ADAPTIVE VERSUS FUZZY CONTROL OF UNCERTAIN MECHANICAL SYSTEMS

Abstract

The motivation of this work is formed by the biological behavior of a receptor cell (sensory system). It is modeled as a spring-mass-damper oscillator with a spatial disturbance signal acting on the frame and an inner active element that generates a force acting on the mass. Both the system parameters and the excitation signal are supposed to be unknown. The goal is to achieve a predefined movement of the mass, such as tracking a set point trajectory or stabilization. Thus, a controller is required to act on the system using the control force as input in such a way that the desired behavior is generated. This is done by means of high-gain-stabilization. Like its biological paradigm, the receptor is in a permanent state of adaption. This means that recurring disturbances, such as wind acting on the vibrissa, are damped in order to achieve λ-stabilization. To achieve this control goal and at the same time deal with unknown systems, adaptive controllers are introduced. These adaptive control strategies are compared with an adaptive fuzzy approach.