ADAPTIVE CRITIC DESIGN NEUROCONTROLLERS FOR NONLINEAR LARGE SCALE SYSTEMS AND ADVANCED NETWORK MANAGEMENT

Missouri University Of Science And Technology, Rolla MO

Investigators

Abstract

0080764 Wunsch Nonlinear non-stationary large-scale systems such as electric power networks, telecommunication networks, financial and transportation systems are difficult to control and manage. Mathematical models of such systems are typically derived based on linear techniques, and wide margins of safety ensure stable operation. Advanced identification and control schemes are necessary in order to improve the throughput-versus-reliability tradeoff of such systems. Preliminary work by these PIs has shown that advanced, value-based control methods can substantially improve the ability of individual turbogenerators to tolerate unexpected system disturbances. This project will extend that work to distributed networks of power generators and power grids in general. The underlying technology is an advanced form of learning-based Approximate Dynamic Programming or "Reinforcement Learning" which outputs measures of value for individual variables, or dynamic "shadow prices," rather than the conventional measures of global strategic utility. Success in this area may also have benefits for other domains where distributed control and distributed intelligence are needed.