Robust Adaptive Fuzzy Control Scheme Research Articles

Synchronization of FitzHugh–Nagumo systems in EES via H∞ variable universe adaptive fuzzy control

In this paper, H ∞ variable universe adaptive fuzzy control law is derived and applied to synchronize two FitzHugh—Nagumo neurons. Firstly, the different dynamical behavior of the nonlinear cable model based on the FitzHugh–Nagumo model responding to the various external electrical simulations (EES) is studied. Next, using the result of the analysis, a robust adaptive variable universe fuzzy control scheme is designed to synchronize two neurons. The computer simulations is provided for verify the efficiency of the designed synchronization scheme.

Robust direct adaptive fuzzy control for nonlinear MIMO systems

Abstract For a class of nonlinear multi-input multi-output systems with uncertainty, a robust direct adaptive fuzzy control scheme was proposed. The feedback control law and adaptive law for parameters were derived based on Lyapunov design approach. The overall control scheme can guarantee that the tracking error converges in the small neighborhood of origin, and all signals of the closed-loop system are uniformly bounded. The main advantage of the proposed control scheme is that in each subsystem only one parameter vector needs to be adjusted on-line in the adaptive mechanism, and so the on-line computing burden is reduced. In addition, the proposed control scheme is a smooth control with no chattering phenomena. A simulation example was proposed to demonstrate the effectiveness of the proposed control algorithm. * Supported by National Natural Science Foundation of China (Grant Nos. 60325311, 6053010, 60521003) and Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT...

Robust adaptive fuzzy VSS control for a class of uncertain nonlinear systems using small gain design

In this paper, a novel robust adaptive fuzzy variable structure control (RAFVSC) scheme is proposed for a class of uncertain nonlinear systems. The uncertain nonlinear system and gain functions originating from modeling errors and external disturbances are all unstructured (or non-repeatable), state-dependent and completely unknown. The Takagi–Sugeno type fuzzy logic systems are used to approximate uncertain functions in the systems and the RAFVSC is designed by use of the input-to-state stability (ISS) approach and small gain theorem. In the algorithm, there are three advantages which are that the asymptotic stability of adaptive control in the presence of unstructured uncertainties can be guaranteed, the possible controller singularity problem in some of existing adaptive control schemes using feedback linearization techniques can be removed and the adaptive mechanism with minimal learning parameterizations can be achieved. The performance and effectiveness of the proposed methods are discussed and illustrated with two simulation examples.

Robust adaptive fuzzy control scheme for nonlinear system with uncertainty

In this paper, a robust adaptive fuzzy control scheme for a class of nonlinear system with uncertainty is proposed. First, using prior knowledge about the plant we obtain a fuzzy model, which is called the generalized fuzzy hyperbolic model (GFHM). Secondly, for the case that the states of the system are not available an observer is designed and a robust adaptive fuzzy output feedback control scheme is developed. The overall control system guarantees that the tracking error converges to a small neighborhood of origin and that all signals involved are uniformly bounded. The main advantages of the proposed control scheme are that the human knowledge about the plant under control can be used to design the controller and only one parameter in the adaptive mechanism needs to be on-line adjusted.

Robust adaptive fuzzy control and its application to ship roll stabilization

A robust adaptive fuzzy control (RAFC) scheme, which can be used to control a class of uncertain nonlinear systems in which the robust relative degree is equal to the system's degree, is proposed for the problem of ship roll stabilization. The uncertain system can be transformed into the controllability canonical form by the global diffeomorphism. Using the universal approximation capability of the fuzzy system, we construct a fuzzy system as the upper bound of uncertainties in the nonlinear uncertain system. Based on Lyapunov direct method, we prove that the RAFC scheme proposed in this paper can guarantee the closed-loop system to converge exponentially with a prescribed degree towards a residual ball around the equilibrium. Since only one parameter needs to be adapted on-line, the computing load of the proposed RAFC algorithm can be significantly reduced. We then demonstrate how to design a RAFC for ship roll stabilization. The feasibility of the proposed method is verified through a container ship simulation.

ROBUST ADAPTIVE FUZZY CONTROL FOR NONLINEAR SYSTEMS WITH NON-MINIMUM PHASE

ABSTRACT This paper presents a robust adaptive fuzzy control scheme for the stabilization of nonlinear systems with non-minimum phase. Generally, while straightforward application of the feedback linearization technique to a non-minimum phase system results in a linear input- output response, the associated internal dynamics are unstable. The proposed scheme employs a fuzzy system to adapatively compensate for the plant nonlinearities and forces the plant output to asymptotically track the output of a prespecified reference model, which can stabilize the internal dynamics. When matching with the model occurs, the plant output will converge to the origin and the internal dynamics can be simultaneously stabilized. The stability and the tracking error asymptotically convergent to a predetermined boundary are established via Lyapunov's stability theorem. Experiments on an inverted wedge system are given to show the effectiveness of the proposed scheme.