long Term Goals x cmd Baseline Controller u nom Σ u x Adaptive Controller u ad Unstart model Develop adaptive architectures that accommodate uncertainty effects due to unstart, MIT AACL 38/38
Adaptive Control of a Generic Hypersonic Vehicle
Adaptive Control of a Generic Hypersonic Vehicle Daniel P. Wiese and Anuradha M. Annaswamy Massachusetts Institute of Technology, Cambridge, MA 2139, USA Jonathan A. Muse and Michael A. Bolender U.S. Air
More informationAdaptive Output Feedback Based on Closed-Loop. Reference Models for Hypersonic Vehicles
Adaptive Output Feedback Based on Closed-Loop Reference Models for Hypersonic Vehicles Daniel P. Wiese 1 and Anuradha M. Annaswamy 2 Massachusetts Institute of Technology, Cambridge, MA 02139 Jonathan
More informationAdaptive control of time-varying systems with gain-scheduling
2008 American Control Conference Westin Seattle Hotel, Seattle, Washington, USA June 11-13, 2008 ThC14.5 Adaptive control of time-varying systems with gain-scheduling Jinho Jang, Anuradha M. Annaswamy,
More informationAdaptive Control of Hypersonic Vehicles in Presence of Aerodynamic and Center of Gravity Uncertainties
Control of Hypersonic Vehicles in Presence of Aerodynamic and Center of Gravity Uncertainties Amith Somanath and Anuradha Annaswamy Abstract The paper proposes a new class of adaptive controllers that
More informationAdaptive Augmentation of a Fighter Aircraft Autopilot Using a Nonlinear Reference Model
Proceedings of the EuroGNC 13, 2nd CEAS Specialist Conference on Guidance, Navigation & Control, Delft University of Technology, Delft, The Netherlands, April -12, 13 Adaptive Augmentation of a Fighter
More informationFault-Tolerant Control of a Unmanned Aerial Vehicle with Partial Wing Loss
Preprints of the 19th World Congress The International Federation of Automatic Control Fault-Tolerant Control of a Unmanned Aerial Vehicle with Partial Wing Loss Wiaan Beeton J.A.A. Engelbrecht Stellenbosch
More informationDepartment of Aerospace Engineering and Mechanics University of Minnesota Written Preliminary Examination: Control Systems Friday, April 9, 2010
Department of Aerospace Engineering and Mechanics University of Minnesota Written Preliminary Examination: Control Systems Friday, April 9, 2010 Problem 1: Control of Short Period Dynamics Consider the
More informationFLIGHT DYNAMICS. Robert F. Stengel. Princeton University Press Princeton and Oxford
FLIGHT DYNAMICS Robert F. Stengel Princeton University Press Princeton and Oxford Preface XV Chapter One Introduction 1 1.1 ELEMENTS OF THE AIRPLANE 1 Airframe Components 1 Propulsion Systems 4 1.2 REPRESENTATIVE
More informationFrequency Domain System Identification for a Small, Low-Cost, Fixed-Wing UAV
Frequency Domain System Identification for a Small, Low-Cost, Fixed-Wing UAV Andrei Dorobantu, Austin M. Murch, Bernie Mettler, and Gary J. Balas, Department of Aerospace Engineering & Mechanics University
More informationMech 6091 Flight Control System Course Project. Team Member: Bai, Jing Cui, Yi Wang, Xiaoli
Mech 6091 Flight Control System Course Project Team Member: Bai, Jing Cui, Yi Wang, Xiaoli Outline 1. Linearization of Nonlinear F-16 Model 2. Longitudinal SAS and Autopilot Design 3. Lateral SAS and Autopilot
More informationConfidence metrics analysis of a fixed-wing UAV. Janos Polgar
Confidence metrics analysis of a fixed-wing UAV A THESIS SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY Janos Polgar IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
More informationTurn Performance of an Air-Breathing Hypersonic Vehicle
Turn Performance of an Air-Breathing Hypersonic Vehicle AIAA Aircraft Flight Mechanics Conference Derek J. Dalle, Sean M. Torrez, James F. Driscoll University of Michigan, Ann Arbor, MI 4809 August 8,
More informationHypersonic Vehicle (HSV) Modeling
Hypersonic Vehicle (HSV) Modeling Carlos E. S. Cesnik Associate Professor of Aerospace Engineering University of Michigan, Ann Arbor HSV Concentration MA Kickoff Meeting Ann Arbor, 29 August 2007 Team
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 informationThe Role of Zero Dynamics in Aerospace Systems
The Role of Zero Dynamics in Aerospace Systems A Case Study in Control of Hypersonic Vehicles Andrea Serrani Department of Electrical and Computer Engineering The Ohio State University Outline q Issues
More informationSquaring-Up Method for Relative Degree Two Plants
1 Squaring-Up Method for Relative Degree Two Plants Zheng Qu 1, Anuradha M. Annaswamy 1 and Eugene Lavretsky Abstract Non-square multi-input-multi-output (MIMO) plants are becoming increasingly common,
More informationDESIGN PROJECT REPORT: Longitudinal and lateral-directional stability augmentation of Boeing 747 for cruise flight condition.
DESIGN PROJECT REPORT: Longitudinal and lateral-directional stability augmentation of Boeing 747 for cruise flight condition. Prepared By: Kushal Shah Advisor: Professor John Hodgkinson Graduate Advisor:
More informationApplications Linear Control Design Techniques in Aircraft Control I
Lecture 29 Applications Linear Control Design Techniques in Aircraft Control I Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore Topics Brief Review
More informationApplications of Linear and Nonlinear Robustness Analysis Techniques to the F/A-18 Flight Control Laws
AIAA Guidance, Navigation, and Control Conference 10-13 August 2009, Chicago, Illinois AIAA 2009-5675 Applications of Linear and Nonlinear Robustness Analysis Techniques to the F/A-18 Flight Control Laws
More informationAim. Unit abstract. Learning outcomes. QCF level: 6 Credit value: 15
Unit T23: Flight Dynamics Unit code: J/504/0132 QCF level: 6 Credit value: 15 Aim The aim of this unit is to develop learners understanding of aircraft flight dynamic principles by considering and analysing
More informationLecture #AC 3. Aircraft Lateral Dynamics. Spiral, Roll, and Dutch Roll Modes
Lecture #AC 3 Aircraft Lateral Dynamics Spiral, Roll, and Dutch Roll Modes Copy right 2003 by Jon at h an H ow 1 Spring 2003 16.61 AC 3 2 Aircraft Lateral Dynamics Using a procedure similar to the longitudinal
More informationAn Adaptive Reset Control System for Flight Safety in the Presence of Actuator Anomalies
21 American Control Conference Marriott Waterfront, Baltimore, MD, USA June 3-July 2, 21 WeB15.2 An Adaptive Reset Control System for Flight Safety in the Presence of Actuator Anomalies Megumi Matsutani
More informationAdaptive Control and the NASA X-15-3 Flight Revisited
Adaptive Control and the NASA X-15-3 Flight Revisited The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation As Published Publisher
More informationContinuous Differentiation of Complex Systems Applied to a Hypersonic Vehicle
Continuous of Complex Systems Applied to a Vehicle AIAA Aircraft Flight Mechanics Conference Derek J. Dalle, Sean M. Torrez, James F. Driscoll University of Michigan, Ann Arbor, MI 4819 August 15, 212,
More informationMAE 142 Homework #5 Due Friday, March 13, 2009
MAE 142 Homework #5 Due Friday, March 13, 2009 Please read through the entire homework set before beginning. Also, please label clearly your answers and summarize your findings as concisely as possible.
More informationSusceptibility of F/A-18 Flight Control Laws to the Falling Leaf Mode Part I: Linear Analysis
Susceptibility of F/A-18 Flight Control Laws to the Falling Leaf Mode Part I: Linear Analysis Abhijit Chakraborty, Peter Seiler and Gary J. Balas Department of Aerospace Engineering & Mechanics University
More informationJ. F. Driscoll. University of Michigan
J. F. Driscoll University of Michigan Sean Torrez, Derek Dalle, Matt Fotia (NASA) AFRL collaborators: Mike Bolender, David Doman, Mike Oppenheimer Control of flight dynamics Pitching moment (M) in vehicle
More informationDigital Autoland Control Laws Using Direct Digital Design and Quantitative Feedback Theory
AIAA Guidance, Navigation, and Control Conference and Exhibit 1-4 August 6, Keystone, Colorado AIAA 6-699 Digital Autoland Control Laws Using Direct Digital Design and Quantitative Feedback Theory Thomas
More informationÜbersetzungshilfe / Translation aid (English) To be returned at the end of the exam!
Prüfung Regelungstechnik I (Control Systems I) Prof. Dr. Lino Guzzella 3.. 24 Übersetzungshilfe / Translation aid (English) To be returned at the end of the exam! Do not mark up this translation aid -
More informationFlying Qualities Criteria Robert Stengel, Aircraft Flight Dynamics MAE 331, 2018
Flying Qualities Criteria Robert Stengel, Aircraft Flight Dynamics MAE 331, 2018 Learning Objectives MIL-F-8785C criteria CAP, C*, and other longitudinal criteria ϕ/β, ω ϕ /ω d, and other lateral-directional
More informationExperimental Aircraft Parameter Estimation
Experimental Aircraft Parameter Estimation AA241X May 14 2014 Stanford University Overview 1. System & Parameter Identification 2. Energy Performance Estimation Propulsion OFF Propulsion ON 3. Stability
More informationRobust Control. 8th class. Spring, 2018 Instructor: Prof. Masayuki Fujita (S5-303B) Tue., 29th May, 2018, 10:45~11:30, S423 Lecture Room
Robust Control Spring, 2018 Instructor: Prof. Masayuki Fujita (S5-303B) 8th class Tue., 29th May, 2018, 10:45~11:30, S423 Lecture Room 1 8. Design Example 8.1 HiMAT: Control (Highly Maneuverable Aircraft
More informationFAULT DETECTION AND FAULT TOLERANT APPROACHES WITH AIRCRAFT APPLICATION. Andrés Marcos
FAULT DETECTION AND FAULT TOLERANT APPROACHES WITH AIRCRAFT APPLICATION 2003 Louisiana Workshop on System Safety Andrés Marcos Dept. Aerospace Engineering and Mechanics, University of Minnesota 28 Feb,
More informationMechanics of Flight. Warren F. Phillips. John Wiley & Sons, Inc. Professor Mechanical and Aerospace Engineering Utah State University WILEY
Mechanics of Flight Warren F. Phillips Professor Mechanical and Aerospace Engineering Utah State University WILEY John Wiley & Sons, Inc. CONTENTS Preface Acknowledgments xi xiii 1. Overview of Aerodynamics
More informationADAPTIVE NEURAL NETWORK CONTROLLER DESIGN FOR BLENDED-WING UAV WITH COMPLEX DAMAGE
ADAPTIVE NEURAL NETWORK CONTROLLER DESIGN FOR BLENDED-WING UAV WITH COMPLEX DAMAGE Kijoon Kim*, Jongmin Ahn**, Seungkeun Kim*, Jinyoung Suk* *Chungnam National University, **Agency for Defense and Development
More informationA SIMPLIFIED ANALYSIS OF NONLINEAR LONGITUDINAL DYNAMICS AND CONCEPTUAL CONTROL SYSTEM DESIGN
A SIMPLIFIED ANALYSIS OF NONLINEAR LONGITUDINAL DYNAMICS AND CONCEPTUAL CONTROL SYSTEM DESIGN ROBBIE BUNGE 1. Introduction The longitudinal dynamics of fixed-wing aircraft are a case in which classical
More informationApril 15, 2011 Sample Quiz and Exam Questions D. A. Caughey Page 1 of 9
April 15, 2011 Sample Quiz Exam Questions D. A. Caughey Page 1 of 9 These pages include virtually all Quiz, Midterm, Final Examination questions I have used in M&AE 5070 over the years. Note that some
More informationAMME3500: System Dynamics & Control
Stefan B. Williams May, 211 AMME35: System Dynamics & Control Assignment 4 Note: This assignment contributes 15% towards your final mark. This assignment is due at 4pm on Monday, May 3 th during Week 13
More informationChapter 4 The Equations of Motion
Chapter 4 The Equations of Motion Flight Mechanics and Control AEM 4303 Bérénice Mettler University of Minnesota Feb. 20-27, 2013 (v. 2/26/13) Bérénice Mettler (University of Minnesota) Chapter 4 The Equations
More informationANALYSIS OF MULTIPLE FLIGHT CONTROL ARCHITECTURES ON A SIX DEGREE OF FREEDOM GENERAL AVIATION AIRCRAFT. A Thesis by. John Taylor Oxford, Jr.
ANALYSIS OF MULTIPLE FLIGHT CONTROL ARCHITECTURES ON A SIX DEGREE OF FREEDOM GENERAL AVIATION AIRCRAFT A Thesis by John Taylor Oxford, Jr. Bachelor of Science, Georgia Institute of Technology, 2007 Submitted
More informationAircraft Stability & Control
Aircraft Stability & Control Textbook Automatic control of Aircraft and missiles 2 nd Edition by John H Blakelock References Aircraft Dynamics and Automatic Control - McRuler & Ashkenas Aerodynamics, Aeronautics
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 informationFlight Dynamics, Simulation, and Control
Flight Dynamics, Simulation, and Control For Rigid and Flexible Aircraft Ranjan Vepa CRC Press Taylor & Francis Group Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Group, an
More informationChapter 2 Review of Linear and Nonlinear Controller Designs
Chapter 2 Review of Linear and Nonlinear Controller Designs This Chapter reviews several flight controller designs for unmanned rotorcraft. 1 Flight control systems have been proposed and tested on a wide
More informationSupervisor: Dr. Youmin Zhang Amin Salar Zahra Gallehdari Narges Roofigari
Supervisor: Dr. Youmin Zhang Amin Salar 6032761 Zahra Gallehdari 1309102 Narges Roofigari 8907926 Fault Diagnosis and Fault Tolerant Control Systems Final Project December 2011 Contents Introduction Quad-Rotor
More informationWhat is flight dynamics? AE540: Flight Dynamics and Control I. What is flight control? Is the study of aircraft motion and its characteristics.
KING FAHD UNIVERSITY Department of Aerospace Engineering AE540: Flight Dynamics and Control I Instructor Dr. Ayman Hamdy Kassem What is flight dynamics? Is the study of aircraft motion and its characteristics.
More informationPitch Control of Flight System using Dynamic Inversion and PID Controller
Pitch Control of Flight System using Dynamic Inversion and PID Controller Jisha Shaji Dept. of Electrical &Electronics Engineering Mar Baselios College of Engineering & Technology Thiruvananthapuram, India
More informationVortex Model Based Adaptive Flight Control Using Synthetic Jets
Vortex Model Based Adaptive Flight Control Using Synthetic Jets Jonathan Muse, Andrew Tchieu, Ali Kutay, Rajeev Chandramohan, Anthony Calise, and Anthony Leonard Department of Aerospace Engineering Georgia
More informationAdaptive Output Feedback Based on Closed-Loop Reference Models for Hypersonic Vehicles
Adaptive Output Feedback Based on Closed-Loop Reference Models for Hypersonic Veicles Daniel P. Wiese and Anurada M. Annaswamy Massacusetts Institute of Tecnology, Cambridge, MA 02139, USA Jonatan A. Muse
More informationH 2 Adaptive Control. Tansel Yucelen, Anthony J. Calise, and Rajeev Chandramohan. WeA03.4
1 American Control Conference Marriott Waterfront, Baltimore, MD, USA June 3-July, 1 WeA3. H Adaptive Control Tansel Yucelen, Anthony J. Calise, and Rajeev Chandramohan Abstract Model reference adaptive
More informationStability of CL System
Stability of CL System Consider an open loop stable system that becomes unstable with large gain: At the point of instability, K( j) G( j) = 1 0dB K( j) G( j) K( j) G( j) K( j) G( j) =± 180 o 180 o Closed
More informationconditions makes precise regulation of angle of attack, angle of sideslip, dynamic pressure, and ight
Abstract Piloting diculties associated with conducting maneuvers in hypersonic ight are caused in part by the nonintuitive nature of the aircraft response and the stringent constraints anticipated on allowable
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 informationIntroduction to Flight Dynamics
Chapter 1 Introduction to Flight Dynamics Flight dynamics deals principally with the response of aerospace vehicles to perturbations in their flight environments and to control inputs. In order to understand
More informationCHAPTER 1. Introduction
CHAPTER 1 Introduction Linear geometric control theory was initiated in the beginning of the 1970 s, see for example, [1, 7]. A good summary of the subject is the book by Wonham [17]. The term geometric
More informationAdaptive Trim and Trajectory Following for a Tilt-Rotor Tricopter Ahmad Ansari, Anna Prach, and Dennis S. Bernstein
7 American Control Conference Sheraton Seattle Hotel May 4 6, 7, Seattle, USA Adaptive Trim and Trajectory Following for a Tilt-Rotor Tricopter Ahmad Ansari, Anna Prach, and Dennis S. Bernstein Abstract
More informationStability and Control Analysis in Twin-Boom Vertical Stabilizer Unmanned Aerial Vehicle (UAV)
International Journal of Scientific and Research Publications, Volume 4, Issue 2, February 2014 1 Stability and Control Analysis in Twin-Boom Vertical Stabilizer Unmanned Aerial Vehicle UAV Lasantha Kurukularachchi*;
More informationExam - TTK 4190 Guidance & Control Eksamen - TTK 4190 Fartøysstyring
Page 1 of 6 Norges teknisk- naturvitenskapelige universitet Institutt for teknisk kybernetikk Faglig kontakt / contact person: Navn: Morten Pedersen, Universitetslektor Tlf.: 41602135 Exam - TTK 4190 Guidance
More informationProblem 1: Ship Path-Following Control System (35%)
Problem 1: Ship Path-Following Control System (35%) Consider the kinematic equations: Figure 1: NTNU s research vessel, R/V Gunnerus, and Nomoto model: T ṙ + r = Kδ (1) with T = 22.0 s and K = 0.1 s 1.
More informationFundamentals of Airplane Flight Mechanics
David G. Hull Fundamentals of Airplane Flight Mechanics With 125 Figures and 25 Tables y Springer Introduction to Airplane Flight Mechanics 1 1.1 Airframe Anatomy 2 1.2 Engine Anatomy 5 1.3 Equations of
More informationWorst-case Simulation With the GTM Design Model
Worst-case Simulation With the GTM Design Model Peter Seiler, Gary Balas, and Andrew Packard peter.j.seiler@gmail.com, balas@musyn.com September 29, 29 Overview We applied worst-case simulation analysis
More informationDistributed Coordination and Control Experiments on a Multi UAV Testbed. Ellis T. King
Distributed Coordination and Control Experiments on a Multi UAV Testbed by Ellis T. King Bachelor of Engineering The State University of Buffalo, 22 Submitted to the Department of Aeronautics and Astronautics
More informationFlight Controller Design for an Autonomous MAV
Flight Controller Design for an Autonomous MAV Dissertation Submitted in partial fulfillment of the requirements for the Master of Technology Program by Gopinadh Sirigineedi 03301012 Under the guidance
More informationFault-Tolerant Flight Control Using One Aerodynamic Control Surface
Fault-Tolerant Flight Control Using One Aerodynamic Control Surface Raghu Venkataraman and Peter Seiler University of Minnesota, Minneapolis, Minnesota 55455 This paper considers a statically stable unmanned
More informationRobot Control Basics CS 685
Robot Control Basics CS 685 Control basics Use some concepts from control theory to understand and learn how to control robots Control Theory general field studies control and understanding of behavior
More informationFormally Analyzing Adaptive Flight Control
Formally Analyzing Adaptive Flight Control Ashish Tiwari SRI International 333 Ravenswood Ave Menlo Park, CA 94025 Supported in part by NASA IRAC NRA grant number: NNX08AB95A Ashish Tiwari Symbolic Verification
More informationAdaptive Linear Quadratic Gaussian Optimal Control Modification for Flutter Suppression of Adaptive Wing
Adaptive Linear Quadratic Gaussian Optimal Control Modification for Flutter Suppression of Adaptive Wing Nhan T. Nguyen NASA Ames Research Center, Moffett Field, CA 9435 Sean Swei NASA Ames Research Center,
More informationLONGITUDINAL STABILITY AUGMENTATION DESIGN WITH TWO DEGREE OF FREEDOM CONTROL STRUCTURE AND HANDLING QUALITIES REQUIREMENTS
LONGITUDINAL STABILITY AUGMENTATION DESIGN WITH TWO DEGREE OF FREEDOM CONTROL STRUCTURE AND HANDLING QUALITIES REQUIREMENTS Francisco J. Triveno Vargas, Fernando J. O. Moreira, Pedro Paglione *EMBRAER,
More informationA Method for Compensation of Interactions Between Second-Order Actuators and Control Allocators
A Method for Compensation of Interactions Between Second-Order Actuators and Control Allocators Michael W. Oppenheimer, Member David B. Doman, Member Control Design and Analysis Branch 10 Eighth St., Bldg.
More informationFlight Control: Theory and Practice
Flight Control: Theory and Practice Oct 30 th, 2017 SFTE Workshop Integrity «Service «Excellence Ben Dickinson, PhD Air Force Research Laboratory Munitions Directorate Eglin AFB, FL 32542 B. Dickinson
More informationÜbersetzungshilfe / Translation aid (English) To be returned at the end of the exam!
Prüfung Regelungstechnik I (Control Systems I) Prof. Dr. Lino Guzzella 5. 2. 2 Übersetzungshilfe / Translation aid (English) To be returned at the end of the exam! Do not mark up this translation aid -
More informationAdaptive Output Feedback Control of the NASA GTM Model with Unknown Nonminimum-Phase Zeros
AIAA Guidance, Navigation, and Control Conference 8 - August, Portland, Oregon AIAA -64 Adaptive Output Feedback Control of the NASA GTM Model with Unknown Nonminimum-Phase Zeros Anthony M. D Amato, E.
More informationReturn Difference Function and Closed-Loop Roots Single-Input/Single-Output Control Systems
Spectral Properties of Linear- Quadratic Regulators Robert Stengel Optimal Control and Estimation MAE 546 Princeton University, 2018! Stability margins of single-input/singleoutput (SISO) systems! Characterizations
More informationRobustness Study for Longitudinal and Lateral Dynamics of RLV with Adaptive Backstepping Controller
Robustness Study for Longitudinal and Lateral Dynamics of RLV with Adaptive Backstepping Controller Anoop P R Department of Electrical and Electronics engineering, TKM college of Engineering,Kollam, India
More informationANALYSIS OF AUTOPILOT SYSTEM BASED ON BANK ANGLE OF SMALL UAV
ANALYSIS OF AUTOPILOT SYSTEM BASED ON BANK ANGLE OF SMALL UAV MAY SAN HLAING, ZAW MIN NAING, 3 MAUNG MAUNG LATT, 4 HLA MYO TUN,4 Department of Electronic Engineering, Mandalay Technological University,
More informationClassify a transfer function to see which order or ramp it can follow and with which expected error.
Dr. J. Tani, Prof. Dr. E. Frazzoli 5-059-00 Control Systems I (Autumn 208) Exercise Set 0 Topic: Specifications for Feedback Systems Discussion: 30.. 208 Learning objectives: The student can grizzi@ethz.ch,
More informationLecture AC-1. Aircraft Dynamics. Copy right 2003 by Jon at h an H ow
Lecture AC-1 Aircraft Dynamics Copy right 23 by Jon at h an H ow 1 Spring 23 16.61 AC 1 2 Aircraft Dynamics First note that it is possible to develop a very good approximation of a key motion of an aircraft
More informationDesign of a Missile Autopilot using Adaptive Nonlinear Dynamic Inversion
2005 American Control Conference June 8-10,2005. Portland, OR, USA WeA11.1 Design of a Missile Autopilot using Adaptive Nonlinear Dynamic Inversion Rick Hindman, Ph.D. Raytheon Missile Systems Tucson,
More informationControl Design for a Non-Minimum Phase Hypersonic Vehicle Model
Control Design for a Non-Minimum Phase Hypersonic Vehicle Model Thomas McKenna A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science University of Washington
More informationA Blade Element Approach to Modeling Aerodynamic Flight of an Insect-scale Robot
A Blade Element Approach to Modeling Aerodynamic Flight of an Insect-scale Robot Taylor S. Clawson, Sawyer B. Fuller Robert J. Wood, Silvia Ferrari American Control Conference Seattle, WA May 25, 2016
More informationPRINCIPLES OF FLIGHT
1 Considering a positive cambered aerofoil, the pitching moment when Cl=0 is: A infinite B positive (nose-up). C negative (nose-down). D equal to zero. 2 The angle between the aeroplane longitudinal axis
More informationTopic # /31 Feedback Control Systems
Topic #12 16.30/31 Feedback Control Systems State-Space Systems Full-state Feedback Control How do we change location of state-space eigenvalues/poles? Or, if we can change the pole locations where do
More informationLinear Flight Control Techniques for Unmanned Aerial Vehicles
Chapter 1 Linear Flight Control Techniques for Unmanned Aerial Vehicles Jonathan P. How, Emilio Frazzoli, and Girish Chowdhary August 2, 2012 1 Abstract This chapter presents an overview of linear flight
More informationMECH 6091 Flight Control Systems Final Course Project
MECH 6091 Flight Control Systems Final Course Project F-16 Autopilot Design Lizeth Buendia Rodrigo Lezama Daniel Delgado December 16, 2011 1 AGENDA Theoretical Background F-16 Model and Linearization Controller
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 informationarxiv: v1 [math.oc] 11 Aug 2015
Robust H Loop-Shaping Differential Thrust Control Methodology for Lateral/Directional Stability of an Aircraft with a Damaged Vertical Stabilizer arxiv:1508.02487v1 [math.oc] 11 Aug 2015 Long Lu and Kamran
More informationCALIFORNIA INSTITUTE OF TECHNOLOGY
CALIFORNIA INSIUE OF ECHNOLOGY Control and Dynaical Systes Course Project CDS 270 Instructor: Eugene Lavretsky, eugene.lavretsky@boeing.co Sring 2007 Project Outline: his roject consists of two flight
More informationLocalizer Hold Autopilot
Localizer Hold Autopilot Prepared by A.Kaviyarasu Assistant Professor Department of Aerospace Engineering Madras Institute Of Technology Chromepet, Chennai Localizer hold autopilot is one of the important
More informationWind Turbine Control
Wind Turbine Control W. E. Leithead University of Strathclyde, Glasgow Supergen Student Workshop 1 Outline 1. Introduction 2. Control Basics 3. General Control Objectives 4. Constant Speed Pitch Regulated
More informationLecture 9. Introduction to Kalman Filtering. Linear Quadratic Gaussian Control (LQG) G. Hovland 2004
MER42 Advanced Control Lecture 9 Introduction to Kalman Filtering Linear Quadratic Gaussian Control (LQG) G. Hovland 24 Announcement No tutorials on hursday mornings 8-9am I will be present in all practical
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 informationNonlinear Reconfiguration for Asymmetric Failures in a Six Degree-of-Freedom F-16
1 Nonlinear Reconfiguration for Asymmetric Failures in a Six Degree-of-Freedom F-16 Suba Thomas Harry G. Kwatny Bor-Chin Chang sthomas@drexel.edu hkwatny@coe.drexel.edu bchang@coe.drexel.edu Department
More informationAdaptive Control of an Aircraft with Uncertain Nonminimum-Phase Dynamics
1 American Control Conference Palmer House Hilton July 1-3, 1. Chicago, IL, USA Adaptive Control of an Aircraft with Uncertain Nonminimum-Phase Dynamics Ahmad Ansari and Dennis S. Bernstein Abstract This
More informationAutonomous Helicopter Landing A Nonlinear Output Regulation Perspective
Autonomous Helicopter Landing A Nonlinear Output Regulation Perspective Andrea Serrani Department of Electrical and Computer Engineering Collaborative Center for Control Sciences The Ohio State University
More informationMultivariable MRAC with State Feedback for Output Tracking
29 American Control Conference Hyatt Regency Riverfront, St. Louis, MO, USA June 1-12, 29 WeA18.5 Multivariable MRAC with State Feedback for Output Tracking Jiaxing Guo, Yu Liu and Gang Tao Department
More informationTime Delay Margin Analysis for Adaptive Flight Control Laws
Time Delay Margin Analysis for Adaptive Flight Control Laws A THESIS SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY Andrei Dorobantu IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
More informationTopic # Feedback Control. State-Space Systems Closed-loop control using estimators and regulators. Dynamics output feedback
Topic #17 16.31 Feedback Control State-Space Systems Closed-loop control using estimators and regulators. Dynamics output feedback Back to reality Copyright 21 by Jonathan How. All Rights reserved 1 Fall
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 informationTopic # Feedback Control Systems
Topic #1 16.31 Feedback Control Systems Motivation Basic Linear System Response Fall 2007 16.31 1 1 16.31: Introduction r(t) e(t) d(t) y(t) G c (s) G(s) u(t) Goal: Design a controller G c (s) so that the
More informationSystems Analysis and Control
Systems Analysis and Control Matthew M. Peet Arizona State University Lecture 21: Stability Margins and Closing the Loop Overview In this Lecture, you will learn: Closing the Loop Effect on Bode Plot Effect
More information