Adaptive dynamic surface control for a class of MIMO nonlinear systems with actuator failures

Abstract

In this article, an adaptive dynamic surface control scheme for a class of MIMO nonlinear systems with actuator failures and uncertainties is presented. In the proposed control scheme, the dynamic changes and disturbances induced by actuator failures are detected and isolated by means of radial basis function neural networks, which also compensate system uncertainties that arise from the mismatch between nominal model and real plant. In the presence of unknown actuation functions, the effectiveness of the control scheme is guaranteed by imposing a structural condition on the actuation matrix. Moreover, the singularity problem that arises from the approximation of unknown actuation functions is circumvented, and thus the use parameter projection is avoided. In this work, the nominal plant is transformed into a suitable form via diffeomorphism. Dynamic surface control design technique is used to develop the control laws. The closed-loop signals are proven to be uniformly ultimately bounded through Lyapunov approach, and the output tracking error is shown to be bounded within a residual set which can be made arbitrarily small by appropriately tuning the controller parameters. Finally, the proposed adaptive control scheme effectiveness is verified by simulation of the longitudinal dynamics of a twin otter aircraft undergoing actuator failures.

Keywords:

• adaptive control
• neural networks
• nonlinear control
• fault-tolerant control

Acknowledgements

This work was partially supported by NSF of China under Grant 60874044 and Research Foundation for Key Disciplines of Beijing Municipal Commission of Education under Grant XK100060422.