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A robust adaptive fuzzy control approach is developed for a class of multi-input-multi-output (MIMO) nonlinear systems with modeling uncertainties and external disturbances by using both the approximation property of the fuzzy logic systems and the backstepping technique. The MIMO systems are composed of interconnected subsystems in the strict-feedback form. The main characteristics of the developed...
An adaptive neural network control scheme is developed for a class of nonlinear systems in the strict-feedback form. Compared with the existing approaches, the main advantage is that the developed scheme can be implemented by utilizing only one neural network approximator. Thus, the designed controller structure is simplified. In addition, less neural network can reduce the running cost in practical...
In this paper, an adaptive control algorithm is developed for a class of nonlinear systems with unknown time-delays based on the approximation property of the fuzzy systems. The systems considered in the paper are in non-affine pure-feedback structure. The existing research result is expanded. This algorithm guarantees that all the signals in the closed-loop system are uniformly bounded and the tracking...
In this paper, the output-feedback stabilization problem is investigated for the first time for a class of stochastic nonlinear systems whose zero dynamics may be unstable. Under the assumption that the inverse dynamics of the system is stochastic input-to-state stabilizable, a stabilizing output-feedback controller is constructively designed by the integrator backstepping method together with a new...
In this paper, robust output-feedback stabilization is presented for a class of uncertain stochastic nonlinear systems with time-varying time delays. First, the conditions of existence and uniqueness of the solution process are provided and two new stability notions are introduced for stochastic time-delay systems. Based on these preparations, for a class of uncertain stochastic nonlinear systems...
Using backstepping method, a direct adaptive robust fuzzy control algorithm is proposed for a class of uncertain nonlinear strict-feedback systems in this paper. Fuzzy systems are utilized to approximate unknown parts of the desired control inputs. The key assumption is that the norms of the optimal approximation parameter vectors and the approximation errors are bounded, and the bounds are unknown...
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