Robust adaptive neural network control for strict-feedback nonlinear systems via small-gain approaches

A novel robust adaptive neural network control (RANNC) is proposed for a class of strict-feedback nonlinear systems with both unknown system nonlinearities and unknown virtual control gain nonlinearities. The synthesis of RANNC is developed by use of the input-tostate stability (ISS), the backstepping technique, and generalized small gain approach. The key feature of RANNC is that the order of its dynamic compensator is only identical to the order n of controlled system, such that it can reduce the computation load and makes particularly suitable for parallel processing. In addition, the possible controller singularity problem can be removed elegantly. Finally, simulation results are presented to validate the effectiveness of the RANNC algorithm.