Reachable Sets of Dynamic Systems: Uncertainty, Sensitivity, and Complex Dynamics introduces differential inclusions, providing an overview as well as multiple examples of its interdisciplinary applications. The design of dynamic systems of any type is an important issue as is the influence of uncertainty in model parameters and model sensitivity. The possibility of calculating the reachable sets may be a powerful additional tool in such tasks. This book can help graduate students, researchers, and engineers working in the field of computer simulation and model building, in the calculation of reachable sets of dynamic models. Introduces methodologies and approaches to the modeling and simulation of dynamic systems Presents uncertainty treatment and model sensitivity are described, and interdisciplinary examples Explores applications of differential inclusions in modeling and simulation

This brief presents a suite of computationally efficient methods for bounding trajectories of dynamical systems with multi-dimensional intervals, or ‘boxes’. It explains the importance of bounding trajectories for evaluating the robustness of systems in the face of parametric uncertainty, and for verification or control synthesis problems with respect to safety and reachability properties. The methods presented make use of: interval analysis; monotonicity theory; contraction theory; and data-driven techniques that sample trajectories. The methods are implemented in an accompanying open-source Toolbox for Interval Reachability Analysis. This brief provides a tutorial description of each method, focusing on the requirements and trade-offs relevant to the user, requiring only basic background on dynamical systems. The second part of the brief describes applications of interval reachability analysis. This makes the brief of interest to a wide range of academic researchers, graduate students, and practising engineers in the field of control and verification.

Determination of the set of all possible states, which a system can attain, plays an important role in safety for critical application. Prior knowledge of this set for the complete run-time provides critical information about how a system may evolve, providing accurate information of all the states, which could violate constraints. The knowledge of these states, helps in estimating control input, which can control the system, such that, these states are eliminated from the reachable set. Computation of reachable set of a dynamic system for a set of initial conditions can be easily performed, provided the analytical solution of the system for all initial conditions can be obtained. However, obtaining analytical solutions for nonlinear systems is a non-trivial task. Therefore, numerical methods are constructed, to obtain approximate solutions for these systems. Owing to the recent advancements in computational technology, it is now possible to tackle nonlinear systems using numerical methods. The reduction in computation al errors and the increase in the rate of computation have enhanced the quality of results obtained from discrete approximations of continuous systems. The iterative property of these discrete approximations can be implemented in the form of algorithms. These algorithms, in turn, compute precise numerical solutions of systems for which analytical solutions are otherwise difficult to obtain. The primary objective of this thesis is to formulate and construct algorithms to compute reachable sets for linear systems and extending these algorithms to compute reachable sets for linear systems with perturbations. The secondary objective is to apply and verify the algorithms on a real-world application, previously studied in the open literature, and to discuss the results obtained. The computation of a reachable set is carried out in MATLAB® and the computed reachable sets for representative mathematical models of dynamic systems are presented and different ideas of reachable states are discussed.

State Estimation for Dynamic Systems presents the state of the art in this field and discusses a new method of state estimation. The method makes it possible to obtain optimal two-sided ellipsoidal bounds for reachable sets of linear and nonlinear control systems with discrete and continuous time. The practical stability of dynamic systems subjected to disturbances can be analyzed, and two-sided estimates in optimal control and differential games can be obtained. The method described in the book also permits guaranteed state estimation (filtering) for dynamic systems in the presence of external disturbances and observation errors. Numerical algorithms for state estimation and optimal control, as well as a number of applications and examples, are presented. The book will be an excellent reference for researchers and engineers working in applied mathematics, control theory, and system analysis. It will also appeal to pure and applied mathematicians, control engineers, and computer programmers.

This book is about dynamical systems that are "hybrid" in the sense that they contain both continuous and discrete state variables. Recently there has been increased research interest in the study of the interaction between discrete and continuous dynamics. The present volume provides a first attempt in book form to bring together concepts and methods dealing with hybrid systems from various areas, and to look at these from a unified perspective. The authors have chosen a mode of exposition that is largely based on illustrative examples rather than on the abstract theorem-proof format because the systematic study of hybrid systems is still in its infancy. The examples are taken from many different application areas, ranging from power converters to communication protocols and from chaos to mathematical finance. Subjects covered include the following: definition of hybrid systems; description formats; existence and uniqueness of solutions; special subclasses (variable-structure systems, complementarity systems); reachability and verification; stability and stabilizability; control design methods. The book will be of interest to scientists from a wide range of disciplines including: computer science, control theory, dynamical system theory, systems modeling and simulation, and operations research.

This thesis proposes an algorithmic controller synthesis based on the computation of probabilistic reachable sets for stochastic hybrid systems. Hybrid systems consist in general of a composition of discrete and continuous valued dynamics, and are able to capture a wide range of physical phenomena. The stochasticity is considered in form of normally distributed initial continuous states and normally distributed disturbances, resulting in stochastic hybrid systems. The reachable sets describe all states, which are reachable by a system for a given initialization of the system state, inputs, disturbances, and time horizon. For stochastic hybrid systems, these sets are probabilistic, since the system state and disturbance are random variables. This thesis introduces probabilistic reachable sets with a predefined confidence, which are used in an optimization based procedure for the determination of stabilizing control inputs. Besides the stabilizing property, the controlled dynamics also observes input constraints, as well as, so-called chance constraints for the continuous state. The main contribution of this thesis is the formulation of an algorithmic control procedure for each considerd type of stochastic hybrid systems, where different discrete dynamics are considered. First, a control procedure for a deterministic system with bounded disturbances is introduced, and thereafter a probabilistic distribution of the system state and the disturbance is assumed. The formulation of probabilistic reachable sets with a predefined confidence is subsequently used in a control procedure for a stochastic hybrid system, in which the switch of the continuous dynamics is externally induced. Finally, the control procedure based on reachable set computation is extended to a type of stochastic hybrid systems with autonomously switching of the continuous dynamics.

This book constitutes the refereed proceedings of the 9th International Workshop on Hybrid Systems: Computation and Control, HSCC 2006, held in Santa Barbara, CA, USA in March 2006. The 39 revised full papers presented together with the abstracts of 3 invited talks were carefully reviewed and selected from 79 submissions. Among the topics addressed are tools for analysis and verification, control and optimization, modeling, engineering applications, and emerging directions in programming language support and implementation. The papers focus on modeling, analysis, and implementation of dynamic and reactive systems involving both discrete and continuous behaviors.