Control System of the Aerial Vehicle with Kalman Filter
using the Neural Network for Adjustment of its Parameters
Control system of the aerial vehicle with the Kalman filter using the neural network for adjustment of its parameters. The article considers a proposed control system with the Kalman filter that adapts itself to environmental noise conditions. The Kalman filter may be used in the control system under condition of remained control quality that is a problem to be solved. To solve this matter, it is required not only to adapt characteristics of the filter in compliance with actual environmental conditions, but make appropriate corrections within the law of control as well. The algorithm of adaptation is realized with use of the neural network, which instructs the control system by measured coordinates of the aerial vehicle. Under low level of noise as a component of an error of control the Kalman filter pass band is extended resulting in a more accurate processing of input effects. Under a high level of noise as a component of control error the Kalman filter pass band is narrowed to effectively suppress jamming and reduce errors in measurements. Simulation of operation of the control system with the Kalman filter considered in this article has demonstrated the effectiveness of the proposed technical concepts.