Pitch control is one of the major aspects of wind turbine control, particularly over high wind Speed and oscillations. General Electric (GE) model of wind turbine is practically compatible with the structure of the wind turbines. It ha s bee n pr ove d t h at simulation results using this model are closer to the actual case, compared to other available models. Therefore, in this paper the GE model is used to evaluate the effectiveness of three different controllers including Fuzzy controller, self-organized Fuzzy controller (SOFC) and PI controller in pitch control of the wind turbine. Afterward, the results of the controller applications as well as the no controller case in t h e pitch control are compared. The results show a better performance of SOFC in damping the oscillations and overshoot of the wind turbine shaft Speed. Finally, electrical power limit and converter cost, the economic analysis of pitch controller application are carriedout. It is shown that the application of the SOFC results are around $142, 646 saving.

Surge is one of the two destructive factors in compressors. surge is the stream instability phenomenon in compressor that imposes severe damages to the compressors. Nowadays, suppressing surge phenomenon is one of the most important issues in oil and gas industries, especially when flow reduction or gas reflux is considered. According to Moore-Greitzer compressor model, this paper designs an active controller for surge control in constant Speed centrifugal compressors. As such, the applied operator considered for surge control is Close Couple Valve (CCV) and it is designed to stabilize a centrifugal compressor system with disturbaces using nonlinear predictive controller. The proposed controller, without any information about the amount of Throttle Valve variations, could control the surge instability and reduce the distance between compressor operation point and the surge line. Finally, the compressor system with controller is simulated and the obtained results will show the efficiency of the designed nonlinear predictive controller.

Nowadays Autonomous Underwater Vehicles (AUVs) are an unavoidable part of marine industries. One of the most important parts of any autonomous vehicle is the control issue to achieve the desired performance. This paper is concerned with Speed control of an AUV model respecting the state and control constraints. According to the Newton-Euler method, the 6 DOF kinematic and dynamic models of the AUV are established. A well-defined performance index and constrained finite horizon optimization program in the form of Model Predictive control (MPC) strategy is proposed to regulate the horizontal Speed of AUV to its desired value while the constraints on the states like depth and control signals are considered in finite time horizon optimization program to be satisfied. The main problem for such a situation is the interaction between Speed control and depth deviation then quadratic programing technique managed responses to avoid state and control signal constraints. Simulation results show a reliable performance of proposed MPC strategy to control the horizontal Speed of AUV while all the constraints on state, control signal and also the variation of the control signal are satisfied.

In the present study, field oriented control of step motor implementation has been analyzed so that it can make a Sensorless control. Efficiency and Facilities of step motor is more than other types of electromotor. Therefore, the numbers of mechanisms and different types of turning can be made into them. Also controlling these motors is easier than other available motors. Stepping motor has been designed with oriented control in MATLAB software by using a Simulink tool box. In two methods above, current, torque and engine Speed have been investigated by and without using Kalman filter. The results showed that using field oriented control can eradicate resonance abnormalities. By using field oriented control, the maximum theoretical engine performance can be achieved.

Vectorial control of the Speed of an induction motor is an advanced Speed control method, enabling us to control torque, magnetic flux individually as that of DC motor. This paper pre3sents, the effecting factors dominating on the motor &the primarily vectorial control, leading to an advanced model of control in various &arbitrary co- ordinate systems. Finally a biased field control method with/&without Speed sensors, introduced & simulated.

Over the past few years, many electrical vehicle manufacturers have focused on developing enhanced controller efficiency for Four-Wheel Drive (FWD) systems. The control of the Speed and direction of the FWD is crucial for safe and efficient operation, particularly in challenging maneuvers. The FWD system movements in straight routes and during maneuvers, turning all four wheels right and left, have not been well covered. Therefore, a robust control design that is capable of controlling the FWD system at an optimal time of operation is highly required. In this research, a Finite Time control (FTC) is designed, implemented, and simulated to improve the robustness and performance of the FWD system during challenging maneuvers. The proposed FTC controls both the Speed and direction of all wheels of FWD according to the route situations. The proposed FTC is compared with an FWD system that is controlled by a traditional Proportional, Integral, and Derivative (PID) controller during straight moving and maneuvers. The comparison is based on controlling parameters such as settling time, maximum overshoot, and Speed error values. The results showed that the proposed FTC has a much faster settling time, significantly less maximum overshoot, and much lower error values than the PID controller. These factors are considered the main features of the contribution of any controller system that aims for optimal and robustness and FTC proved to have them adequately.

This paper presents an emotional controller for brushless DC motor (BLDC) drive. The proposed controller is called brain emotional learning based intelligent controller (BELBIC). The utilization of the new controller is based on the emotion processing mechanism in brain. This intelligent control is inspired by the limbic system of mammalian brain, especially amygdala. The controller is successfully implemented in simulation using MATLAB software, brushless dc drive with trapezoidal back-emf. In this work, a novel and simple implementation of BLDC motor drive system is achieved by using the intelligent controller, which controls the motor Speed accurately. This emotional intelligent controller has simple structure with high auto learning feature. Simulation results show that both accurate steady state and fast transient Speed responses can be achieved in wide range of Speed from 20 to 300 [rpm]. Moreover, to evaluate this emotional controller, the performance of the proposed control scheme is compared with both Fuzzy Logic (FL) and PID controllers, in different conditions. This indicates proper operating in comparison to the FLC and PID controllers. And also shows excellent promise for industrial scale utilization.