Control theory is a quickly developing science. The complexity in design and operation of controlled systems increases tremendously. This requires a computational system theory for synthesis and real-time implementation that enables the integration of model-based control with system identification for multivariable and large scale systems. Three important trends are the development of large-scale systems, model-based methods, and intelligent control.
- Complex large-scale systems will consist of numerous interacting networked subsystems, where hybrid and stochastic aspects will be addressed, as well as coordination within and across all levels of distributed and multi-level control frameworks.
- Model-based systems will be highly dependent on the on-line availability of goal-oriented, dynamic models that are adaptive to changing circumstances in both the system and its environment. The complexity of the models and the model-based systems is increasing rapidly.
- Future control systems will be able to effectively learn from experience, actively acquire knowledge about the process controlled, optimize their performance and realize a high-degree of autonomous behavior where necessary.
Furthermore, as control becomes an integral part in the design of new systems, there is an enormous need for the development of new tools, methods and theories.