This paper deals with adaptive control of nonlinear dynamic systems preceded by unknown backlash-like hysteresis nonlinearities, where the hysteresis is described by a dynamic equation. By utilizing this dynamic model and by combining a fuzzy universal function approximator with adaptive control techniques, a stable adaptive fuzzy control algorithm is developed without constructing a hysteresis...

Fuzzy inference systems and neural networks are complementary technologies in the design of adaptive intelligent systems. Artificial Neural Network (ANN) learns from scratch by adjusting the interconnections between layers. Fuzzy Inference System (FIS) is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. A neuro-fuzzy system is sim...

In this paper, the principle of sliding mode control is used as a basis to develop an adaptive fuzzy controller for uncertain dynamic time-delayed systems with series nonlinearities. The control method provides a simple way to achieve asymptotic stability of the uncertain time-delayed system. Other attractive features of the method include a minimal realization of the adaptive fuzzy controller ...

In this paper, we present a combined direct and indirect adaptive control scheme for adjusting an adaptive fuzzy controller, and an adaptive fuzzy identification model parameters. First, using adaptive fuzzy building blocks, with a common set of parameters, we design and study an adaptive controller and an adaptive identification model that have been proposed for a general class of the uncertai...

A novel adaptive control strategy is proposed herein to increase the efficiency of adaptive control by combining Takagi–Sugeno (T–S) fuzzy modeling and the Ge–Yao–Chen (GYC) partial region stability theory. This approach provides two major contributions: (1) increased synchronization efficiency, especially for parameters tracing and (2) a simpler controller design. Two simulated cases are prese...

In this paper, the convergence of parameter estimates and the interactions among the two adaptive fuzzy systems constituting an indirect adaptive fuzzy controller are studied, both analytically and by means of simulations with a second-order nonlinear system. The analytical results and the simulations, performed with various initial conditions and learning rates, highlight how the interactions ...

This paper proposes an adaptive TS recurrent fuzzy CMAC (TS-RFCMAC) model based control of uncertain time-delay nonlinear systems. First, we introduce a TS-RFCMAC network and its application on system modeling. Next, a TS-RFCMAC controller is developed based on parallel distributed compensation and adaptive control laws. Even if uncertain local subsystem matrices and fuzzy sets exist in the mod...

This paper proposes a novel intelligent control scheme using type-2 fuzzy neural network type-2 FNN system. The control scheme is developed using a type-2 FNN controller and an adaptive compensator. The type-2 FNN combines the type-2 fuzzy logic system FLS , neural network, and its learning algorithm using the optimal learning algorithm. The properties of type-1 FNN system parallel computation ...

this paper deals with leader-following and leaderless consensus problems of high-order multi-input/multi-output (mimo) multi-agent systems with unknown nonlinear dynamics in the presence of uncertain external disturbances. the agents may have different dynamics and communicate together under a directed graph. a distributed adaptive method is designed for both cases. the structures of the contro...

This paper explores the learning fuzzy inference systems implemented as adaptive fuzzy-neural networks. The research into application of learning techniques to fuzzy inference systems (FIS) has matured into a family of adaptive fuzzy inference systems (AFIS). In most cases, the learning FIS and AFIS families can be interpreted as a partially connected multilayer feedforward neural network with ...