Robust Asymptotic Control for Intelligent Unknown Mechatronic Systems

In order to fulfil ever increasing performances in industry, man made systems are becoming very complex and are requiring to solve the following paradox. Whereas with growing complexity the uncertainty on the system and its environment increases, at the same time much better preciseness in dynamical behaviour is demanded for system operation. As the environment is not rigidly fixed, it becomes mandatory to adapt intelligently to its change. Mathematically, this implies to satisfy conditions for robust asymptotic stability. Classical mechanistic trajectory based approach is no longer valid and a new approach based on more global functional manifold is proposed here which ends up on explicit criteria guaranteeing the property. Two cases are occurring. If system uncertainty ball is smaller than affordable robustness ball, the criterion can be worked out within equivalence class which is completely defined from system equations. If system uncertainty ball is larger, a more sophisticated but still explicit method is indicated which adjusts system representation parameters so that they converge toward reliable system dynamical approximation so that robust asymptotic stability property still holds. As such, system dynamics are securely managed at execution level, preparing the system for including next level of decision and freeing operator action to concentrate on supervision