The neuro-fuzzy controller incorporates fuzzy logic algorithm with an artificial neural network (ANN) structure. The conventional PI controller is replaced by Adaptive NeuroFuzzy Inference System (ANFIS), which tunes the fuzzy inference system with hybrid learning algorithm, This makes fuzzy system training with performance of the neuro-fuzzy based vector controlled of the system under controll...

This paper introduces Fuzzy State Automata for control applications. They are derived from the integration of traditional nite state automata and neuro-fuzzy systems, where a nite state automaton tracks the state of the plant under control and modiies the characteristic of the neuro-fuzzy system accordingly. The main diierence with respect to existing systems is that the states of the automata ...

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...

This paper discusses a neuro-fuzzy controller for sensor-based mobile robot navigation in indoor environments. The control system consists of a hierarchy of robot behaviors.

The dynamics of robot manipulators are highly nonlinear with strong couplings existing between joints and are frequently subjected to structured and unstructured uncertainties. Fuzzy Logic Controller can very well describe the desired system behavior with simple “if-then” relations owing the designer to derive “if-then” rules manually by trial and error. On the other hand, Neural Networks perfo...

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...

In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) based control is proposed for the tracking of a Micro-Electro Mechanical Systems (MEMS) gyroscope sensor. The ANFIS is used to train parameters of the controller for tracking a desired trajectory. Numerical simulations for a MEMS gyroscope are looked into to check the effectiveness of the ANFIS control scheme. It proves that the sy...

This paper proposes a modified particle swarm optimization algorithm (MPSO) to design adaptive neuro-fuzzy controller parameters for controlling the behavior of non-linear dynamical systems. The modification of the proposed algorithm includes adding adaptive weights to the swarm optimization algorithm, which introduces a new update. The proposed MPSO algorithm uses a minimum velocity threshold ...

This paper presents a novel development of neuro-fuzzy coordination between Static synchronous series compensators (SSSC) to damp the oscillations in a multi-machine power system. The oscillations in power systems have to be taken a serious note of when the fault occurs in any part of the system, else this might lead to the instability mode and shutting down of the power system. The SSSC based ...