Neural network observer-based predefined time tracking control for non-strict feedback nonlinear system: A fault-tolerant performance function approach

Predefined time performance control has been widely used in practical applications due to its ability in improving the system’s transient performance. However, imprecise feedback information from faulty sensors will make this control strategy ineffective and seriously compromise the system performance. This paper concentrates on addressing predefined time tracking control for non-strict feedback nonlinear systems while considering sensor faults. First, a fault-tolerant performance function combined with the settling time regulator is constructed to handle the output constraints in the presence of system faults. Second, in spite of the output feedback information being imprecise, the designed adaptive neural network observer can still obtain the real state information. Third, the designed control scheme can efficiently counteract the negative influences of unknown nonlinearities and faulty sensors, which makes the system achieve asymptotic tracking with predefined time performance. Finally, the acquired control algorithm’s applicability is demonstrated through numerical simulations.