Informatica

An adaptive control scheme for mechanical manipulators is proposed. The control loop essentially consists of a network for learning the robot's inverse dynamics and on-line generating the control signal. Some simulation results are provided to evaluate the design. A supervisor is used to improve the performances of the system during the adaptation transients. The supervisor exerts two supervisory actions. The first one consists basically of updating the free-design adaptive controller parameters so that the value of a quadratic loss function is maintained sufficiently small. Such a function involves past tracking errors and their predictions both on appropriate time horizons of low performances during the adaptation transients. The supervisor exerts two supervisory actions. The second supervisory action consists basically of a on-line adjustment of the sampling period within an interval centered in a nominal value of the sampling period. The sampling period is selected so that the transient of the tracking error is improved according to the simple intuitive rule of using a sampling rate faster as the tracking error changes faster.

The objective of expert systems is the use of Artificial Intelligence tools so as to solve problems within specific prefixed applications. Even when such systems are widely applied in diverse applications, as manufacturing or control systems, until now, there is an important gap in the development of a theory being applicable to a description of the involved problems in a unified way. This paper is an attempt in supplying a simple formal description of expert systems together with an application to a robot manipulator case.