We are developing exoskeletal robots for human (especially for physically weak people) motion support. In this paper, we propose a fuzzy-neuro control method for a 1DOF exoskeletal robot to support the human elbow motion. The proposed controller controls the angular position and impedance of the exoskeltal robot system based on vague biological signals that reflect the human subject’s intention...

This paper presents an innovative approach to self-adaptation of the structure of a neuro-fuzzy controller in real time. Without any off-line pretraining, the algorithm achieves very high control performance through the iteration of a three-stage algorithm. In the first stage, coarse tuning of the neuro-fuzzy rules (both rule consequents and membership functions of the premises) is accomplished...

In this paper, a hybrid neuro-fuzzy controller (NFC) is presented for the speed control of brushless DC motors to improve the control performance of the drive under transient and steady state conditions. In the hybrid control system, proportional-derivative (PD) type neuro-fuzzy controller (NFC) is the main tracking controller, and an integral compensator is proposed to compensate the steady st...

Neural Networks and Fuzzy Inference Systems are becoming well-recognized tools of designing an identifier/controller capable of perceiving the operating environment and imitating a human operator with high performance. The motivation behind the use of neuro-fuzzy approaches is based on the complexity of real life systems, ambiguities on sensory information or timevarying nature of the system un...

A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision...

This work is an attempt to illustrate the usage and effectiveness of soft computing techniques in the estimation and control of multi input and multi output systems. This paper focuses on neuro-fuzzy system ANFIS (Adaptive Neuro Fuzzy Inference system). An Adaptive Network based Fuzzy Interference System architecture extended to cope with multivariable systems has been used. The performance of ...

This paper shows the design of a neuro-fuzzy system to control the vertical motion of a TF-120 fast ferry, so that to improve the passengers comfort by reducing the main cause of seasickness. The aim of this controller is to decrease the pitch acceleration by controlling the position of some actuators, varying their working angles. The controller is based on a fuzzy model of the ship behaviour....

Direct torque control (DTC) of Switched reluctance motor is known to have simple control structure with comparable performance to that of field oriented control techniques. However, the role of optimal selection of the voltage space vector is one of the weakest points in a conventional DTC drive. In this paper optimal selection of voltage space vectors is achieved using neuro fuzzy controller. ...

With their significant advantages over traditional electromagnetic motors, ultrasonic motors (USMs) are becoiming attractive. This paper presents a neuro-fuzzy dualmode controller, combining the knowledge-based fuzzy logic and the leaming-incorporated neural network as well as using dual control inputs, for servo control of travelling-wave USMk. According to the proposed neuro-fuzzy control, bo...

An intelligent approach for smart material actuator modeling of the actuation lines in a morphing wing system is presented, based on adaptive neuro-fuzzy inference systems. Four independent neuro-fuzzy controllers are created from the experimental data using a hybrid method -a combination of back-propagation and Least-Mean-Square (LMS) methods -to train the fuzzy inference systems. The controll...