Design and implementation of an adaptive neural-network compensator for control systems

Recently, many studies have been made for intelligent controls using the neural-network (NN). These NN approaches for control strategies are based on the concept of replacing the conventional controller with a new NN controller. However, it is usually difficult and unreliable to replace the factory-installed controller with another controller in the workplace. In this case, it is desirable to install an additional outer control loop around the conventional control system to compensate for the control error of the preinstalled conventional control system. This paper presents an adaptive NN compensator for the outer loop to compensate for the control errors of conventional control systems. The proposed adaptive NN compensator generates a new command signal to the conventional control system using the control error that is the difference between the desired reference input and the actual system response. The proposed NN-compensated control system is adaptable to the environment changes and is more robust than the conventional control systems. Experimental results for a SCARA-type manipulator show that the proposed adaptive NN compensator enables the conventional control system to have precise control performance