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The sign of unknown input coefficients is assumed to be known in most papers about the input uncertainties. In this paper, a Nussbaum gain method is adopted to cope with the situation that both the sign and the value of input are unknown. And the unknown parameters can be estimated under the situation of unknown sign of control. At last, numerical simulations are done to show the effectiveness of...
The observer-based integral adaptive fuzzy sliding mode controllers are developed for a class of uncertain nonlinear systems. By designing the state observer, the fuzzy systems, which are used to approach any unknown functions, it can be constructed using the state observer-based estimations. Based on Lyapunov stability theorem, the proposed integral adaptive fuzzy sliding mode control system can...
This paper deals with the design of a robust adaptive fuzzy tracking control for a class of uncertain and disturbed nonlinear systems. In addition to the desired performances and the convergence of the tracking error, the proposed approach guarantees the uniformly ultimately boundedness of the resulting closed-loop system. Furthermore, it allows overcoming many problems related to adaptive fuzzy controllers...
An adaptive neural network control (ANNC) is proposed for a class of strict-feedback uncertain nonlinear systems with unknown system nonlinearities and unknown virtual control gain nonlinearities. Combining the dynamic surface control (DSC) technique with minimal-learning-parameters (MLP) algorithm, a systematic procedure for synthesis of ANNC is developed based on the universal approximation of neural...
In this paper, we consider the problem of adaptive Hinfin control for stochastic systems with parameter uncertainties, time delay and unknown nonlinear perturbations. The unknown nonlinear perturbations are norm-bounded and the gains are unknown while there is adaptive parameter in the adjustable output. Using linear matrix inequality and Lyapunov-Krassovskii approaches, the designed controller not...
In this paper, a modular approach is proposed for a class of strict-feedback stochastic nonlinear systems with uncertain Wiener noises and constant unknown parameters. Both the adaptive Backstepping procedure and input-to-state stable(ISS) controller of global stabilization in probability are designed to guarantee that the system states are bounded and has adaptive stabilization while the covariance...
In this paper, the problem of adaptive robust state observer design is considered for a class of uncertain nonlinear time-delay systems. It is supposed that the upper bound of the nonlinearity and uncertainty, including delayed states, is a linear function of some parameters which are still assumed to be unknown. An improved adaptation law with sigma-modification is employed to estimate the unknown...
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