AML Talk

Noise rejection, handling the difficulties coming from the mathematical representation of the system under investigation and alleviation of structural or unstructural uncertainties constitute prime challenges that are frequently encountered in the practice of systems and control engineering. Designing a controller has primarily the aim of achieving the tracking precision as well as a degree of robustness against the difficulties stated. From this point of view, variable structure systems theory offer well formulated solutions to such ill-posed problems containing uncertainty and imprecision. In this talk, a simple controller structure is discussed. The architecture is known as Adaptive Linear Element (ADALINE) in the framework of neural computing, and can be seen in many architectures of computational intelligence. As imposed by the algorithm, the parameters of the controller evolve dynamically in time such that a sliding motion is obtained. The algorithm discussed drives the error on the output of the controller towards zero learning error level, and the state tracking error vector of the plant is driven towards the origin of the phase space simultaneously. The algorithm has been simulated for the control of a 2-DOF SCARA manipulator, and a good performance under the presence of dynamic complexity, observation noise and nonzero initial conditions has shown.