A fuzzy control design method for four-wheel-steering vehicles, using fuzzy models, is presented. The design model is obtained from a vehicle model using fuzzy modeling approach. In the first step of the controller design, an optimal steering controller is proposed for each local model using LQR method. Then, the local controllers are combined using fuzzy rules to form a fuzzy controller. In th...

unknown noise and artifacts present in medical signals with  non-linear fuzzy filter will be estimate and then removed. an adaptive neuro-fuzzy interference system which has a nonlinear  structure presented  for the noise function prediction by before samples. this paper is about a neuro-fuzzy method to estimate unknown noise of electrocardiogram (ecg) signal. adaptive neural combined with fuzz...

Abstract A novel adaptive fuzzy controller for a class of uncertain multivariable nonlinear systems with time delays is proposed in this paper. By combining fuzzy Takagi-Sugeno (T-S) models and fuzzy logic systems, the modeling error and the uncertainties are not required to satisfy the constraint conditions. In the meantime, fuzzy T-S models are used to approximate the nonlinear systems and fu...

This paper proposes a reinforcement fuzzy adaptive learning control network (RFALCON), constructed by integrating two fuzzy adaptive learning control networks (FALCON), each of which has a feedforward multilayer network and is developed for the realization of a fuzzy controller. One FALCON performs as a critic network (fuzzy predictor), the other as an action network (fuzzy controller). Using t...

This paper proposes inverse fuzzy-model-based feed-forward fuzzy controllers to compensate non-linear terms that affect the system dynamics. The gradient-descent algorithm can be used on-line to form adaptive fuzzy controllers. This ability allows these controllers to be used in applications were the knowledge to control the process does not exist or the process is subject to changes in its dyn...

On the basis of inverse dynamics controller as a nominal control portion, two types of novel robust adaptive inverse dynamics control schemes are proposed for the trajectory tracking control of robot manipulator with uncertain dynamics. They are composed of an adaptive fuzzy control algorithm and a nonlinear H∞ tracking control model. The adaptive fuzzy control algorithm is employed to approxim...

the proposed iafc neural networks have both stability and plasticity because theyuse a control structure similar to that of the art-1(adaptive resonance theory) neural network.the unsupervised iafc neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. this fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. the supervised iafc ...

In this paper, a novel adaptive fuzzy backstepping output feedback control scheme is proposed for a class of single-input single-output (SISO) uncertain nonlinear systems without measurements of states. Fuzzy logic systems (FLS) are used to tackle unknown nonlinear functions, and the adaptive fuzzy output feedback controller is constructed by combining fuzzy filters observer design and the dyna...

In this paper, the authors propose two indirect adaptive fuzzy control schemes for a class of uncertain continuous-time single-input single-output (SISO) nonlinear dynamic systems with known and unknown control direction. Within these schemes, fuzzy systems are used to approximate unknown nonlinear functions and the Nussbaum gain technique is used to deal with the unknown control direction. Thi...

This paper presents a discrete-time robust control for electrically driven robot manipulators in the task space. A novel discrete-time model-free control law is proposed by employing an adaptive fuzzy estimator for the compensation of the uncertainty including model uncertainty, external disturbances and discretization error. Parameters of the fuzzy estimator are adapted to minimize the estimat...