Morphing Aerospace Vehicles and Structures

John Valasek, Texas A&M University, USAJohn Valasek is Associate Professor and Director of the Vehicle Systems & Control Laboratory within the Aerospace Engineering Department at Texas A&M University. He has been actively conducting flight mechanics and controls research of Manned and Unmanned Air Vehicles in both Industry and Academia for 25 years. He was previously a Flight Control Engineer for the Northrop Corporation, Aircraft Division. He has published over 100 peer reviewed articles, and is co-inventor on a patent for autonomous air refueling of unmanned air vehicles. His research is currently focused on bridging the gap between traditional computer science topics and aerospace engineering topics, encompassing machine learning and multi-agent systems, intelligent autonomous control, vision based navigation systems, fault tolerant adaptive control, and cockpit systems and displays.?He teaches courses in Atmospheric Flight Mechanics, Digital Flight Control Systems, Vehicle Management Systems, Cockpit Systems & Displays, and Aircraft Design.

• List of Contributors xiii Foreword xvSeries Preface xviiAcknowledgments xix1 Introduction 1John Valasek1.1 Introduction 11.2 The Early Years: Bio-Inspiration 21.3 The Middle Years: Variable Geometry 51.4 The Later Years: A Return to Bio-Inspiration 91.5 Conclusion 10References 10Part I BIO-INSPIRATION2 Wing Morphing in Insects, Birds and Bats: Mechanism and Function 13Graham K. Taylor, Anna C. Carruthers, Tatjana Y. Hubel, and Simon M. Walker2.1 Introduction 132.2 Insects 142.2.1 Wing Structure and Mechanism 152.2.2 Gross Wing Morphing 182.3 Birds 252.3.1 Wing Structure and Mechanism 252.3.2 Gross Wing Morphing 282.3.3 Local Feather Deflections 302.4 Bats 322.4.1 Wing Structure and Mechanism 332.4.2 Gross Wing Morphing 352.5 Conclusion 37Acknowledgements 37References 383 Bio-Inspiration of Morphing for Micro Air Vehicles 41Gregg Abate and Wei Shyy3.1 Micro Air Vehicles 413.2 MAV Design Concepts 433.3 Technical Challenges for MAVs 463.4 Flight Characteristics of MAVs and NAVs 473.5 Bio-Inspired Morphing Concepts for MAVs 483.5.1 Wing Planform 503.5.2 Airfoil Shape 503.5.3 Tail Modulation 503.5.4 CG Shifting 503.5.5 Flapping Modulation 513.6 Outlook for Morphing at the MAV/NAV scale 513.7 Future Challenges 513.8 Conclusion 53References 53Part II CONTROL AND DYNAMICS4 Morphing Unmanned Air Vehicle Intelligent Shape and  Flight Control 57John Valasek, Kenton Kirkpatrick, and Amanda Lampton4.1 Introduction 574.2 A-RLC Architecture Functionality 584.3 Learning Air Vehicle Shape Changes 594.3.1 Overview of Reinforcement Learning 594.3.2 Implementation of Shape Change Learning Agent 624.4 Mathematical Modeling of Morphing Air Vehicle 634.4.1 Aerodynamic Modeling 634.4.2 Constitutive Equations 644.4.3 Model Grid 674.4.4 Dynamical Modeling 684.4.5 Reference Trajectory 714.4.6 Shape Memory Alloy Actuator Dynamics 714.4.7 Control Effectors on Morphing Wing 734.5 Morphing Control Law 734.5.1 Structured Adaptive Model Inversion (SAMI) Control for Attitude Control 734.5.2 Update Laws 764.5.3 Stability Analysis 774.6 Numerical Examples 774.6.1 Purpose and Scope 774.6.2 Example 1: Learning New Major Goals 774.6.3 Example 2: Learning New Intermediate Goals 804.7 Conclusions 84Acknowledgments 84 References 845 Modeling and Simulation of Morphing Wing Aircraft 87Borna Obradovic and Kamesh Subbarao5.1 Introduction 875.1.1 Gull-Wing Aircraft 875.2 Modeling of Aerodynamics with Morphing 885.2.1 Vortex-Lattice Aerodynamics for Morphing 905.2.2 Calculation of Forces and Moments 925.2.3 Effect of Gull-Wing Morphing on Aerodynamics 925.3 Modeling of Flight Dynamics with Morphing 935.3.1 Overview of Standard Approaches 935.3.2 Extended Rigid-Body Dynamics 975.3.3 Modeling of Morphing 1005.4 Actuator Moments and Power 1055.5 Open-Loop Maneuvers and Effects of Morphing 1095.5.1 Longitudinal Maneuvers 1095.5.2 Turn Maneuvers 1145.6 Control of Gull-Wing Aircraft using Morphing 1185.6.1 Power-Optimal Stability Augmentation System using Morphing 1195.7 Conclusion 123Appendix 123References 1246 Flight Dynamics Modeling of Avian-Inspired Aircraft 127Jared Grauer and James Hubbard Jr6.1 Introduction 1276.2 Unique Characteristics of Flapping Flight 1296.2.1 Experimental Research Flight Platform 1296.2.2 Unsteady Aerodynamics 1306.2.3 Configuration-Dependent Mass Distribution 1316.2.4 Nonlinear Flight Motions 1316.3 Vehicle Equations of Motion 1346.3.1 Conventional Models for Aerospace Vehicles 1346.3.2 Multibody Model Configuration 1366.3.3 Kinematics 1386.3.4 Dynamics 1386.4 System Identification 1406.4.1 Coupled Actuator Models 1416.4.2 Tail Aerodynamics 1436.4.3 Wing Aerodynamics 1436.5 Simulation and Feedback Control 1446.6 Conclusion 148References 1487 Flight Dynamics of Morphing Aircraft with Time-Varying Inertias 151Daniel T. Grant, Stephen Sorley, Animesh Chakravarthy, and Rick Lind7.1 Introduction 1517.2 Aircraft 1527.2.1 Design 1527.2.2 Modeling 1547.3 Equations of Motion 1567.3.1 Body-Axis States 1567.3.2 Influence of Time-Varying Inertias 1577.3.3 Nonlinear Equations for Moment 1577.3.4 Linearized Equations for Moment 1597.3.5 Flight Dynamics 1617.4 Time-Varying Poles 1627.4.1 Definition 1627.4.2 Discussion 1647.4.3 Modal Interpretation 1647.5 Flight Dynamics with Time-Varying Morphing 1667.5.1 Morphing 1667.5.2 Model 1667.5.3 Poles 1687.5.4 Modal Interpretation 171References 1748 Optimal Trajectory Control of Morphing Aircraft in Perching Maneuvers 177Adam M. Wickenheiser and Ephrahim Garcia8.1 Introduction 1778.2 Aircraft Description 1798.3 Vehicle Equations of Motion 1818.4 Aerodynamics 1858.5 Trajectory Optimization for Perching 1918.6 Optimization Results 1968.7 Conclusions 202References 202Part III SMART MATERIALS AND STRUCTURES9 Morphing Smart Material Actuator Control Using Reinforcement Learning 207Kenton Kirkpatrick and John Valasek9.1 Introduction to Smart Materials 2079.1.1 Piezoelectrics 2089.1.2 Shape Memory Alloys 2089.1.3 Challenges in Controlling Shape Memory Alloys 2099.2 Introduction to Reinforcement Learning 2109.2.1 The Reinforcement Learning Problem 2109.2.2 Temporal-Difference Methods 2119.2.3 Action Selection 2139.2.4 Function Approximation 2159.3 Smart Material Control as a Reinforcement Learning Problem 2189.3.1 State-Spaces and Action-Spaces for Smart Material Actuators 2189.3.2 Function Approximation Selection 2209.3.3 Exploiting Action-Value Function for Control 2209.4 Example 2219.4.1 Simulation 2229.4.2 Experimentation 2259.5 Conclusion 228References 22910 Incorporation of Shape Memory Alloy Actuators into Morphing Aerostructures 231Justin R. Schick, Darren J. Hartl and Dimitris C. Lagoudas10.1 Introduction to Shape Memory Alloys 23110.1.1 Underlying Mechanisms 23210.1.2 Unique Engineering Effects 23310.1.3 Alternate Shape Memory Alloy Options 23710.2 Aerospace Applications of SMAs 23810.2.1 Fixed-Wing Aircraft 23910.2.2 Rotorcraft 24510.2.3 Spacecraft 24610.3 Characterization of SMA Actuators and Analysis of Actuator Systems 24710.3.1 Experimental Techniques and Considerations 24810.3.2 Established Analysis Tools 25210.4 Conclusion 256References 25611 Hierarchical Control and Planning for Advanced Morphing Systems 261Mrinal Kumar and Suman Chakravorty11.1 Introduction 26111.1.1 Hierarchical Control Philosophy 26211.2 Morphing Dynamics and Performance Maps 26411.2.1 Discretization of Performance Maps via Graphs 26511.2.2 Planning on Morphing Graphs 27011.3 Application to Advanced Morphing Structures 27111.3.1 Morphing Graph Construction 27311.3.2 Introduction to the Kagom´e Truss 27511.3.3 Examples of Morphing with the Kagom´e Truss 27711.4 Conclusion 279References 27912 A Collective Assessment 281John Valasek12.1 Looking Around: State-of-the-Art 28112.1.1 Bio-Inspiration 28112.1.2 Aerodynamics 28112.1.3 Structures 28212.1.4 Automatic Control 28212.2 Looking Ahead: The Way Forward 28212.2.1 Materials 28212.2.2 Propulsion 28312.3 Conclusion 283Index 285