A robust partial-state feedback asymptotic regulating control scheme is developed for a class of cascade systems with both nonlinear uncertainties and unknown control directions. A parameter separation technique is in...A robust partial-state feedback asymptotic regulating control scheme is developed for a class of cascade systems with both nonlinear uncertainties and unknown control directions. A parameter separation technique is introduced to separate the time-varying uncertainty and the unmeasurable state from nonlinear functions. Then, the Nussbaum-type gain method together with the idea of changing supply functions is adopted in the design of a smooth partial-state regulator that can ensure all the signals of the closed-loop system are globally uniformly bounded. Especially, the system state asymptotically converges to zero. The design procedure is illustrated through an example and the simulation results show that the controller is feasible and effective.展开更多
This paper considers the problem of adaptive con-trol for a class of multiple input multiple output （MIMO） nonlinear discrete-time systems based on input-output model with unknown interconnections between subsystems...This paper considers the problem of adaptive con-trol for a class of multiple input multiple output （MIMO） nonlinear discrete-time systems based on input-output model with unknown interconnections between subsystems. Based on the Taylor ex-pand technology, an equivalent model in affine-like form is derived for the original nonaffine nonlinear system. Then a direct adap-tive neural network （NN） control er is implemented based on the affine-like model. By finding an orthogonal matrix to tune the NN weights, the closed-loop system is proven to be semiglobal y uni-formly ultimately bounded. The σ-modification technique is used to remove the requirement of persistence excitation during the adaptation. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters.展开更多
基金supported by the National Natural Science Foundation of China (No.60774010,60574080)the research startup Foundation of Qufu Normal University
文摘A robust partial-state feedback asymptotic regulating control scheme is developed for a class of cascade systems with both nonlinear uncertainties and unknown control directions. A parameter separation technique is introduced to separate the time-varying uncertainty and the unmeasurable state from nonlinear functions. Then, the Nussbaum-type gain method together with the idea of changing supply functions is adopted in the design of a smooth partial-state regulator that can ensure all the signals of the closed-loop system are globally uniformly bounded. Especially, the system state asymptotically converges to zero. The design procedure is illustrated through an example and the simulation results show that the controller is feasible and effective.
基金supported by the Fundamental Research Funds for Central Universities(N100604002)
文摘This paper considers the problem of adaptive con-trol for a class of multiple input multiple output （MIMO） nonlinear discrete-time systems based on input-output model with unknown interconnections between subsystems. Based on the Taylor ex-pand technology, an equivalent model in affine-like form is derived for the original nonaffine nonlinear system. Then a direct adap-tive neural network （NN） control er is implemented based on the affine-like model. By finding an orthogonal matrix to tune the NN weights, the closed-loop system is proven to be semiglobal y uni-formly ultimately bounded. The σ-modification technique is used to remove the requirement of persistence excitation during the adaptation. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters.
