Filter-based neural control for switched uncertain nonlinear systems with incomplete measurements

The problem which is concerned with filter-based neural control for switched uncertain nonlinear systems with incomplete measurement is studied in this paper. Filters are used to estimate unknown states, while neural networks approximate unknown functions. Data loss, saturation and other problems often occur during data transmission. An appropriate control law is designed by a backstepping method to solve this problem. Through using the average dwell time, it is proved that the switching system tends to be stable in a certain time. The analysis of probabilistic stability is also carried out to ensure that the system can achieve uniformly ultimately boundedness. Finally, the effectiveness of the previous design is proved by simulation.