This introductory module sets the stage for the course, outlining its objectives and motivations. Students will gain insight into the importance of optimal control and guidance in aerospace applications.
This introductory module sets the stage for the course, outlining its objectives and motivations. Students will gain insight into the importance of optimal control and guidance in aerospace applications.
This module provides an overview of the state-space approach and matrix theory, which are foundational for understanding optimal control systems. Key topics include:
This module reviews various numerical methods that are crucial for solving optimal control problems. Students will learn about:
Understanding these methods is vital for implementing control strategies effectively.
This module introduces static optimization concepts, focusing on the foundational principles and techniques. Key discussions include:
This module continues the exploration of static optimization, delving deeper into techniques and applications. Students will examine:
This module provides a comprehensive review of the calculus of variations, a mathematical tool essential for optimal control theory. Topics include:
Continuing the exploration of calculus of variations, this module focuses on advanced techniques and problem-solving approaches, including:
This module focuses on the optimal control formulation using the calculus of variations. Key aspects include:
This module introduces classical numerical methods used to solve optimal control problems. Students will learn about:
This module delves into the Linear Quadratic Regulator (LQR), a critical method in control theory. Key discussions include:
This module continues the examination of the Linear Quadratic Regulator (LQR), focusing on advanced topics such as:
This module concludes the study of the Linear Quadratic Regulator (LQR), addressing the following aspects:
This module continues with advanced LQR discussions, focusing on:
This module introduces discrete-time optimal control techniques, emphasizing their relevance in modern control systems. Key topics include:
This module provides an overview of flight dynamics, crucial for understanding aircraft behavior. It covers:
Continuing the exploration of flight dynamics, this module addresses advanced topics such as:
This module concludes the study of flight dynamics, focusing on:
This module focuses on linear optimal missile guidance using LQR techniques. Key topics include:
This module introduces SDRE (State Dependent Riccati Equation) and θ-D designs, focusing on their applications in optimal control. Key discussions include:
This module delves into dynamic programming, a critical method for solving complex control problems. Topics covered include:
This module introduces approximate dynamic programming (ADP) and the adaptive critic method. Key discussions include:
This module focuses on transcription methods for solving optimal control problems. Key topics include:
This module discusses model predictive static programming (MPSP) and its applications in optimal guidance of aerospace vehicles. Key discussions include:
This module focuses on MPSP for optimal missile guidance, covering:
This module introduces model predictive spread control (MPSC) and generalized MPSP (G-MPSP) designs, emphasizing their applications in aerospace. Key topics include:
This module provides an introduction to linear quadratic observers and offers an overview of state estimation techniques. Key discussions include:
This module reviews probability theory and random variables, which are integral to understanding stochastic processes in control. Key topics include:
This module focuses on the design of the Kalman filter, a key tool in state estimation. Topics covered include:
This module continues the study of Kalman filter design, emphasizing:
This module concludes the Kalman filter design discussion, covering:
This module explores integrated estimation, guidance, and control techniques, emphasizing their importance in aerospace systems. Topics include:
Continuing the discussion on integrated estimation, guidance, and control, this module examines advanced methods and applications:
This module introduces Linear Quadratic Gaussian (LQG) design, focusing on:
This module focuses on constrained optimal control methods, addressing key topics such as:
This module continues the exploration of constrained optimal control, focusing on:
This module concludes the study of constrained optimal control, emphasizing:
This module introduces optimal control of distributed parameter systems, highlighting:
This module continues the exploration of optimal control for distributed parameter systems, focusing on:
This module provides a summary of key concepts covered throughout the course, emphasizing:
This concluding module provides additional take-home materials summarizing essential concepts and encouraging further study in optimal control, guidance, and estimation. Key points include: