This paper addresses the issue of trajectory tracking control based on a neural network controller for industrial manipulators. A new control scheme is proposed based on neural network technology and linear feedback approach for tracking a planned trajectory. In detail, the control system is designed with two parallel subsystems designed separately. One is a linear controller, and another one i...

In this paper we will address our efforts to design a neural control system that can control a simulated aircraft, which ultimately should lead to a realistic artificial pilot. The system we designed consists of a flight plan module, the actual neural controller module, and a graphic user interface. The goal of the flight plan module is to manage the global control of the whole system. For the ...

If nonlinearities such as saturation of the amplifier gain and motor torque, gear backlash, and shaft compliancesjust to name a few are considered in the position control system of marine helm, traditional control methods are no longer sufficient to be used to improve the performance of the system. In this paper an alternative approach to traditional control methods a neural network reference c...

This study proposes a soft computing speed controller where recurrent neural network speed controller is trained by genetic extended kalman filter, based on a predetermined genetic Proportional Integral (PI) controller. A genetic PI gives to this proposed speed controller exact values without mathematical equations, unlike the classical PI who is need for it necessary. With this proposed method...

A control scheme for dynamic tracking of mobile robots is presented, which integrates a velocity controller based on backstepping techniques and a torque controller based on improved RBF neural networks. The proposed torque control strategy derived from sliding modes depends on the dynamics of mobile robots. Because of the uncertainties in robot dynamics, the robustness of the system cannot be ...

A speed sensorless neuro-fuzzy controller is proposed for brush type DC motors. The actual speed of the DC machine is estimated using a feed-forward neural network. The inputs of the neural network are the armature current and voltage of the DC machine and their changes in time. Because DC machines are usually fed by 4-quadrant chopper, the measured armature voltage and current contains higher ...

The goal of this chapter is to enable a nonholonomic mobile robot to track a specified trajectory with minimum tracking error. Towards that end, an adaptive P controller is designed whose gain parameters are tuned by using two feed-forward neural networks. Back-propagation algorithm is chosen for online learning process and posture-tracking errors are considered as error values for adjusting we...

|In this paper we present an approach to design a controller that enables a simulated car to drive autonomously around a race track. The input to the controller is the current speed of the car and several sensor signals indicating the properties of the race track, and as its output the controller is supposed to determine the car's change of direction and its change of speed in response to the i...

In this paper, dynamic modeling of a Vestas 660 kW wind turbine and its validation are performed based on operational data extracted from Eoun-Ebn-Ali wind farm in Tabriz, Iran. The operational data show that the turbine under study, with a classical PI controller, encounters high fluctuations when controlling the output power at its rated value. The turbine modeling is performed by deriving th...

Controllers capable to perform failure diagnosis have an additional diagnostic output to detect and isolate sensor and actuator faults. A linear such controller is usually called a four-parameter controller. Neural networks have proved to be a very powerful tool in the control systems area, where they have been used in the modelling and control of dynamical systems. In this paper, a neural netw...