Novel Density Based State Estimation Methods in Nonlinear Model Predictive Control
Title | Novel Density Based State Estimation Methods in Nonlinear Model Predictive Control PDF eBook |
Author | Sridhar Ungarala |
Publisher | |
Pages | |
Release | 2005 |
Genre | Chemical engineering |
ISBN |
Optimization-based Tuning of Nonlinear Model Predictive Control with State Estimation
Title | Optimization-based Tuning of Nonlinear Model Predictive Control with State Estimation PDF eBook |
Author | E. Ali |
Publisher | |
Pages | 34 |
Release | 1993 |
Genre | |
ISBN |
Data-based Techniques to Improve State Estimation in Model Predictive Control
Title | Data-based Techniques to Improve State Estimation in Model Predictive Control PDF eBook |
Author | Murali R. Rajamani |
Publisher | |
Pages | 264 |
Release | 2007 |
Genre | |
ISBN |
Nonlinear Model Predictive Control with Particle Filter for State Estimation
Title | Nonlinear Model Predictive Control with Particle Filter for State Estimation PDF eBook |
Author | Satyendra Kumar Botchu |
Publisher | |
Pages | 242 |
Release | 2006 |
Genre | Observers (Control theory) |
ISBN |
Deterministic Sampling for Nonlinear Dynamic State Estimation
Title | Deterministic Sampling for Nonlinear Dynamic State Estimation PDF eBook |
Author | Gilitschenski, Igor |
Publisher | KIT Scientific Publishing |
Pages | 198 |
Release | 2016-04-19 |
Genre | Electronic computers. Computer science |
ISBN | 3731504731 |
The goal of this work is improving existing and suggesting novel filtering algorithms for nonlinear dynamic state estimation. Nonlinearity is considered in two ways: First, propagation is improved by proposing novel methods for approximating continuous probability distributions by discrete distributions defined on the same continuous domain. Second, nonlinear underlying domains are considered by proposing novel filters that inherently take the underlying geometry of these domains into account.
State Estimation and Stabilization of Nonlinear Systems
Title | State Estimation and Stabilization of Nonlinear Systems PDF eBook |
Author | Abdellatif Ben Makhlouf |
Publisher | Springer Nature |
Pages | 439 |
Release | 2023-11-06 |
Genre | Technology & Engineering |
ISBN | 3031379705 |
This book presents the separation principle which is also known as the principle of separation of estimation and control and states that, under certain assumptions, the problem of designing an optimal feedback controller for a stochastic system can be solved by designing an optimal observer for the system's state, which feeds into an optimal deterministic controller for the system. Thus, the problem may be divided into two halves, which simplifies its design. In the context of deterministic linear systems, the first instance of this principle is that if a stable observer and stable state feedback are built for a linear time-invariant system (LTI system hereafter), then the combined observer and feedback are stable. The separation principle does not true for nonlinear systems in general. Another instance of the separation principle occurs in the context of linear stochastic systems, namely that an optimum state feedback controller intended to minimize a quadratic cost is optimal for the stochastic control problem with output measurements. The ideal solution consists of a Kalman filter and a linear-quadratic regulator when both process and observation noise are Gaussian. The term for this is linear-quadratic-Gaussian control. More generally, given acceptable conditions and when the noise is a martingale (with potential leaps), a separation principle, also known as the separation principle in stochastic control, applies when the noise is a martingale (with possible jumps).
Advances in State Estimation, Diagnosis and Control of Complex Systems
Title | Advances in State Estimation, Diagnosis and Control of Complex Systems PDF eBook |
Author | Ye Wang |
Publisher | |
Pages | 0 |
Release | 2021 |
Genre | |
ISBN | 9783030524418 |
This book presents theoretical and practical findings on the state estimation, diagnosis and control of complex systems, especially in the mathematical form of descriptor systems. The research is fully motivated by real-world applications (i.e., Barcelona's water distribution network), which require control systems capable of taking into account their specific features and the limits of operations in the presence of uncertainties stemming from modeling errors and component malfunctions. Accordingly, the book first introduces a complete set-based framework for explicitly describing the effects of uncertainties in the descriptor systems discussed. In turn, this set-based framework is used for state estimation and diagnosis. The book also presents a number of application results on economic model predictive control from actual water distribution networks and smart grids. Moreover, the book introduces a fault-tolerant control strategy based on virtual actuators and sensors for such systems in the descriptor form. .