This book is an outgrowth of discussions that got started in at least three workshops sponsored by the National Science Foundation (NSF):.A workshop on neurocontrol and aerospace applications held in October 1990, under joint sponsorship from McDonnell Douglas and the NSF programs in Dynamic Systems and Control and Neuroengineering.A workshop on intelligent control held in October 1990, under joint sponsorship from NSF and the Electric Power Research Institute, to scope out plans for a major new joint initiative in intelligent control involving a number of NSF programs.A workshop on neural networks in chemical processing, held at NSF in January-February 1991, sponsored by the NSF program in Chemical Reaction Processes The goal of this book is to provide an authoritative source for two kinds of information: (1) fundamental new designs, at the cutting edge of true intelligent control, as well as opportunities for future research to improve on these designs; (2) important real-world applications, including test problems that constitute a challenge to the entire control community. Included in this book are a series of realistic test problems, worked out through lengthy discussions between NASA, NetJroDyne, NSF, McDonnell Douglas, and Honeywell, which are more than just benchmarks for evaluating intelligent control designs. Anyone who contributes to solving these problems may well be playing a crucial role in making possible the future development of hypersonic vehicles and subsequently the

Supervised learning requires an explicit target paired with each input. When explicit targets are not available, internally generated targets are required. Expectation Driven Learning (EDL) generates internal expectations that provide these targets for any supervised learning method. A system which incorporates EDL must be provided intrinsic measures of value. Expectations are of these measures of value and provide targets for learning after each

The oil and gas industry and the United States government both face tremendous challenges to explore discover, appraise, develop, and exploit vast new hydrocarbon reserves in waters deeper than 6000 feet in the ultra-deepwater of the Gulf of Mexico. Yet these new reserves of hydrocarbons are needed to offset the economically detrimental, long-term decline in production from within the borders of the United States (Figure 1). Figure 1. The Sigsbee salt sheet (white) defines the ultra-deepwater at the boundary of the continental margin and deep basin of the northern Gulf of Mexico. It’s southern terminus is marked by an 800 meter escarpment, and the whole salt sheet is moving downhill to the south at several cm/yr. (from Advanced Resources

The generation of a sequence of Control actions to move a system from an initial state to a final one is an ill-posed problem because the solution is not unique. Soft constraints like the minimization of a cost associated to control actions makes the problem mathematically solvable in the framework of optimal control theory. We present here a method to approximate the solution of the problems of this category based on Heuristic Dynamic Programming proposed by Werbos: Local Dynamic Programming. Its main features are the exploration of a volume around the actual trajectory and the introduction of a set of correcting functions. Its application to the generation of a trajectory whose kinematics is minimum jerk is presented; in this situation, the introduction of a short term temporal credit assignment improves the convergence tackling the lack of controllability in the Plant. A control problem can be stated as the generation of a temporal sequence that would move a certain plant in a desired way. The simplest structure that can