Ellipsoidal Calculus for Estimation and Control

It is well known that the emphasis of mathematical modelling on the basis of available observations is first -to use the data to specify or refine the mathematical model, then - to analyze the model through available or new mathematical tools, and further on - to use this analysis in order to predict or prescribe (control) the future course of the modelled process. This is particularly done by specifying feedback control strategies (policies) that realize the desired goals. An important component of the overall process is to verify the model and its performance over the actual course of events. The given principles are also among the objectives of modern control theory, whether directed at traditional (aerospace, mechanics, regula tion, technology) or relatively new applications (environment, popula tion, finances and economics, biomedical issues, communication, and transport) . Among the specific features of the controlled processes in the mentioned areas are usually their dynamic nature and the uncertainty in their de scription.

Contenu
I. Evolution and Control: The Exact Theory.- 1.1 The System.- 1.2 Attainability and the Solution Tubes.- 1.3 The Evolution Equation.- 1.4 The Problem of Control Synthesis: A Solution Through Set-Valued Techniques.- 1.5 Control Synthesis Through Dynamic Programming Techniques.- 1.6 Uncertain Systems: Attainability Under Uncertainty.- 1.7 Uncertain Systems: The Solvability Tubes.- 1.8 Control Synthesis Under Uncertainty.- 1.9 State Constraints and Viability.- 1.10 Control Synthesis Under State Constraints.- 1.11 State Constrained Uncertain Systems: Viability Under Counteraction.- 1.12 Guaranteed State Estimation: The Bounding Approach.- 1.13 Synopsis.- 1.14 Why Ellipsoids.- II. The Ellipsoidal Calculus.- 2.1 Basic Notions: The Ellipsoids.- 2.2 External Approximations: The Sums Internal Approximations: The Differences.- 2.3 Internal Approximations: The Sums External Approximations: The Differences.- 2.4 Sums and Differences: The Exact Representation.- 2.5 The Selection of Optimal Ellipsoids.- 2.6 Intersections of Ellipsoids.- 2.7 Finite Sums and Integrals: External Approximations.- 2.8 Finite Sums and Integrals: Internal Approximations.- III. Ellipsoidal Dynamics: Evolution and Control Synthesis.- 3.1 Ellipsoidal-Valued Constraints.- 3.2 Attainability Sets and Attainability Tubes: The External and Internal Approximations.- 3.3 Evolution Equations with Ellipsoidal-Valued Solutions.- 3.4 Solvability in Absence of Uncertainty.- 3.5 Solvability Under Uncertainty.- 3.6 Control Synthesis Through Ellipsoidal Techniques.- 3.7 Control Synthesis: Numerical Examples.- 3.8 Ellipsoidal Control Synthesis for Uncertain Systems.- 3.9 Control Synthesis for Uncertain Systems: Numerical Examples.- 3.10 Target Control Synthesis Within Free Time Interval.- IV. Ellipsoidal Dynamics: State Estimation and Viability Problems.- 4.1 Guaranteed State Estimation: A Dynamic Programming Perspective.- 4.2 From Dynamic Programming to Ellipsoidal State Estimates.- 4.3 The State Estimates, Error Bounds, and Error Sets.- 4.4 Attainability Revisited: Viability Through Ellipsoids.- 4.5 The Dynamics of Information Domains: State Estimation as a Tracking Problem.- 4.6 Discontinuous Measurements and the Singular Perturbation Technique.