Fuzzy tuned nonlinear rate controller for manipulators

This paper addresses the problem of controlling a class of nonlinear dynamic systems, including robots, represented by autonomous nonlinear models without functional input derivatives. Industrial and space robots are controlled with PID or PD controllers which are difficult to tune for aperiodic response over a wide range of spatial motion, since the controller gains are functions of changing plant parameters such as effective inertia. A controller which can achieve fast robot performance despite these shortcomings, and with high pay-loads, is a disclosed four stage variable-structure nonlinear rate controller (NRC). This work expands the NRC application with a fuzzy logic tuner resulting in the system´s robust performance with respect to variable loads and command input. The fuzzy tuner consists of nine rules which are logical functions of the system error and its derivative