Adaptive Control for a Class of Uncertain Strict-feedback Nonlinear Systems based on a Generalized Fuzzy Hyperbolic Model

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Daniel Felix Ritchie School of Engineering and Computer Science, Electrical and Computer Engineering


Adaptive control, Generalized fuzzy hyperbolic model, Lyapunov function, Strict-feedback


In this study, we propose an effective method for designing an adaptive controller for a class of uncertain strict-feedback nonlinear systems with unknown bounded disturbances. During the controller design process, all of the unknown functions are accumulated at the intermediate steps to approximate the last step. In addition, only one generalized fuzzy hyperbolic model is used to approximate the total unknown functions for the system. Thus, only the actual control law needs to be implemented and one adaptive law is proposed for the overall controller design process. As a result, the controller design is much simpler and the computational burden is reduced greatly. Using Lyapunov techniques, we obtain the uniformly ultimately bounded stability of all the signals for the closed-loop system. Our simulation results verified the theoretical analysis and they illustrated the superior performance of the method proposed in this study.

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