Design and Analysis Models With PID and PID Fuzzy Controllers for Six-Phase Drive

Fuzzy PID control is a process control technique combining the advantages of fuzzy logic and PID control. It maintains precise control over processes by combining fuzzy logic and proportional-integral-derivative (PID) controllers. With this method, it is possible to achieve higher levels of accuracy and stability in controlling process variables such as temperature, pressure, flow rate, and position. This makes Fuzzy PID control an ideal cordial application requiring precise process control. Fuzzy PID control uses the PID parameter tuning experience to make the fuzzy controller automatically tune its parameters, giving PID control a better control effect. In this paper, a fuzzy PID controller is designed to control the temperature of an electric boiler. The control effects of PID control, fuzzy control, and fuzzy PID control are compared by simulation analysis. The simulation results show that the unclear PID control effect is ideal and has a good application prospect.

This paper proposes a propulsion control algorithm considering End-effect of a linear induction motor (LIM) used in a magnetic levitation train. Indirect field oriented control (IFOC) technique is one of the popular control techniques widely applied to LIM drive control. The main idea of IFOC in a LIM is the decoupling of the flux and torque. Therefore, IFOC has a higher dynamic characteristic than the scalar control [1]. The LIM applied to the magnetic levitation trains should control the thrust force and also take into account the normal force for levitation stabilization. When the LIM is driven, normal force is inevitable. The normal force is a function of the speed, the slip frequency and the current of the train. In the rotary type induction motor, the normal force is canceled due to its symmetrical structure. Therefore, IM could operate in a low slip and high efficiency range. However, in the case of LIM, a high attraction force is generated in a low slip region, which cause the instability of the levitation system. Therefore, the propulsion control for magnetic levitation trains must operate in a higher slip region than the slip used in conventional IFOC. In this paper, the slip characteristics of the LIM are analyzed to improve the efficiency and safety of the magnetic levitation train by FEM analysis. A constant slip frequency propulsion control algorithm is proposed for operation in the analyzed slip region. The proposed algorithm can be controlled by separating magnetic flux and thrust from IFOC. The proposed algorithm is verified in 250 kW test bench. The results of FEM analysis according to speed and slip at constant voltage condition are shown on Fig. 1. The dotted lines of Indirect Field Oriented Control(IFOC) and Constant Slip Indirect Field Oriented Control(CSIFOC) are shown on the figure. The slip of the IFOC is low and that leads to operate near the maximum thrust and efficiency. However, the normal force of LIM is also increase, which could not be allowed in maglev application. On the other hand, when CSIFOC is performed considering normal force, the slip is higher than the conventional algorithm. Although the CSIFOC is not operated in maximum thrust slip, the normal force could be greatly reduced. Fig.2 (a) shows the CSIFOC algorithm considering the end-effect of the linear induction motor. In general IFOC, the d-axis current reference is calculated through the magnetic flux reference value, and the q-axis is calculated based on thrust. However, the CSIFOC for the LIM generates the d-axis and q-axis current references along with the slip frequency and thrust. Fig. 2 (b) shows the waveform of the experimental results based on the general IFOC and the proposed algorithm. Both IFOC and CSIFOC were tested with the same rate pattern of the same load. It is confirmed that CSIVC has larger slip than conventional IFOC and low normal force in all regions. In this paper, a constant slip frequency IFOC algorithm is proposed considering the end effect of LIM used for magnetic levitation trains. The efficiency, thrust and normal force according to the speed and slip through the FEM analysis of the LIM were analyzed and the slip condition suitable for the magnetic levitation train is proposed. In addition, a current reference generation algorithm based on the thrust and slip is proposed in the IFOC considering the end effect to operate under the proposed slip condition. In the full paper, the detailed formulas of the proposed algorithm are developed and will be verified through simulation and experimental results.

In CNC lathe servo feed system, many parameters are uncertain, so it is difficult to establish accurate mathematical model of the feed system, and parameters of conventional PID controller can not change the parameters to adjust with the controlled object. Fuzzy PID controller, which has the advantages of conventional PID control and fuzzy control, can solve these problems very well. So we can make it applied in CNC lathe servo feed system. This paper has made a simulation of fuzzy self-turning PID controller in the MATLAB environment. The simulation shows that the fuzzy self-turning PID controller has a better control capability and a higher stability, and it improved the servo feed system dynamic performance.

Use of induction motor (IM) at it's highest efficiency is a big demanding task because of their complex mathematical model and non linear features during saturation. These elements make the control of IM strenuous and call for the use of high performance control algorithm such as field orientation control. This paper introduces a speed controller, particularly at minimum speeds by utilizing Fuzzy logic controller (FLC) for indirect field-oriented control (IFOC) of induction motor (IM) drives fed by a four-switch three-phase (FSTP) inverter. From suggested viewpoint, the IM drive is used with a FSTP inverter as an alternative for the conventional six-switch three-phase (SSTP) inverter. Utter IFOC programme includes the FLC of IM drives with the suggested FSTP inverter is raised in MATLAB/Simulink. By considering the good dynamic performance, cost minimization and low total harmonic distortion (THD), FSTP inverters are comparable with SSTP inverters. The dynamic performance and robustness of proposed FLC with the FSTP inverter fed IM drive is verified with PI controller. It is established that the suggested FLC is further powerful than the PI controller under speed tracking and load disturbances

The paper deals with an enhanced MATLAB Simulink model of a fuzzy-controlled system for a symmetrical six-phase motor supplied with modified PD-I and PI-D controllers and analyzes its performance and transients at special form speed references. The Simulink model of the motor was developed with the actual parameters of a six-phase motor having a displacement of phase windings by 60 electrical degrees. The model of the six-phase motor is based on the d-q mathematical model and indirect field-oriented control. A fuzzy controller with per unit input and output membership functions and 49 rules is designed. The simulation results are presented and analyzed. The results with modified PD-I and PI-D indicate the robustness of the modelled system with a six-phase motor against disturbances and follow the reference input with great accuracy. The fuzzy controlled system's response to complex reference input indicates a small speed delay at the beginning of the process and accurate following of the response input with an error smaller than 0.75 % during the application of load torque disturbance.

The paper is concerned with two types of fuzzy PID controllers, the rule-based fuzzy PID controller and the learning fuzzy PID controller. Initially, the development of the rule-based fuzzy PID controller is studied, and subsequently, the development of the learning fuzzy PID controller is outlined. For the rule-based fuzzy PID controller, the PID gains are firstly tuned using a conventional tuning method. The rule buffer of the rule-based fuzzy then readjusts the PID gains during the system operation. The learning fuzzy PID controller not only readjusts the PID gains, it also creates and updates the rules in the rule-buffer automatically during the application of the learning fuzzy PID controller to the process. The rule-based fuzzy PID controller and the learning fuzzy PID controller are both applied to a non-linear revolute-joint robot-arm for path tracking experiments. The results of the experiments for the rule-based fuzzy PID controller and the learning fuzzy controller are compared with a conventional PID controller in order to evaluate the results.

Aiming at the limitation of traditional PID algorithm in the field of control, the fuzzy control theory and traditional PID control algorithm are combined to realize parameter self-tuning function, a fuzzy self-tuning PID controller, and this algorithm is analyzed in-depth study. It provides a new solution of modern industrial control problem that combining with fuzzy control technology and PID control technology. The FPGA has high integration, short development cycle, the characteristics of strong reusability; fuzzy PID controller has a good practical value and optimistic application prospect based on FPGA.

Fuzzy logic control based on the Takagi–Sugeno inference method has been applied for the yeast alcoholic fermentation running in a continuous-time biochemical reactor in this paper. A type-1 fuzzy PID controller was designed to temperature control in a biochemical reactor. The fuzzy PID controller was also designed using type-2 fuzzy sets. The advantage of the fuzzy control is that it can be used very successfully for control of strongly non-linear processes and processes that are difficult to model because of complicated reaction kinetics. Obtained simulation results confirm this fact. The disadvantage of the fuzzy control design lies in the time-consuming tuning of controllers. The subtractive clustering method was used to identify the rule base. This approach was chosen to minimize the number of rules of the designed fuzzy logic controllers and to simplify the fuzzy controller design. Simulation results confirm that fuzzy PID controllers can assure better performance than conventional PID controllers. Yeast alcoholic fermentation is an exothermic process and the cooling is necessary to maintain the optimal temperature in the reactor. Fuzzy PID control reduces coolant consumption in comparison with conventional PID control and so it assures energy-efficient control of continuous-time alcoholic fermentation. Using type-2 fuzzy sets in the fuzzy PID controller design is very promising as the type-2 fuzzy controller offers even better results than the type-1 fuzzy controller.

Research on adaptive deformable mirror technology for voice coil actuators is an important trend in the development of large ground-based telescopes. A voice coil adaptive deformable mirror contains a large number of actuators, and there are problems with structural coupling and large temperature increases in their internal coils. Additionally, parameters of the traditional proportional integral derivative control cannot be adjusted in real-time to adapt to system changes. These problems can be addressed by introducing fuzzy control methods. A table lookup method is adopted to replace real-time calculations of the regular fuzzy controller during the control process, and a prototype platform has been established to verify the effectiveness and robustness of this process. Experimental tests compare the control performance of traditional and fuzzy proportional integral derivative controllers, showing that, in system step response tests, the fuzzy control system reduces rise time by 20.25%, decreases overshoot by 78.24%, and shortens settling time by 67.59%. In disturbance rejection experiments, fuzzy control achieves a 46.09% reduction in the maximum deviation, indicating stronger robustness. The fuzzy proportional integral derivative controller, based on table lookup, outperforms the standard controller significantly, showing excellent potential for enhancing the dynamic performance and disturbance rejection capability of the voice coil motor actuator system.

To improve tracking precision and increase the utilization of solar energy, the solar automatic tracking fuzzy PID control system based on ARM is designed. Embedded microcontroller 32-bit ARM processor is the core. A kind of mixed tracking mode is adopted, and it combines photoelectric tracking mode and solar trajectory tracking mode. In the solar trajectory tracking mode, the fuzzy PID segmented tracking control method is put forward. The fuzzy control and PID control are combined to control the system. If the system's angle deviation is large, PID control is adopted, else fuzzy control is adopted. And the controller can be automatically switched control mode according to the size of angle deviation. Matlab simulation results indicate that the solar automatic tracking system with fuzzy PID control has a good dynamic characteristics and high tracking accuracy, effectively improves the photoelectric conversion efficiency.

As an important carrier of intelligent logistics, AGV can effectively reduce the labor cost of factories and warehouses. With the comprehensive promotion of “smart manufacturing”, the manufacturing industry has a stronger demand for automated AGV logistics systems. This paper firstly analyzes the principle of fuzzy PID controller and establishes its kinematic model to obtain the intrinsic relationship between the motion form and the speed of each driving wheel. Secondly, through the data measured and fed back by the fuzzy PID control system, the AGV driving angle deviation and position deviation are used as the input of the fuzzy controller; its fuzzy deviation correction technology is applied to realize the path correction of the AGV and ensure the stable operation of the AGV through the cooperative motion control technology. Finally, MATLAB software establishes the joint simulation model of the AGV fuzzy PID control system. The results show that through the two algorithms of PID control and fuzzy PID control correction test, the fuzzy PID controller system corrects the deflection efficiency by 47.41%; in the kinematic test, the path tracking efficiency is increased by 30.76%, the distance correction efficiency is increased by 69.56%, the angle correction efficiency is increased by 69.56%, and the average performance is increased by 53.44%. Through the two comparisons, it is concluded that the fuzzy controller designed in this paper has the advantages of fast response speed, good stability, and strong deflection correction performance, which substantially improves the speed and accuracy of intelligent search of intelligent vehicles.

In research applications is very important to have a flexible automation structure for different plant conditions with a large flow of information. To satisfy real-time requirements a communication technique based in fieldbus network was used to interconnect devices in an intelligent distributed control system. Using this structure, an experimental tuning for fuzzy-PID controller based in scaling gains from a well-tuned conventional PID controller was studied in this work. Due to difficulties involving fuzzy controllers tuning (expert knowledge, adjustments in rules, membership functions and gains) it is recommended to develop a tuning procedure, which will greatly reduce the time of design. From the gains of a well-tuned conventional PID controller a hybrid velocity/position fuzzy-PID controller was designed. Experimental tests were carried out for level control in a tank system under different disturbances in set point values. Tuning performance of different conventional and fuzzy-PID structures were evaluated with respect to their functional behaviors (overshoot, rise time and ISE criterion). Results obtained showed that fuzzy controller structure has a great potential for application even when it is possible to implement a conventional PID controller. It is also observed that there is saturation of the manipulated variable for the conventional PID controller which does not occur in the fuzzy-PID controller.

In the process of frozen food production, its refrigeration system is a typical large inertia, nonlinear, strongly coupled system, which is prone to large overshooting during operation, resulting in cost increase and quality control problems. This paper solves the problem of large overshoot in the digital PID control process by designing a fuzzy control, and designs a practical fuzzy controller so that the design method of the fuzzy PID controller can be realized in the simulation and actual control of the freezing system. A genetic algorithm optimization scheme is also designed for the fuzzy PID control system to optimize the fuzzy controller’s affiliation function, control rules, as well as proportionality and quantization factors, to enhance the model’s dynamic characteristics and robustness. Design of food quick-freezing simulation experiments and use the model in the refrigeration system control in practice, compare the performance of this model with the PID, fuzzy PID model, and found that the temperature difference set value of 4 ℃ and 3 ℃, respectively, the parameters of the traditional PID controller in the commissioning stage of the system after the end of the rectification to remain fixed, which makes the actual quality of the cooling water temperature difference control degradation. And the fuzzy genetic PID controller still has good control quality. In this paper, the model curve converges rapidly, the overshoot is only 3.9%, and the regulation time is only 450M seconds. The designed method of the study is feasible and has high industrial application value.

The speed control of induction motors can be divided into two distinct strategies, depending on the type of dynamics that is required: scalar control (static control of the torque) and vector control (dynamic control of the torque) (Blaabjerg et al. 2005; Bose, 2000, 2006; Filote et al. 2009; Holtz, 2002; Leonhard, 1990; Umanand & Bhat, 1995; Vas, 1998). Since the couple and flux levels depend on the motor behavior to frequency and voltage applied to it, maintaining the flux constant is strongly required in scalar control (V/f control). Despite the simplicity and the low cost implementation of the control method, it still presents the disadvantage of poor torque dynamics. Vector control reassesses one of the advantages of direct current (dc) drives, which is the separation of speed and couple loops. According to its structure and to its own functioning principle, the dc drive is naturally field oriented, hence, the separation of the speed and current loops. In the case of vector controllers of induction motor, the magnitude and phase stator current are controlled in accordance with the flux vector. There are three vector control strategies according to the type of drive-controlled flux: stator flux, rotor flux and air gap flux. We can determine the magnitude and position of the rotor flux by using flux sensors (direct field-oriented control), estimators or observers (indirect field-oriented control) and by measuring some electrical and mechanical measurable states of electrical drives induction motor systems. When the rotor flux estimation is performed after measuring only the electrical measurable states (voltages and stator currents), in this case we have a sensorless vector control system. The implementation of the direct field-oriented control requires the measurement or calculation of the flux space vector (magnitude and position). The measurement of the air gap flux requires the use of some Hall sensors (Umanand & Bhat, 1995), which requires specially constructed induction motor. On one hand, they are very sensitive to temperature and mechanical vibrations. The flux signal is highly distorted by slot harmonics (Kreindler, 1994), whose spectrum and amplitude depend on the rotor speed (hence, the difficulty to be filtered). The implementation of the indirect field-oriented control requires the identification of rotor flux instantaneous position, and the calculus of stator current prescribed value in sensorless vector control system (Gadue et. al. 2009; Griva et al. 1997; Lascu et al. 2004). The

PID and fuzzy PID controller are applied into the wind turbine with nonlinear mathematical model. PID control has simple principle and its parameters setting are rather easy. Fuzzy control need not to establish the mathematical of the control system and has strong robustness. Fuzzy PID control is combined with PID control and fuzzy control. The advantages of fuzzy PID control are simple, easy in setting parameters and strong robustness. Fuzzy PID controller combines with closed-loop optimal fuzzy reasoning (COFR), which can effectively improve the robustness, when the robustness is special requirement. MATLAB software is used for simulations, results display that fuzzy PID controller which combines with COFR has better performances than PID controller when errors exist, especially robustness performance.