Improved Data-driven Adaptive Control Structure Against Input and Output Saturation

Abstract

This article presents an improved data-driven adaptive control structure to address the problem of input and output saturation in unknown nonlinear systems with multiple inputs and multiple outputs. In the suggested structure, a virtual model of the controlled system is initially built utilizing a multi-layered group method of data handling neural network. The control signal is then applied to this virtual model to predict the output before being applied to the system. If the predicted output is saturated, the control signals are readjusted to prevent saturation and are then applied to the system. By using this proposed structure, the performance of model-free adaptive control against input/output saturation phenomena is improved and the occurrence of saturation is prevented. Based on Lyapunov’s theory, the stability of the suggested structure is proven. The controller has been applied to an interconnected three-tank system and a subway train which results clearly illustrate the advantages of the suggested method over the traditional form of model-free adaptive control design.