The PID control system is widely used for industrial machine control processes. The success of PID control is determined by tuning PID parameters. In PID control the tuning is carried out offline without taking into account changes that occur in the plant and the disturbances that arise. This study aims to optimize PID parameters online by taking into account the changes that occur in the plant and the disturbances that arise using fuzzy logic-based controls and tested on a BLDC motor which is a non-linear system. Set PID parameters with fuzzy logic using a combination of 49 if-then rules. To set proper PID parameters in real time, a two-level control system was built. The first level to define PID parameters by finding the minimum and maximum values of  kp, ki and kd by the reaction method curve. The second level is designing the Fuzzy system to automatically set the PID parameters, then formulating a combination of 49 fuzzy if-then rules to get the value kp, ki, kd, error and change in delta error value. Testing of set point changes at BLDC Motor loads with no load and 0.5kg load and changes in speed get a response from the PID control system with an average value of 0.025 seconds rise time, 0.1625 seconds preset time, and 15.98% overshoot. While the Fuzzy PID control produces an average rise time value of 0.0025 seconds, preset time 0.057 seconds, overshoot of 5.42%.

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Autotuning Fuzzy PID Controller for Speed Control of BLDC Motor

In this paper, we are presenting a survey of research works dealing with Type-2 fuzzy logic controllers designed using optimization algorithms inspired on natural phenomena. Also, in this review, we analyze the most popular optimization methods used to find the important parameters on Type-1 and Type-2 fuzzy logic controllers to improve on previously obtained results. To this end have included a summary of the results obtained from the web of science database to observe the recent trend of using optimization methods in the area of optimal type-2 fuzzy logic control design. Also, we have made a comparison among countries of the network of researchers using optimization methods to analyze the distribution and impact of the papers.

A survey of Type-2 fuzzy logic controller design using nature inspired optimization

In this work, the sensorless speed control of a brushless direct current motor utilizing a neural network is presented. This control is done using a two-layer neural network that uses the backpropagation algorithm for training. The values provided by a Proportional, Integral, and Derivative (PID) control to this type of motor are used to train the network. From this PID control, the velocity values and their corresponding signal control (u) are recovered for different values of load pairs. Five different values of load pairs were used to consider the entire working range of the motor to be controlled. After carrying out the training, it was observed that the proposed network could hold constant load pairs, as well as variables. Several tests were carried out at the simulation level, which showed that control based on neural networks is robust. Finally, it is worth mentioning that this control strategy can be realized without the need for a speed sensor.

https://www.mdpi.com/2297-8747/25/3/57/pdf

Sensorless Speed Tracking of a Brushless DC Motor Using a Neural Network

In this article, an improved hybrid particle swarm optimization (IHPSO) algorithm is proposed to solve the optimization problem of controller parameters in the design of a permanent magnet synchronous motor (PMSM) servo system. The proposed algorithm presents the directional mutation operation to the particles, which fixes the position of some particular particles so as to enhance the searching ability to some remote regions. In order to cooperate with directional mutation operation, the updating formula of particles velocity is ulteriorly improved. Then, the proposed IHPSO algorithm is adopted to optimize parameters of the designed controller. A simulation and an experimental platform of the PMSM servo system are designed using a biological intelligence controller based on hormone regulation for the speed control and feedforward compensation for the position controller, where IHPSO is applied to the parameter optimization for speed and position controllers, which validate the effectiveness of the IHPSO algorithm and the designed control system. 

Design of Permanent Magnet Synchronous Motor Servo System Based on Improved Particle Swarm Optimization

Abstract Aiming at the problems of the poor adaptive ability in the current control methods for brushless DC motor, an adaptive fuzzy proportional integral derivative controller (AFPID) is proposed to realize the better control performance of speed for brushless DC motor. AFPID includes a conventional PID controller (C-PID) and PI + PD architectures with a configurable fuzzy logic controller (C-PID-FLC). The FLC in C-PID-FLC consists of two fuzzy inference engines, one is used to self-tune the parameters for PI control, the other is for the scaling factor self-tuning of PI control parameters. The PD structure in C-PID-FLC is mainly to reduce oscillation, overcome overshoot and speed up system response while effectively eliminating static errors. When the system reaches a certain stable state of rotation, AFPID adjusts the C-PID controller ground on the speed error, which saves control costs under the premise of ensuring control performance. AFPID adaptively realizes the respective advantages of C-PID and C-PID-FLC. Compared with other control methods, the merits of the proposed controller are highlighted. The results show that the AFPID controller has a better control performance regardless of changes in load disturbance and parameter variations. And through the change of mechanical parameters of brushless DC motor, the sensitivity of AFPID is analyzed. 

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An adaptive fuzzy PID controller for speed control of brushless direct current motor

This work focuses on adaptive controller design using fuzzy Logic (FL) controller to get better dynamic performance under non-linearities and parameter uncertainties. In the proposed controller, the membership function (MF) parameters are tuned in line with the parameter variations of the motor. The steady state, regulatory and servo response of the proposed controller are analysed to highlight the merits of the controller. The simulation results indicate that the proposed controller possesses good tracking capability and faster response time in comparison with conventional schemes.

Adaptive MF tuned fuzzy logic speed controller for BLDC motor drive using ANN and PSO technique

/pdf/treatment-of-textile-industry-wastewater-by-using-solar-2ga9t05m.pdf

Amirthalingam Ramya

Papers

Adaptive MF tuned fuzzy logic speed controller for BLDC motor drive using ANN and PSO technique

Treatment of Textile Industry Wastewater by Using Solar Photocatalytic Process: Experimental Studies & Statistical Optimization