The mathematical theory of control, essentially developed during the last decades, is used for solving many problems of practical importance. The efficiency of its applications has increased in connection with the refine ment of computer techniques and the corresponding mathematical soft ware. Real-time control schemes that include computer-realized blocks are, for example, attracting ever more attention. The theory of control provides abstract models of controlled systems and the processes realized in them. This theory investigates these models, proposes methods for solv ing the corresponding problems and indicates ways to construct control algorithms and the methods of their computer realization. The usual scheme of control is the following: There is an object F whose state at every time instant t is described by a phase variable x. The object is subjected to a control action u. This action is generated by a control device U. The object is also affected by a disturbance v generated by the environment. The information on the state of the system is supplied to the generator U by the informational variable y. The mathematical character of the variables x, u, v and yare determined by the nature of the system.
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"...the book under review is very valuable in the area and the authors ought to be commended for their nice work."
--SIAM Review
"This is an interesting book..."
--Zentralblatt Math
One of the basic aspirations of contemporary research in differential games is to produce a theory that would combine theoretical depth with the ability to ensure effective calculation and simulation of the solutions. Accumulating research in differential games, this book gives an account of how to design state and output feedback control strategies for a broad class of differential games (such as, pursuit, evasion, tracking, encounter, energy - minimum cost, etc), or more generally, for games with a "positional cost functional that satisfies the evolutionary optimality principle". This is done through specially developed techniques of open-loop stochastic control and some convexification procedures of nonlinear analysis with model examples worked out.
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