Robust and Adaptive Control Theories and Applications
Control engineering is a rapidly evolving discipline with a wide range of applications, including but not limited to chemical, electrical, mechanical, and civil systems. However, the mathematical models upon which control theories are based can never precisely describe all the characteristics of any given system. This uncertainty is a direct result of various factors, such as incomplete system knowledge, variable dynamics and parameters, complex physical mechanisms, and external disturbances. The discrepancy between a physical system and its mathematical description is therefore an issue of grave concern for control engineers. Robust and adaptive control theories have emerged as highly efficient tools for dealing with uncertainties, capable of guaranteeing robust and stable system performance under varying operational conditions. This research aims at developing robust and adaptive control strategies, with particular emphasis on mechanical and electrical systems. Possible applications include vibration suppression of automotive systems, automation of industrial processes, force feedback and haptic interfacing.
The accuracy and performance of any control system is greatly dependent upon the mathematical model on which it is based. Thus, there is a strong correlation between the practice of control engineering and system identification. System identification refers to the use of measured data in combination with stochastic or deterministic methods to discern the structure and relevant parameters of a given system. As such, it is an invaluable tool when dealing with systems with uncertain or unknown mathematical models and parameters. This research is concerned primarily with applying system identification theories to complex systems involving nonlinearities and hybrid models.