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Foreword; Preface; Acknowledgements; Contents; 1 Systems Theory and Stability Concepts; 1.1 Outline; 1.2 Characteristics of the Dynamics of Nonlinear Systems; 1.3 Computation of Isoclines; 1.4 Stability Features of Dynamical Systems; 1.4.1 The Phase Diagram; 1.4.2 Stability Analysis of Nonlinear Systems; 1.4.3 Local Stability Properties of a Nonlinear Model; 1.5 Phase Diagrams and Equilibria; 1.5.1 Phase Diagrams for Linear Dynamical Systems; 1.5.2 Multiple Equilibria for Nonlinear Dynamical Systems; 1.5.3 Limit Cycles; 1.6 Bifurcations; 1.6.1 Bifurcations of Fixed Points
1.6.2 Saddle-Node Bifurcations of Fixed Points in a One-Dimensional System1.6.3 Pitchfork Bifurcation of Fixed Points; 1.6.4 The Hopf Bifurcation; 1.7 Chaos in Dynamical Systems; 1.7.1 Chaotic Dynamics; 1.7.2 Examples of Chaotic Dynamical Systems; 2 Main Approaches to Nonlinear Control; 2.1 Outline; 2.2 Overview of Main Approaches to Nonlinear Control; 2.3 Control Based on Global Linearization Methods; 2.3.1 Overview of Differential Flatness Theory; 2.3.2 Differential Flatness for Finite Dimensional Systems; 2.4 Control Based on Approximate Linearization Methods
2.4.1 Approximate Linearization Round Temporary Equilibria2.4.2 The Nonlinear H-Infinity Control; 2.4.3 Approximate Linearization with Local Fuzzy Models; 2.5 Control Based on Lyapunov Stability Analysis; 2.5.1 Transformation of Nonlinear Systems into a Canonical Form; 2.5.2 Adaptive Control Law for Nonlinear Systems; 2.5.3 Approximators of System Unknown Dynamics; 2.5.4 Lyapunov Stability Analysis for Dynamical Systems; 3 Main Approaches to Nonlinear Estimation; 3.1 Outline; 3.2 Linear State Observers; 3.3 The Continuous-Time Kalman Filter for Linear Models
3.4 The Discrete-Time Kalman Filter for Linear Systems3.5 The Extended Kalman Filter for Nonlinear Systems; 3.6 Sigma-Point Kalman Filters; 3.7 Particle Filters; 3.7.1 The Particle Approximation of Probability Distributions; 3.7.2 The Prediction Stage; 3.7.3 The Correction Stage; 3.7.4 The Resampling Stage; 3.7.5 Approaches to the Implementation of Resampling; 3.8 The Derivative-Free Nonlinear Kalman Filter; 3.8.1 Conditions for solving the estimation problem in single-input nonlinear systems; 3.8.2 State Estimation with the Derivative-Free Nonlinear Kalman Filter
3.8.3 Derivative-Free Kalman Filtering for multivariable Nonlinear Systems3.9 Distributed Extended Kalman Filtering; 3.9.1 Calculation of Local Extended Kalman Filter Estimations; 3.9.2 Extended Information Filtering for State Estimates Fusion; 3.10 Distributed Sigma-Point Kalman Filtering; 3.10.1 Calculation of Local Unscented Kalman Filter Estimations; 3.10.2 Unscented Information Filtering for State Estimates Fusion; 3.11 Distributed Particle Filter; 3.11.1 Distributed Particle Filtering for State Estimation Fusion; 3.11.2 Fusion of the Local Probability Density Functions
1.6.2 Saddle-Node Bifurcations of Fixed Points in a One-Dimensional System1.6.3 Pitchfork Bifurcation of Fixed Points; 1.6.4 The Hopf Bifurcation; 1.7 Chaos in Dynamical Systems; 1.7.1 Chaotic Dynamics; 1.7.2 Examples of Chaotic Dynamical Systems; 2 Main Approaches to Nonlinear Control; 2.1 Outline; 2.2 Overview of Main Approaches to Nonlinear Control; 2.3 Control Based on Global Linearization Methods; 2.3.1 Overview of Differential Flatness Theory; 2.3.2 Differential Flatness for Finite Dimensional Systems; 2.4 Control Based on Approximate Linearization Methods
2.4.1 Approximate Linearization Round Temporary Equilibria2.4.2 The Nonlinear H-Infinity Control; 2.4.3 Approximate Linearization with Local Fuzzy Models; 2.5 Control Based on Lyapunov Stability Analysis; 2.5.1 Transformation of Nonlinear Systems into a Canonical Form; 2.5.2 Adaptive Control Law for Nonlinear Systems; 2.5.3 Approximators of System Unknown Dynamics; 2.5.4 Lyapunov Stability Analysis for Dynamical Systems; 3 Main Approaches to Nonlinear Estimation; 3.1 Outline; 3.2 Linear State Observers; 3.3 The Continuous-Time Kalman Filter for Linear Models
3.4 The Discrete-Time Kalman Filter for Linear Systems3.5 The Extended Kalman Filter for Nonlinear Systems; 3.6 Sigma-Point Kalman Filters; 3.7 Particle Filters; 3.7.1 The Particle Approximation of Probability Distributions; 3.7.2 The Prediction Stage; 3.7.3 The Correction Stage; 3.7.4 The Resampling Stage; 3.7.5 Approaches to the Implementation of Resampling; 3.8 The Derivative-Free Nonlinear Kalman Filter; 3.8.1 Conditions for solving the estimation problem in single-input nonlinear systems; 3.8.2 State Estimation with the Derivative-Free Nonlinear Kalman Filter
3.8.3 Derivative-Free Kalman Filtering for multivariable Nonlinear Systems3.9 Distributed Extended Kalman Filtering; 3.9.1 Calculation of Local Extended Kalman Filter Estimations; 3.9.2 Extended Information Filtering for State Estimates Fusion; 3.10 Distributed Sigma-Point Kalman Filtering; 3.10.1 Calculation of Local Unscented Kalman Filter Estimations; 3.10.2 Unscented Information Filtering for State Estimates Fusion; 3.11 Distributed Particle Filter; 3.11.1 Distributed Particle Filtering for State Estimation Fusion; 3.11.2 Fusion of the Local Probability Density Functions