Approximation-Based Nussbaum Gain Adaptive Control of Nonlinear Systems With Periodic Disturbances
针对含周期扰动、死区输出和未知控制方向的非线性系统，结合傅里叶级数和神经网络逼近时变扰动，用Nussbaum函数处理死区和方向问题，基于李雅普诺夫和反步法设计控制器，使跟踪误差收敛到原点附近。
This article considers the Nussbaum gain adaptive control issue for a type of nonlinear systems, in which some sophisticated and challenging problems, such as periodic disturbances, dead zone output, and unknown control direction are addressed. The Fourier series expansion and radial basis function neural network are incorporated into a function approximator to model time-varying-disturbed function with a known period in nonlinear systems. To deal with the problems of the dead zone output and unknown control direction, the Nussbaum-type function is recommended in the design of the control algorithm. Applying the Lyapunov stability theory and backstepping technique, the proposed control strategy ensures that the tracking error is pulled back to a small neighborhood of origin and all closed-loop signals are bounded. Finally, simulation results are presented to show the availability and validity of the analysis approach.