This paper presents an adaptive neuro-fuzzy controller ANFIS (Adaptive Neuro-Fuzzy Inference System) for a bilateral teleoperation system based on FPGA (Field Programmable Gate Array). The proposed controller combines the learning capabilities of neural networks with the inference capabilities of fuzzy logic, to adapt with dynamic variations in master and slave robots and to guarantee good prac...

The aim of this paper is to design a Fuzzy Logic Controller-based Model Reference Adaptive Controller. It consists of Fuzzy Logic Controller (FLC) along with a conventional Model Reference Adaptive Control (MRAC) scheme. The idea is to control the plant by conventional MRAC with a suitable single reference model, and at the same time control the plant by FLC. In the conventional MRAC scheme, th...

In this paper, speed control of asynchronous machine fed by a photovoltaic (PV) generator based on a direct proportional and integral controller and an adaptive fuzzy logic controller (AFLC) is presented. Also In this study, it is proposed that the PV output voltage is varies between certain cut in and cut off values that to extract maximum power during different insolation values instead of op...

In a soft real-time system, the deadline missing ratio is an important metric to evaluate system performance. In addition, considering the unsteadiness and unpredictability of a practical task running environment due to the unsteadiness of network communication and the estimation deviation, it is necessary to introduce fuzzy concept and theory to the scheduling and performance control of the so...

Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the radial basis function neural network (RBF) with G...

Abstract: In this paper an application of the adaptive neuro-fuzzy inference system has been introduced to predict the behavior of a chaotic robot. The chaotic mobile robot implies a mobile robot with a controller that ensures chaotic motions. Chaotic motion is characterized by the topological transitivity and the sensitive dependence on initial conditions. We have used the controller such that...

In this paper, the stability analysis of a GA-Based adaptive fuzzy sliding model controller for a nonlinear system is discussed. First, a nonlinear plant is well-approximated and described with a reference model and a fuzzy model, both involving FLC rules. Then, FLC rules and the consequent parameter are decided on via an Evolved Bat Algorithm (EBA). After this, we guarantee a new tracking perf...

The DC-DC power converters are widely used; however, the controller design for the DC-DC power converters cannot easily design if the load dynamics vary widely. This paper proposes an adaptive wavelet neural control (AWNC) system for a forward DC-DC power converter. The proposed AWNC system is composed of a neural controller and a robust controller. The neural controller uses a wavelet neural n...

The presented control scheme utilizes Adaptive Neuro Fuzzy Inference System (ANFIS) controller to track rotational speed of a reference engine and disturbance rejection during engine idling. To evaluate the performance of the controller a model of the system is developed and simulation results are presented. It is shown that the ANFIS controller is suitable for control systems with large time d...

The presented control scheme utilizes Adaptive Neuro Fuzzy Inference System (ANFIS) controller to track a reference engine rotational speed and disturbance rejection during engine idling. To evaluate the performance of the controller a model of the engine is simulated and simulation results presented. ANFIS implements a first order Sugeno-style fuzzy system. It is a method for tuning an existin...