Prescribed Performance Neural Control of Strict-Feedback Systems via Disturbance Observers

الملخص EN

This paper provides a disturbance observer-based prescribed performance control method for uncertain strict-feedback systems.

To guarantee that the tracking error meets a design prescribed performance boundary (PPB) condition, an improved prescribed performance function is introduced.

And radial basis function neural networks (RBFNNs) are used to approximate nonlinear functions, while second-order filters are employed to eliminate the “explosion-complexity” problem inherent in the existing method.

Meanwhile, disturbance observers are constructed to estimate the compounded disturbance which includes time-varying disturbances and network construction errors.

The stability of the whole closed-loop system is guaranteed via Lyapunov theory.

Finally, comparative simulation results confirm that the proposed control method can achieve better tracking performance.