The task of monitoring the execution of a software-based controller in order to detect, classify, and recover from discrepancies between the actual effects of control actions and the effects predicted by a model, is the topic of this thesis. Model-based execution monitoring is proposed as a technique for increasing the safety and optimality of operation of large and complex industrial process controllers, and of controllers operating in complex and unpredictable environments (such as unmanned aerial vehicles).
In this thesis we study various aspects of model-based execution monitoring, including the following:
The relation between previous approaches to execution monitoring in Control Theory, Artificial Intelligence and Computer Science is studied and a common conceptual framework for design and analysis is proposed.
An existing execution monitoring paradigm, ontological control, is generalized and extended. We also present a prototype implementation of ontological control with a first set of experimental results where the prototype is applied to an actual industrial process control system: The ABB STRESSOMETER cold mill flatness control system.
A second execution monitoring paradigm, stability-based execution monitoring, is introduced, inspired by the vast amount of work on the "stability" notion in Control Theory and Computer Science.
Finally, the two paradigms are applied in two different frameworks. First, in the "hybrid automata" framework, which is a state-of-the-art formal modeling framework for hybrid (that is, discrete+continuous) systems, and secondly, in the logical framework of GOLOG and the Situation Calculus.