The adaptive stabilization problem of nonlinear systems are studied. For a class of uncertain nonlinear systems with unknown control direction, we proposed a robust adaptive backstepping scheme withσ-modification by ...The adaptive stabilization problem of nonlinear systems are studied. For a class of uncertain nonlinear systems with unknown control direction, we proposed a robust adaptive backstepping scheme withσ-modification by introducing Nussbaum function and Backstep- ping methods, and proved that all the signals of the closed-loop systems are bounded.展开更多
In this paper, a state feedback adaptive stabilization for a class of large-scale stochastic nonlinear systems is designed with Lyapunov and Backstepping method. In the systems there are uncertain terms, whose bounds ...In this paper, a state feedback adaptive stabilization for a class of large-scale stochastic nonlinear systems is designed with Lyapunov and Backstepping method. In the systems there are uncertain terms, whose bounds are governed by a set of unknown parameters. The designed controllers would make the close-loop systems asymptotically stable and adaptive for the unknown parameters. As an application, a second order example is delivered to illustrate the approach.展开更多
文摘The adaptive stabilization problem of nonlinear systems are studied. For a class of uncertain nonlinear systems with unknown control direction, we proposed a robust adaptive backstepping scheme withσ-modification by introducing Nussbaum function and Backstep- ping methods, and proved that all the signals of the closed-loop systems are bounded.
基金The research is supported by the National Science Foundation of Henan Educational Committee of China （No. 2003110002）.
文摘In this paper, a state feedback adaptive stabilization for a class of large-scale stochastic nonlinear systems is designed with Lyapunov and Backstepping method. In the systems there are uncertain terms, whose bounds are governed by a set of unknown parameters. The designed controllers would make the close-loop systems asymptotically stable and adaptive for the unknown parameters. As an application, a second order example is delivered to illustrate the approach.
