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The problem of adaptive robust stabilization is considered for a class of nonlinear systems with uncertainties which consist of uncertain system parameters and multiple external disturbances. It is supposed that the upper bounds of these uncertainties are unknown. In particular, different from the results reported in the control literature, in this paper the multiple external disturbances are assumed...
In recent years, adaptive inverse control is a very vivid field because of its advantages. It is quite different from the traditional control. Adaptive inverse control adopts feedback in parameters tuning of the system, not the signal flow. In this paper, we apply the recursive least-squares (RLS) algorithm to the adaptive inverse control to achieve the learning of the inverse model quickly. Besides,...
The output tracking problem of periodic reference signals (of known period) for single-input single-output observable minimum phase uncertain linear time-invariant systems with unitary relative degree is considered. A continuous global iterative learning control via output error feedback is designed which guarantees closed loop boundedness and asymptotic output tracking, thus improving the L2 convergence...
A direct adaptive control method with a simple identification of the controller parameters is presented for a class of uncertain time-varying nonlinear systems. The systems are divided into a finite number of subsystems, which are interpolated by functions of an exogenous scheduling variable. The adaptive method yields stability of all signals in the closed-loop, as well as convergence of the state...
An adaptive control scheme is proposed for a kind of time-varying nonlinear systems. In the scheme the nonlinear systems are divided into a finite number of subsystems, which are in strict feedback and interpolated by functions of an exogenous scheduling variable. The adaptive method yields stability of all signals in the closed-loop. The results of simulation show that the controller converges very...
Combining the pointwise integral mechanism with the feedback linearization approach, a novel adaptive repetitive learning control for high-order nonlinearly parameterized uncertain systems with time-varying and time-invariant parameters is proposed. It can be applied to the time-varying parametric uncertainty systems with unknown compact set, rapid time-varying periodic and where the prior knowledge...
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