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Aircraft Control and Simulation
Brian L. Stevens,Frank L. Lewis +1 more
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TLDR
Equations of Motion Building the Aircraft Model Basic Analytical and Computational Tools Aircraft Dynamics and Classical Design Techniques Modern Design Techniques Robustness and Multivariable Frequency-Domain Techniques Digital Control Appendices Index.Abstract:
Equations of Motion Building the Aircraft Model Basic Analytical and Computational Tools Aircraft Dynamics and Classical Design Techniques Modern Design Techniques Robustness and Multivariable Frequency-Domain Techniques Digital Control Appendices Index.read more
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BookDOI
Handbook of Marine Craft Hydrodynamics and Motion Control: Fossen/Handbook of Marine Craft Hydrodynamics and Motion Control
TL;DR: In this article, the authors present a survey of the latest tools for analysis and design of advanced guidance, navigation and control systems and present new material on underwater vehicles and surface vessels.
Sigma-point kalman filters for probabilistic inference in dynamic state-space models
TL;DR: This work has consistently shown that there are large performance benefits to be gained by applying Sigma-Point Kalman filters to areas where EKFs have been used as the de facto standard in the past, as well as in new areas where the use of the EKF is impossible.
Journal ArticleDOI
Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem
TL;DR: An online algorithm based on policy iteration for learning the continuous-time optimal control solution with infinite horizon cost for nonlinear systems with known dynamics, which finds in real-time suitable approximations of both the optimal cost and the optimal control policy, while also guaranteeing closed-loop stability.
Journal ArticleDOI
Discrete-Time Nonlinear HJB Solution Using Approximate Dynamic Programming: Convergence Proof
TL;DR: It is shown that HDP converges to the optimal control and the optimal value function that solves the Hamilton-Jacobi-Bellman equation appearing in infinite-horizon discrete-time (DT) nonlinear optimal control.
Proceedings Article
Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data
TL;DR: A new underlying probabilistic model for principal component analysis (PCA) is introduced that shows that if the prior's covariance function constrains the mappings to be linear the model is equivalent to PCA, and is extended by considering less restrictive covariance functions which allow non-linear mappings.