×
The submission system is temporarily under maintenance. Please send your manuscripts to
Go to Editorial ManagerTraditional Proportional-Integral-Derivative (PID) controllers continue to produce effective performance in controlling DC motors. Nowadays, advancements in optimization techniques are improving control performance. These techniques minimize fluctuations and accelerate convergence. In this study, the Particle Swarm Optimization (PSO) algorithm was applied using the Integral Time Absolute Error (ITAE) objective function. The PSO used to tune PID parameters in MATLAB/Simulink. The resulting PSO-PID performance was compared with that of a conventional PID controller. The findings indicate that the PSO-PID approach reduces peak overshoot errors from 2.1% to 0.5%. Moreover, the PSO-PID substantially decreases the settling time. Despite adding GA, PSO-PID already shows excellent results (0.93 s settling time, compared with 1.85 s in GA and 1.15 s in classical PID). These improvements contribute to smoother actuator voltage. It can also reduce mechanical and electrical issues and enhance long-term system reliability.
This study uses intelligent techniques to regulate brushless direct current speed (BLDC) motors. After these motors solved the problem of using brushes and commutators in traditional DC motors, they succeeded in replacing brushes and commutators with electronic commutators. Due to the use of electronic switching, brushless motor algorithms are more complex than those of conventional motors. In this study, to adjust the PID controller's settings (Kp, Ki, and Kd), a trial-and-error approach was taken, and a completely new method known as the settings of known PID controllers have been modified using the new Gray Wolf algorithm. A BLDC motor's main benefit is that it has easy speed adjustment across a broad range, whereas AC motors often cannot be controlled in this way. Through the use of Matlab/Simulink, the BLDC motor's mathematical model was developed and implemented. The simulation results show that in the first case, a PID controller effectively induces the turbulent dynamic behavior of BLDC under load and no-load conditions, and in the second case, the speed shows the lowest rise time, stability, overshoot, and stability conditions, and performs at its best. The characteristics of the traditional PID controller that regulates the engine speed must be regulated online to achieve the use of intelligent technologies, and the adjustment is done online using the neural network. The results showed that this technology, or feature - online tuning - is the most effective and reliable of all.
This study presents a speed control design for switched reluctance motor (SRM) drive based on PID controller. The applications of Switched Reluctance Motors (SRMs) have being increased day by day, but this type of motors represents a highly nonlinear system, therefore there are a lot of difficulties in modeling and controlling them. We have proposed a non-linear mathematical model of a four phases 8/6 poles SRM then simulated it through Simulink/Matlab facilities. The whole control mechanism consists of a hysteresis current controller to minimize the torque ripple and a PID speed controller. The control design results are then validated in real-time by Simulink/Matlab software package.
A five-phase two-motor drive system with a series connection of stator windings and decoupled dynamic control is considered in the present paper. The two-motor drive system is supplied from a single five-phase Space Vector Pulse Width Modulation (S VPWM) Voltage Source Inverter (VSI) and controlled using a vector control scheme, provided that the stator windings are connected in series with appropriate phase transposition. The concept has been developed under the assumption that the inverter voltages are controlled in the stationary dq-reference frame. A fuzzy logic-based speed controller has been constructed and used to drive the two-motor in this work. The two-motor system, inverter system, and fuzzy controller models are implemented and tested using Simulink/Matlab facilities. 1be presented results show the validity of the model to do well for the sake of speed control in wider different operating conditions.