Study of Solutions to Differential Inclusions by the "Pipe Method"

A pipe of a differential inclusion is a set-valued map associating with each time t a subset P(t) of states which contains a trajectory of the differential inclusion for any initial state x_o belonging to P(O). As in the Liapunov method, knowledge of a pipe provides information on the behavior of the trajectory. In this paper, the characterization of pipes and non-smooth analysis of set-valued maps are used to describe several classes of pipes. This research was conducted within the framework of the Dynamics of Macrosystems study in the System and Decision Sciences Program

Contingent Isaacs Equations of a Differential Game

The purpose of this paper is to characterize classical and lower semicontinuous solutions to the Hamilton-Jacobi-Isaacs partial differential equations associated with a differential game and, in particular, characterize closed subsets the indicators of which are solutions to these equations. For doing so, the classical concept of derivative is replaced by contingent epi-derivative, which can apply to any function. The use of indicator of subsets which are solutions of either one of the contingent Isaacs equation allows to characterize areas of the playability set in which some behavior (playability, winability, etc.) of the players can be achieved

Smooth and Heavy Solutions to Control Problems

We introduce the concept of viability domain of a set-valued map, which we study and use for providing the existence of smooth solutions to differential inclusions. We then define and study the concept of heavy viable trajectories of a controlled system with feedbacks. Viable trajectories are trajectories satisfying at each instant given constraints on the state. The controls regulating viable trajectories evolve according a set-valued feedback map. Heavy viable trajectories are the ones which are associated to the controls in the feedback map whose velocity has at each instant the minimal norm. We construct the differential equation governing the evolution of the controls associated to heavy viable trajectories and we state their existence

Slow and Heavy Viable Trajectories of Controlled Problems. Part 1. Smooth Viability Domains

We define slow and heavy viable trajectories of differential inclusions and controlled problems. Slow trajectories minimize at each time the norm of the velocity of the state (or the control) and heavy trajectories the norm of the acceleration of the state (or the velocity of the control). Macrosystems arising in social and economic sciences or biological sciences seem to exhibit heavy trajectories. We make explicit the differential equations providing slow and heavy trajectories when the viability domain is smooth

Qualitative Equations: The Confluence Case

This paper deals with a domain of Artificial Intelligence known under the name of "qualitative simulation" or "qualitative physics", to which special volumes of "Artificial Intelligence" (1984) and or "IEEE Transactions on Systems, Man and Cybernetics" (1987) have been devoted. It defines the concept of "qualitative frame" of a set, which allows to introduce strict, large and dual confluence frames of a finite dimensional vector-space. After providing a rigorous definition of standard, lower and upper qualitative solutions in terms of confluences introduced by De Kleer, it provides a duality criterion for the existence of a strict standard solution to both linear and non linear equations. It also furnishes a dual characterization of the existence or upper and lower qualitative solutions to a linear equation. These theorems are extended to the case or "inclusions", where single-valued maps are replaced by set-valued maps. This may be useful for dealing with qualitative properties of maps which are not precisely known, or which are defined by a set of properties, a requirement which is at the heart of qualitative simulation

Qualitative Differential Games: A Viability Approach

The author defines the playability property of a qualitative differential game, defined by a system of differential equations controlled by two controls. The rules of the game are defined by constraints on the states of each player depending on the state of the other player. This paper characterizes the playability property by a regulation map which associates with any playable state a set of playable controls. In other words, the players can implement playable solutions to the differential game by playing for each state a static game on the controls of the regulation subset. One must extract among theses playable controls the set of discriminating and pure controls of one of the players. Such controls are defined through an adequate "contingent" Hamilton-Jacobi-Isaacs equation. Sufficient conditions implying the existence of continuous or minimal playable, discriminating and pure feedbacks are provided

Hazy Differential Inclusions

This paper is devoted to differential inclusions the right-hand sides of which are hazy subsets, which are fuzzy subsets whose membership functions are cost functions taking their values in [0,infinity] instead of [0,1]. By doing so, the concept of uncertainty involved in differential inclusions becomes more precise, by allowing the velocities not only to depend in a multivalued way upon the state of the system, but also in a fuzzy way. The viability theorems are adapted to hazy differential inclusions and to sets of state constraints which are either usual or hazy. The existence of a largest closed hazy viability domain contained in a given closed hazy subset is also provided

A Viability Approach to Liapunov's Second Method

The purpose of this note is to extend Liapunov's second method to the case of differential inclusions, when viability requirements are made and when the Liapunov functions are continuous

Evolution of Prices under the Inertia Principle

The decentralized evolution of allocations of resources among consumers described by their change functions is characterized by a regulation rule associating with each allocation the subset of prices regulating them. Sufficient budgetary conditions analog to the Walras law for the viability of such evolutions are then provided. Next, the issue of finding feedback controls is tackled: conditions under which slow evolutions are given. More to the point, dynamical feedback controls obeying the inertia principle are provided: prices are changed only when the viability of the evolution mechanism is at stakes. We then derive the differential equations governing the heavy evolution of prices: for a given bound on inflation rates, the price evolves with minimal velocity