Advanced Trajectory Optimization, Guidance and Control Strategies for Aerospace Vehicles: Methods and Applications (Springer Aerospace Technology)

Preface
Acknowledgements
Contents
About the Authors
Part I Advanced Trajectory Optimization Methods
1 Review of Advanced Trajectory Optimization Methods
	1.1 Introduction
	1.2 Mathematical Formulation of the Problem
		1.2.1 Continuous Dynamical Systems
		1.2.2 Variable/Path Constraints
		1.2.3 Mission Objectives
		1.2.4 Overall Formulation
		1.2.5 Numerical Solution Approach
	1.3 Optimization Algorithms
		1.3.1 Gradient-Based Methods
		1.3.2 Evolutionary-Based Methods
		1.3.3 Convexification-Based Methods
		1.3.4 Dynamic Programming-Based Methods
	1.4 Multi-objective Spacecraft Trajectory Optimization
		1.4.1 Multi-objective Evolutionary Algorithms
		1.4.2 Multi-objective Transcription Methods
	1.5 Stochastic Spacecraft Trajectory Optimization
		1.5.1 Chance-Constrained Spacecraft Trajectory Optimization
		1.5.2 Chance-Constrained Spacecraft Trajectory Optimization: Stochastic Dynamics
	1.6 Recent Practical Applications of the Optimized Trajectory
		1.6.1 Design of Integrated Spacecraft Guidance and Control Systems
		1.6.2 Design of Spacecraft/Satellite Formation Control Schemes
		1.6.3 Database-Based Online Guidance Strategy
	1.7 Conclusions and Future Development
	References
2 Heurestic Optimization-Based Trajectory Optimization
	2.1 Introduction
	2.2 Biased Particle Swarm Optimization Approach
		2.2.1 Unconstrained Multi-objective Optimal Control Problem
		2.2.2 MOPSO Algorithm
		2.2.3 ε-Bias Selection
		2.2.4 Local Exploration
		2.2.5 Evolution Restart Strategy
		2.2.6 Overall Algorithm Framework
	2.3 Constrained Atmospheric Entry Problem
		2.3.1 System Model
		2.3.2 Entry Phase Constraints
		2.3.3 Objectives
	2.4 Test Results and Analysis
		2.4.1 Test Case Specification
		2.4.2 Performance of Different Methods
		2.4.3 Convergence Analysis for Evolutionary Methods
		2.4.4 Computational Performance of Different Methods
		2.4.5 Impact of the Bias Selection Strategy and Local Exploitation Process
		2.4.6 Impact of the Restart Strategy
	2.5 Conclusion
	References
3 Highly Fidelity Trajectory Optimization
	3.1 Introduction
	3.2 Time-Optimal Reconnaissance Maneuver Optimization Problem
		3.2.1 Model Dynamics
		3.2.2 Flight Constraints and Objective
		3.2.3 Overall Trajectory Optimization Formulation
	3.3 Solution Method
		3.3.1 Radau Pseudospectral Method
		3.3.2 A Pipelined Optimization Strategy
	3.4 Simulation Results
		3.4.1 Simulation Setting
		3.4.2 Optimized Results of Using Different Models
		3.4.3 Results with and Without Mesh Adaptive Process
		3.4.4 Comparative Results and Analysis
		3.4.5 Case Studies with Noise-Perturbed Initial Conditions
	3.5 Conclusions
	References
4 Fast Trajectory Optimization  with Chance Constraints
	4.1 Introduction
	4.2 Atmospheric Entry Optimal Control Problem
		4.2.1 Hypersonic Vehicle Dynamics and Constraints
		4.2.2 Atmospheric Entry Optimal Control Model
	4.3 Nonconvex Chance-Constrained Optimization Approach
		4.3.1 Handling the Probabilistic Constraint
		4.3.2 Deterministic NCCO Model
	4.4 Convex Chance-Constrained Optimization Approach
		4.4.1 Convex Relaxation of Dynamics and Hard Constraints
		4.4.2 Convex Approximation of Control Chance Constraint
		4.4.3 Overall CCCO Model
	4.5 Performance Evaluation
		4.5.1 Parameters and Mission Cases Specification
		4.5.2 NCCO Results and Discussions
		4.5.3 CCCO Results and Discussions
	4.6 Conclusion
	References
5 Fast Generation of Chance-Constrained Flight Trajectory for Unmanned Vehicles
	5.1 Introduction
	5.2 Trajectory Planning Formulation
		5.2.1 Unmanned Vehicle System Equations
		5.2.2 Geometric Constraints
		5.2.3 Relationship Between Geometric Constraints and Vehicle Actual Constraints
		5.2.4 Control Chance Constraints
		5.2.5 Probabilistic Collision Avoidance Constraints
		5.2.6 Objective and Optimization Model
	5.3 Convex-Programming-based Trajectory Planning Approach
		5.3.1 Convexification of System Equations and Constraints
		5.3.2 Convex Trajectory Optimization Model
	5.4 Deterministic Chance-Constrained Trajectory Planning Formulation
		5.4.1 Convex Approximation of Control Chance Constraints
		5.4.2 Convex Approximation of Probabilistic Collision Avoidance Constraints
		5.4.3 Overall Algorithm Framework
	5.5 Numerical Results
		5.5.1 Unmanned Vehicle Trajectory Generation
		5.5.2 Comparative Case Study: Without Chance Constrains
		5.5.3 Chance-Constrained Unmanned Vehicle Trajectory Generation
		5.5.4 Comparative Case Studies: With Control Chance Constrains
		5.5.5 Comparative Case Studies: With Control and Obstacle Chance Constrains
		5.5.6 Sensitivity Analysis
	5.6 Conclusion
	References
Part II Advanced Guidance and Control Methods for Aerospace Vehicles
6 Review of Advanced Guidance  and Control Methods
	6.1 Introduction
		6.1.1 Background
		6.1.2 Motivation
		6.1.3 Organisation of the Article
	6.2 Types of Guidance and Control Systems
		6.2.1 Integrated Guidance and Control System
		6.2.2 Partially Integrated Guidance and Control System
	6.3 Review of Stability Theory-Based G&C Methods
		6.3.1 Design and Applications of Robust G&C Algorithms
		6.3.2 Design and Applications of Stochastic G&C Algorithms
		6.3.3 Potential Issues and Challenges of Stability Theory-Based G&C Algorithms
		6.3.4 Design and Applications of Data-Driven G&C Algorithms
	6.4 Review of Optimisation-Based G&C Methods
		6.4.1 Design and Applications of Dynamic Programming-Based G&C Methods
		6.4.2 Design and Applications of Model Predictive Control-Based G&C Methods
		6.4.3 Challenges of Using Optimisation Theory-Based G&C Methods in Space/Aerospace Applications
	6.5 Review of AI-Based G&C Strategies
		6.5.1 Connection Between AI and Guidance and Control Problems
		6.5.2 Design and Applications of AI-Based G&C Methods
		6.5.3 Potential Issues and Challenges of AI-Based G&C Methods
	6.6 Conclusions and Future Developments
		6.6.1 Concluding Remarks
		6.6.2 Continuing Research
	References
7 Optimization-Based Predictive G&C Method
	7.1 Introduction
	7.2 Missile-Target Nonlinear Model
		7.2.1 2-D Missile Target Engagement
		7.2.2 3-D Missile Target Engagement
	7.3 Receding Horizon Pseudospectral Control
		7.3.1 Discrete Approximation Model
		7.3.2 Moving Horizon Estimation
		7.3.3 Receding Horizon Pseudospectral Control
		7.3.4 NLP Optimality and Approximated KKT Conditions
		7.3.5 Implementation Consideration
	7.4 Simulation Studies
		7.4.1 Parameter Specification
		7.4.2 Interception Results
		7.4.3 Comparative Study
		7.4.4 Effect of Parameter Uncertainty
	7.5 Conclusion
	References
8 Robust Model Predictive Control for Attitude Control Tracking
	8.1 Introduction
		8.1.1 Literature Review
		8.1.2 Motivations and Contributions
		8.1.3 Organization
	8.2 Problem Formulation
		8.2.1 Spacecraft Attitude Dynamics
		8.2.2 Control Problem Objectives
		8.2.3 Assumptions and Preliminaries
	8.3 Design of the TRMPC Algorithm
		8.3.1 Outer-Loop TRMPC Design
		8.3.2 Inner-Loop TRMPC Design
		8.3.3 Overall Algorithm Framework
	8.4 Analysis of Feasibility and Stability
		8.4.1 Recursive Feasibility
		8.4.2 Control Stability
	8.5 Performance Evaluation
		8.5.1 Parameter Assignment
		8.5.2 Experimental Setup
		8.5.3 Tracking Performance Evaluation and Comparative Studies
		8.5.4 Case Studies on Algorithm Parameters
	8.6 Conclusions
	8.7 Appendix: Proof of Theorem 2
	References