Neural Network-Based State Estimation of Nonlinear Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Neural Network-Based State Estimation of Nonlinear Systems write by Heidar A. Talebi. This book was released on 2009-12-04. Neural Network-Based State Estimation of Nonlinear Systems available in PDF, EPUB and Kindle. "Neural Network-Based State Estimation of Nonlinear Systems" presents efficient, easy to implement neural network schemes for state estimation, system identification, and fault detection and Isolation with mathematical proof of stability, experimental evaluation, and Robustness against unmolded dynamics, external disturbances, and measurement noises.
Neural Network-Based State Estimation of Nonlinear Systems
Neural Network-Based State Estimation of Nonlinear Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Neural Network-Based State Estimation of Nonlinear Systems write by Heidar A. Talebi. This book was released on 2009-12-14. Neural Network-Based State Estimation of Nonlinear Systems available in PDF, EPUB and Kindle. "Neural Network-Based State Estimation of Nonlinear Systems" presents efficient, easy to implement neural network schemes for state estimation, system identification, and fault detection and Isolation with mathematical proof of stability, experimental evaluation, and Robustness against unmolded dynamics, external disturbances, and measurement noises.
Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems
Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems write by Kasra Esfandiari. This book was released on 2021-06-18. Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems available in PDF, EPUB and Kindle. The focus of this book is the application of artificial neural networks in uncertain dynamical systems. It explains how to use neural networks in concert with adaptive techniques for system identification, state estimation, and control problems. The authors begin with a brief historical overview of adaptive control, followed by a review of mathematical preliminaries. In the subsequent chapters, they present several neural network-based control schemes. Each chapter starts with a concise introduction to the problem under study, and a neural network-based control strategy is designed for the simplest case scenario. After these designs are discussed, different practical limitations (i.e., saturation constraints and unavailability of all system states) are gradually added, and other control schemes are developed based on the primary scenario. Through these exercises, the authors present structures that not only provide mathematical tools for navigating control problems, but also supply solutions that are pertinent to real-life systems.
Differential Neural Networks for Robust Nonlinear Control
Differential Neural Networks for Robust Nonlinear Control - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Differential Neural Networks for Robust Nonlinear Control write by Alexander S. Poznyak. This book was released on 2001. Differential Neural Networks for Robust Nonlinear Control available in PDF, EPUB and Kindle. This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical, etc.). Contents: Theoretical Study: Neural Networks Structures; Nonlinear System Identification: Differential Learning; Sliding Mode Identification: Algebraic Learning; Neural State Estimation; Passivation via Neuro Control; Neuro Trajectory Tracking; Neurocontrol Applications: Neural Control for Chaos; Neuro Control for Robot Manipulators; Identification of Chemical Processes; Neuro Control for Distillation Column; General Conclusions and Future Work; Appendices: Some Useful Mathematical Facts; Elements of Qualitative Theory of ODE; Locally Optimal Control and Optimization. Readership: Graduate students, researchers, academics/lecturers and industrialists in neural networks.
State Estimation and Stabilization of Nonlinear Systems
State Estimation and Stabilization of Nonlinear Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook State Estimation and Stabilization of Nonlinear Systems write by Abdellatif Ben Makhlouf. This book was released on 2023-11-06. State Estimation and Stabilization of Nonlinear Systems available in PDF, EPUB and Kindle. 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).
