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在已知名义系统的基础上，将ＣＭＡＣ神经网络用于一类状态反馈可线性化的单输入单输出（ＳＩＳＯ）连续时间非线性系统的鲁棒自适应反馈线性化，使系统获得要求的跟踪性能，控制器的结构为自适应反馈线性化控制律加一个鲁棒控制项，在很弱的假设条件下，应用李雅普诺夫稳定性理论证明了闭环系统内的所有信号为ＵＵＢ（均匀最终有界）．本方法特别适合于已知名义系统模型但具有不确定性的一类非线性系统的实时控制。仿真算例进一步证明了本方法的正确与有效。
Based on the known nominal system, the CMAC neural network is used for robust adaptive feedback linearization of a class of state-feedback linearizable single-input-single-output (SISO) continuous-time nonlinear systems, which enables the system to obtain the required tracking The structure of the controller adds a robust control term to the adaptive feedback linearization control law. Under very weak assumptions, the Lyapunov stability theory proves that all the signals in the closed-loop system are UUB Community). The method is especially suitable for the real-time control of a class of nonlinear systems with known nominal system model but uncertainties. The simulation example further proves that this method is correct and effective.