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Nuclear plants of the 21st century will employ higher levels of automation and fault tolerance to increase availability, reduce accident risk, and lower operating costs. Key developments in control algorithms, fault diagnostics, fault tolerance, and communication in a distributed system are needed to implement the fully automated plant. Equally challenging will be integrating developments in separate information and control fields into a cohesive system, which collectively achieves the overall goals of improved performance, safety, reliability, maintainability, and cost-effectiveness. Under the Nuclear Energy Research Initiative (NERI), the U. S. Department of Energy is sponsoring a project to address some of the technical issues involved in meeting the long-range goal of 21st century reactor control systems. This project, ''A New Paradigm for Automated Development Of Highly Reliable Control Architectures For Future Nuclear Plants,'' involves researchers from Oak Ridge National Laboratory, University of Tennessee, and North Carolina State University. This paper documents a research effort to develop methods for automated generation of control systems that can be traced directly to the design requirements. Our final goal is to allow the designer to specify only high-level requirements and stress factors that the control system must survive (e.g. a list of transients, or a requirement to withstand a single failure.) To this end, the ''control engine'' automatically selects and validates control algorithms and parameters that are optimized to the current state of the plant, and that have been tested under the prescribed stress factors. The control engine then automatically generates the control software from validated algorithms. Examples of stress factors that the control system must ''survive'' are: transient events (e.g., set-point changes, or expected occurrences such a load rejection,) and postulated component failures. These stress factors are specified by the designer and become a database of prescribed transients and component failures. The candidate control systems are tested, and their parameters optimized, for each of these stresses. Examples of high-level requirements are: response time less than xx seconds, or overshoot less than xx% ... etc. In mathematical terms, these types of requirements are defined as ''constraints,'' and there are standard mathematical methods to minimize an objective function subject to constraints. Since, in principle, any control design that satisfies all the above constraints is acceptable, the designer must also select an objective function that describes the ''goodness'' of the control design. Examples of objective functions are: minimize the number or amount of control motions, minimize an energy balance... etc

NERI PROJECT 99-119. TASK 1. ADVANCED CONTROL TOOLS AND METHODS. FINAL REPORT