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Model-based Nonlinear Control of Aeroengines

This book aims to develop systematic design methodologies to model-based nonlinear control of aeroengines, focusing on (1) modelling;

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Model-Based Tracking Control of Nonlinear Systems

Model-Based Control of Nonlinear Systems presents model-based control techniques for nonlinear, constrained systems. It covers constructive;

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Model-Based Tracking Control of Nonlinear Systems

Model-Based Control of Nonlinear Systems presents model-based control techniques for nonlinear, constrained systems. It covers constructive;

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Nonlinear Industrial Control Systems

Nonlinear Industrial Control Systems presents a range of mostly optimisation-based methods for severely nonlinear systems; it discusses;

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Stabilization of Nonlinear Systems Using Receding-horizon Control Schemes

While conceptually elegant, the generic formulations of nonlinear model predictive control are not ready to use for the stabilization;

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Model Predictive Control

Although industrial processes are inherently nonlinear, many contributions for controller design for those plants are based on the;

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Nonlinear Control for Blood Glucose Regulation of Diabetic Patients: An LMI Approach

Nonlinear Control for Blood Glucose Regulation of Diabetic Patients: An LMI-Based Approach exposes readers to the various existing;

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Economic Model Predictive Control

This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear;

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Virtual Equivalent System Approach for Stability Analysis of Model-based Control Systems

. In closing, the VES concept and methodology are used to assess the stability of multiple model adaptive control (MMAC) systems and T-S model-based;

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Virtual Equivalent System Approach for Stability Analysis of Model based Control

. In closing, the VES concept and methodology are used to assess the stability of multiple model adaptive control (MMAC) systems and T-S model-based;

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Model Based Fuzzy Control

objective of model based fuzzy control is to use the full range of linear and nonlinear design and analysis methods to design such fuzzy controllers;

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Explicit Nonlinear Model Predictive Control

Nonlinear Model Predictive Control (NMPC) has become the accepted methodology to solve complex control problems related to process;

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Nonlinear Model Predictive Control

Over the past few years significant progress has been achieved in the field of nonlinear model predictive control (NMPC), also referred to;

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Distributed Cooperative Control and Communication for Multi agent Systems

rigid body systems. The model-based and data-driven control method are employed to design the (optimal) cooperative control protocol. The;

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Distributed Cooperative Control and Communication for Multi agent Systems

rigid body systems. The model-based and data-driven control method are employed to design the (optimal) cooperative control protocol. The;

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Operator-Based Nonlinear Control Systems Design And Applicat

stability result, a critical factor in any nonlinear control system's stability and performance. Operator-Based Nonlinear Control Systems: Design;

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Computer-aided Nonlinear Control System Design

design for highly nonlinear systems. Computer-aided Nonlinear Control System Design provides such an approach based on the use of describing;

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Process Operational Safety and Cybersecurity

: machine-learning-based modeling of nonlinear dynamical systems for model predictive control; a framework for detection and resilient control;

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Nonlinear Pinning Control of Complex Dynamical Networks

inverse optimal control approach is employed. For both cases, model-based is considered in Chapter 3 and Chapter 5; then, Chapter 4 and Chapter 6;

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Sliding Mode Control (SMC)

Sliding mode control was first introduced in the 1950s. It is a nonlinear control technique with many unique properties. In this book;

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Model-Based Control Of Networked Systems

This monograph introduces a class of networked control systems (NCS) called model-based networked control systems (MB-NCS) and presents;

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Analysis and Synthesis of Fuzzy Control Systems

lack of tools for systematic stability analysis and controller design. To address this problem, many model-based fuzzy control approaches have;

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Analysis and Synthesis of Fuzzy Control Systems

lack of tools for systematic stability analysis and controller design. To address this problem, many model-based fuzzy control approaches have;

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Control Oriented Modelling & Identificat

-based model reduction techniques; integrated modelling and parameter estimation; identification for robust control of complex systems; subspace;

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Introduction To Population Pharmacokinet

data requirements, model building and evaluation, and quality control aspects Provides examples of nonlinear modeling concepts and estimation;

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Advanced and Optimization Based Sliding Mode Control

based event-triggered sliding mode control schemes, including schemes in combination with model predictive control, and presents adaptive;

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Neural Network Control of Nonlinear Discrete-Time Systems

Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities;

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