Intelligent Autonomous Control of Spacecraft with Multiple Constraints

Description

This book explores the intelligent autonomous control problems for spacecraft with multiple constraints, such as pointing/path constraints, linear/angular velocity constraints, performance constraints, etc. It provides  an almost self-contained presentation of dynamics modeling, controller design and analysis, as well as simulation studies. The book aims to offer a valuable guide for researchers and aerospace engineers to address the theoretical and technical difficulties in different applications, ranging from spacecraft attitude reorientation and tracking to spacecraft proximity operations, and is mainly intended for technical and engineering staff engaged in spacecraft dyanmics and control areas. Preface Acknowledgements Contents Acronyms 1 Introduction 1.1 Review of Spacecraft Motion Planning 1.1.1 Geometric Method 1.1.2 Artificial Potential Function Method 1.1.3 Discretized Method 1.1.4 Randomized Planning Method 1.1.5 Optimization-Based Method 1.1.6 Artificial Intelligence-Based Method 1.2 Review of Spacecraft Attitude and Position Control 1.2.1 Adaptive Control of Spacecraft 1.2.2 Anti-Disturbance Control of Spacecraft 1.2.3 Fault-Tolerant Control of Spacecraft 1.2.4 State-Constrained Control of Spacecraft 1.2.5 Intelligent Control of Spacecraft 1.3 Contents of the Book References 2 Dynamics Modeling and Mathematical Preliminaries 2.1 Introduction 2.2 Notations 2.3 Coordinate Frames 2.4 Mathematical Models of Spacecraft Dynamics 2.4.1 Spacecraft Attitude Dynamics 2.4.2 Spacecraft Relative Position Dynamics 2.4.3 Spacecraft Relative Position-Attitude Coupled Dynamics 2.4.4 Dual-Quaternion-Based Spacecraft Relative Motion Dynamics 2.5 Lyapunov Stability Theory References 3 Data-Driven Adaptive Control for Spacecraft Constrained Reorientation 3.1 Introduction 3.2 Problem Statement 3.2.1 Attitude Constraints 3.2.2 Angular Velocity Constraints 3.2.3 Problem Statement and Challenges 3.3 I&I Adaptive Attitude Control 3.3.1 Regressor Reconfiguration 3.3.2 I&I Adaptive Controller Design 3.4 Data-Driven I&I Adaptive Control 3.4.1 Filtered System Dynamics 3.4.2 Data-Driven Adaptive Extension 3.5 Numerical Simulations 3.5.1 Performance Validation 3.5.2 Comparison Results 3.5.3 Robustness Tests 3.6 Hardware-in-Loop Experiments 3.7 Summary References 4 Learning-Based Fault-Tolerant Control for Spacecraft Constrained Reorientation Maneuvers 4.1 Introduction 4.2 Adaptive FTC for Spacecraft Constrained Reorientation 4.2.1 Problem Formulation 4.2.2 Adaptive FTC Under Attitude Constraints 4.2.3 Adaptive FTC Under Attitude and Angular Velocity Constraints 4.2.4 Numerical Simulations 4.3 Learning-Based Optimal FTC for Spacecraft Constrained Reorientation 4.3.1 Problem Formulation 4.3.2 Constrained Optimal FTC Design 4.3.3 Single-Critic NN Design and Stability Analysis 4.3.4 Numerical Simulations 4.4 Summary References 5 Intelligent Fault Diagnosis and Fault-Tolerant Control of Spacecraft 5.1 Introduction 5.2 Preliminaries 5.3 Disturbance Observation Scheme 5.4 Fault Diagnosis Scheme 5.4.1 Fault Diagnosis Using Addaptive Estimator 5.4.2 Fault Diagnosis Using Neural Network 5.5 Fault-Tolerant Control 5.6 Numerical Simulation 5.6.1 Disturbances Model 5.6.2 Simulation Conditions 5.6.3 Simulation of Disturbance Observation Scheme 5.6.4 Simulation of Fault Diagnosis Scheme 5.6.5 Simulation of Fault-Tolerant Control Scheme 5.7 Summary References 6 Reinforcement Learning-Based Dynamic Control Allocation for Spacecraft Attitude Stabilization 6.1 Introduction 6.2 Problem Formulation 6.3 Dynamic Control Allocation Scheme 6.3.1 Cost Function 6.3.2 Optimal Manipulation Law Based on Reinforcement Learning 6.3.3 Parameters Solving Based on Neural Network 6.4 Simulation 6.4.1 Simulation of Singularity Problem 6.4.2 Simulation of Dynamic Control Allocation 6.5 Summary References 7 Learning-Based Adaptive Optimal Event-Triggered Control for Spacecraft Formation Flying 7.1 Introduction 7.2 Problem Formulation 7.3 Event-Based Adaptive Optimal Control 7.3.1 Continuous Near Optimal Tracking Control Law 7.3.2 Event-Triggered Mechanism 7.3.3 Stability Analysis 7.3.4 Zeno-Free Analysis 7.4 Numerical Simulations 7.5 Summary References 8 Adaptive Prescribed Performance Pose Control of Spacecraft Under Motion Constraints 8.1 Introduction 8.2 Problem Formation 8.2.1 Relative Position Tracking 8.2.2 Boresight Pointing Adjustment 8.3 Problem Solution 8.3.1 Prescribed Performance 8.3.2 Non-CE Adaptive Pose Control 8.4 Numerical Simulations 8.4.1 Nominal Simulation Campaign 8.4.2 Practical Simulation Campaign 8.4.3 Monte Carlo Simulation Campaign 8.5 Summary References 9 I&I Adaptive Pose Control of Spacecraft Under Kinematic and Dynamic Constraints 9.1 Introduction 9.2 Problem Formulation 9.2.1 Relative Position Tracking 9.2.2 Boresight Pointing Adjustment 9.2.3 Challenges 9.3 Adaptive Controller Design 9.3.1 I&I Adaptive Position Controller 9.3.2 I&I Adaptive Attitude Controller 9.3.3 Discussion 9.4 Numerical Simulations 9.4.1 Baseline Simulation Configuration 9.4.2 Ideal Simulation Scenario 9.4.3 Practical Simulation Scenario 9.5 Summary References 10 Composite Learning Pose Control of Spacecraft with Guaranteed Parameter Convergence 10.1 Introduction 10.2 Preliminaries 10.2.1 Gradient Descent Estimator 10.2.2 Dynamic Regressor Extension and Mixing 10.3 Composite Learning Pose Control 10.3.1 Filtered System Dynamics 10.3.2 Traditional Composite Adaptive Law 10.3.3 Composite Learning Law 10.4 Numerical Simulations 10.4.1 Ideal Simulation Campaign 10.4.2 Practical Simulation Campaign 10.5 Summary References 11 Reinforcement Learning-Based Pose Control of Spacecraft Under Motion Constraints 11.1 Introduction 11.2 Problem Formulation 11.2.1 Motion Constraints 11.2.2 Control Objective 11.3 Learning-Based Pose Control 11.3.1 Reward Function Design 11.3.2 Optimal Control Solution Analysis 11.3.3 Online Learning Control Algorithm 11.3.4 Initial Control Policy 11.4 Numerical Simulations 11.4.1 Point to Point Maneuvers Without Constraints 11.4.2 Docking to the Target with Constraints 11.4.3 Monte-Carlo Simulations 11.5 Summary References Appendix Conclusion A.1 General Conclusion A.2 Future Work