ABSTRACT
Most classical and modern control methodologies need to know the mathematical model of the system to be controlled. In some cases the systems are too complex to know their mathematical model with accuracy. In other cases, not all control techniques can be applied. The motivation to use a new control technique arises from the need to use mobile robots in disaster environments, in these environments the sensors of the robot are not always reliable, in addition to the robot being exposed to a large number of internal and external disturbances, the majority of which are not measurable. Therefore an inverse optimal neuronal controller is able to obtain the mathematical model 62implicitly and work under the presence of external disturbances and uncertainties and in turn adapt to the changes suffered by the robot [108].
