Optimal Control and Estimation MAE 546 Robert Stengel. !! Optimal Control and Estimation is a graduate course that presents the theory and application of optimization, probabilistic modeling, and stochastic control to dynamic systems. But is there an assignment that is best, or optimal? 25 Minimize an Absolute Criterion •! !Part 2!! The exam is an open-book exam where written material such as the script is allowed, while electronic devices are not allowed.
Optimal estimation and filtering methods are included, and optimal fault estimation, safety-critical, fault-tolerant and reliable control systems. Retrouvez Optimal Control and Estimation et des millions de livres en stock sur Amazon.fr. Typical Problems in Optimal Control and Estimation! By constant state feedback the dynamics of a linear, controllable, timeinvariant system can be assigned in an arbitrary manner. 3!Path Constraints and! 8 Probability and Statistics Least-Squares Estimation for Static Systems 9 Propagation of Uncertainty Kalman Filter in Dynamic Systems [Assignment #4 due] 10 Kalman-Bucy Filter Nonlinear State Estimation 11 Nonlinear State Estimation Adaptive State Estimation [Assignment #5 due] 12 Stochastic Optimal Control Linear-Quadratic-Gaussian Control It also covers optimal control of hybrid systems, switching systems, repetitive and periodic control. The course will be accompanied by weekly exercises with exercise questions and computer exercises using the environment MATLAB. 8 Probability and Statistics Least-Squares Estimation for Static Systems 9 Propagation of Uncertainty Kalman Filter in Dynamic Systems [Assignment #4 due] 10 Kalman-Bucy Filter Nonlinear State Estimation 11 Nonlinear State Estimation Adaptive State Estimation [Assignment #5 due] 12 Stochastic Optimal Control Linear-Quadratic-Gaussian Control !Principles for Optimal Control of Dynamic Systems ! Suitable papers will normally be concerned with model based optimal control methods covering topics such as optimal control in multi-agent systems, optimal nonlinear and robust control, H2 and H∞ design, linear-quadratic control, stochastic control, multi-criteria and multiple-model control. !Part 2!!
We start by discussing discrete time linear systems, their basic stability properties, time varying systems, linearization of nonlinear systems. Optimal estimation and filtering methods are included, and optimal fault estimation, safety-critical, fault-tolerant and reliable control systems. INTRODUCTION 1 1.1 Framework for Optimal Control 1 1.2 Modeling Dynamic Systems 5 1.3 Optimal Control Objectives 9 1.4 Overview of the Book 16 Problems 17 References 18 2. At the end of the course the students shall have full understanding of how to use the linear quadratic regulator (LQR), the Kalman filter, Lyapunov and Riccati Equations, dynamic programming, constrained optimal control, moving horizon estimation (MHE) and model predictive control (MPC).The exam date is August 14, 2015, 10:00am. The focus of the course is state space control in discrete time. CONTENTS 1. Topics in optimization algorithms for control are relevant such as static and dynamic optimization techniques, nonlinear programming, constrained control …
The first two chapters introduce optimal control and review the mathematics of control and estimation. Particular attention is given to modeling dynamic systems, measuring and controlling their behavior, and developing strategies for future courses of action. to achieve a goal •!