INTELLIGENT ROBUST CONTROL METHODOLOGIES FOR MECHATRONIC AND ROBOTIC SYSTEMS

Abstract

Traditional model-based control methodologies require accurate system modeling and system parameters for controller design. In practical applications, it might be one of the challenging issues that the systems are too complex to be modeled or the parameters are difficult to be accurately identified. As a result, the design of robust controller becomes a promising trend for next generation of the mechatronic and robotic system. This thesis presents several intelligent robust control methodologies, which can achieve excellent performance for motion systems with inaccurate system parameters or without the requirements of the model information as prior knowledge. Moreover, the real-time experiments also illustrate that the requirements of precision motion control can be satisfied using the proposed methods. It is pertinent to note that applications of the proposed methodologies can be further deployed advantageously to other appropriate mechatronic and robotic systems with similar difficulties.