List of publications without theses

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult control using conventional techniques. Here, a novel design of an adaptive neuro fuzzy inference strategy (ANFIS) for controlling input displacement of a new adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Since the conventional control strategy is a very challenging task, fuzzy logic based controllers are considered as potential candidates for such an application. Fuzzy based controllers develop a control signal which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS controller, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.

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 lambda-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.