Tham khảo tài liệu 'adaptive control 2011 part 16', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 368 Adaptive Control Fig. 15. The norms of weights and output of RBFNof subsysteml Fig. 16. The norms of weights and output of RBFNof subsystem 2 5. Conclusion In this chapter first a novel design ideal has been developed for a general class of nonlinear systems which the controlled plants are a class of non-affine nonlinear implicit function and smooth with respect to control input. The control algorithm bases on some mathematical theories and Lyapunov stability theory. In order to satisfy the smooth condition of these theorems hyperbolic tangent function is adopted instead of sign function. This makes control signal tend smoother and system running easier. Then the proposed scheme is extended to a class of large-scale interconnected nonlinear systems which the subsystems are composed of the above-mentioned class of non-affine nonlinear functions. For two classes of interconnection function two RBFN-based decentralized adaptive control schemes are proposed respectively. Using an on-line approximation approach we have been able to relax the linear in the parameter requirements of traditional nonlinear decentralized adaptive control without considering the dynamic uncertainty as part of the interconnections and disturbances. The theory and simulation results show that the neural network plays an important role in systems. The overall adaptive schemes are proven to Adaptive Control for a Class of Non-affine Nonlinear Systems via Neural Networks 369 guarantee uniform boundedness in the Lyapunov sense. The effectiveness of the proposed control schemes are illustrated through simulations. As desired all signals in systems including control signals are tend to smooth. 6. Acknowledgments This research is supported by the research fund granted by the Natural Science Foundation of Shandong Y2007G06 and the Doctoral Foundation of Qingdao University of Science and Technology. 7. References Lewis . Yesildirek A. Liu K. 1996 . Multilayer neural-net robot controller with .
