The goal of this work is to present a robust optimal control approach, in order to improve the speed error-tracking and control capability of a permanent magnet DC Motor (PMDC) driven wire-feeder systems (WFSs) of gas metal arc welding (GMAW) process. The proposed speed controller employs an optimized fractional-order-proportional+integral+derivative (FOPID) controller that serves to eliminate oscillations, overshoots, undershoots and steady state fluctuations of the PMDC motor and makes the wire-feeder unit (WFU) has fast and stable starting process as well as excellent dynamic characteristics. The fixed controller parameters are meta-heuristically selected via an ant colony optimization (ACO) algorithm. Numerical simulations are performed in Matlab/Simulink environment and the performance of the proposed ACO-FOPID controller is validated. The simulation results clearly demonstrate the significant improvement rendered by the proposed approach in the wire-feeder system's reference tracking performance, torque disturbance rejection capability, and transient recovery time.
The most important issue in the use of wind energy conversion systems is to ensure maximum power extraction in terms of efficiency. Therefore, maximum power point tracking algorithms are as important as the maximum power point tracking controller. In this study, maximum power extraction frameworks operating the state-of-the-art optimization methods are presented for permanent magnet synchronous generator–based wind energy conversion system. These frameworks consist of a Gauss map–based chaotic particle swarm optimization and a hybrid maximum power point tracking approach that combines feedback linearization technique with fractional-order calculus. The feedback linearization control strategy can fully decouple and linearize the original state variables of the nonlinear system and thus provide an optimal controller crossing wide-range operating conditions. The objective is to maintain the tip speed ratio at its optimal value, which implies the use of a rotational speed loop. The method is based on the feedback linearization technique and the fractional control theory. Gauss map–based chaotic particle swarm optimization, which is a remarkable and recent optimization technique, is utilized to achieve optimum coefficients to efficiently ensure the maximum power point tracking operation in here. A simulation study is carried out on a 3-kW wind energy conversion system to show the effectiveness of the proposed control scheme.
Advanced control algorithms must be used to make wind power generation truly cost effective and reliable. In this study, we develop a new and simple control scheme that employs model predictive control (MPC), which is used in permanent magnet synchronous generators and grid-connected inverters. The proposed control law is based on two points, namely, MPC-based torque-current control loop is used for the generator-side converter to reach the maximum power point of the wind turbine, and MPC-based direct power control loop is used for the grid-side converter to satisfy the grid code and help improve system stability. Moreover, a simple prediction scheme is developed for the direct-drive wind energy conversion system (WECS) to reduce the computation burden for real-time applications. A small-scale WECS laboratory prototype is built and evaluated to verify the validity of the developed control methods. Acceptable results are obtained from the real-time implementation of the proposed MPC methods for WECS.
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