Learning to be Autonomous: Intelligent Supervisory Control

A brief introduction to the main ideas in Autonomous Control Systems is rst given and certain important issues in modeling, analysis and design are discussed. Control systems with high degree of autonomy should perform well under signi cant uncertainties in the system and environment for extended periods of time, and they must be able to compensate for certain system failures without external intervention. Highly autonomous control systems evolve from conventional control systems by adding intelligent components, and their development requires interdisciplinary research. A working characterization of intelligent controllers is introduced and it is argued that the supervisory controller discussed here, which can learn events, is indeed intelligent. There are problems in Autonomous Control Hybrid control systems are of great importance in the development of autonomous control and they are discussed extensively. An appropriate hybrid system model is rst introduced and it is used to develop a DES model for the hybrid control system. Logical DES theory is then extended to include hybrid systems and a DES supervisory controller is designed. To cope with changing complex systems, learning must be introduced. Symbol/event bindings are discussed and the framework for an intelligent supervisory controller is developed.