Self-adaptive robot control using fuzzy logic

This paper presents a new methodology to achieve real time self tuning and self learning in fuzzy controllers, with application to motion control of a trailer for reaching an aiming point and obstacle-avoidance. The advantage of this approach is that it only requires qualitative information about the plant to be controlled, in terms of the monotony presented by the output with respect to the control signal and the delay of the plant. Also, starting with a non-optimum controller, the system is able to self-adapt its behaviour in order to reduce the error. Thus, it is capable of controlling highly non-linear systems, in a pseudo-optimum way, even when these are time variable, for example, the dynamic of the robot or trailer change (i.e.: different mass, different environments, different dynamics of the system). Control is achieved by means of two auxiliary systems: the first one is responsible for adapting the consequences of the main controller to minimize the error arising at the plant output, while the second auxiliary system compiles real input/output data obtained from the plant. The methodology has been successfully applied to a real robot with different dynamics and in different environments, showing its ability to tune its +behaviour