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The focal point of the effort is on the control of systems with an incompletely known process. When fuzzy dynamic programming is applied to control systems with incompletely known or changing dynamics, it is considerably simpler than minimizing the sensitivity function, and yields a globaly good closed-loop control. It also furnishes a link between dynamic programming and Lyapunov stability theory. Another aspect of the work on fuzzy dynamic programming is aimed at developing computer algorithm for obtaining the guaranteed cost function (or guaranteed cost matrix for linear systems), and also to apply it to learning and adaptive systems. Another area investigated is the inverse problem of guaranteed cost control: 'When is a controller for a system with unknown parameters a guaranteed cost controller.' Necessary and sufficient condition for a controller being a guaranteed cost controller is obtained for a certain class of linear systems. (Author)