Fuzzy logic speed control and current-harmonic reduction in permanent-magnet brushless AC drives

Performance evaluation of a brushless DC (BLDC) motor with conventional PI and Fuzzy Logic based speed controller is presented in this paper. The BLDC motor with non-sinusoidal (trapezoidal) back-electromotive force has been analyzed with both the speed and current controllers. It is the always preferred to have a drive with faster and smoother speed response and reduced ripples in current and torque. The controlling schemes for BLDCM using its Back EMF have been conventionally used into many applications. In this paper, a simple control scheme has been implemented which does not need to know the back emf or shape function and even no complicated calculations. In order to solve the problems associated with conventional PI speed controller, Fuzzy logic speed controller is proposed to reduce starting current, eliminate overshoot in the torque and fast speed response. It is simple in design and eliminates the complex mathematical computation. The effectiveness of proposed system has been validated by simulation results and improved performance of controlling BLDCM. The proposed algorithm gives robust control. The robustness of the proposed algorithm is demonstrated through the MATLAB simulation.

In this paper, temperature monitoring of sterilizing equipment system was established with the help of fuzzy and self tuning Adaptive fuzzy logic controller designed in Lab VIEW software. It combines the advantages of both fuzzy logic and self tuning Adaptive fuzzy logic controller. The implementation attempts to rectify the errors between the measured value and the set point which helps to achieve efficient temperature control. The Adaptive fuzzy controller uses defined rules to control the system based on the current values of input variables and temperature errors. The simulation results presented in order to evaluate the proposed method. The result shows that self tuning Adaptive fuzzy logic controller was tolerant to disturbance and the temperature control is most accurate.

This paper presents a theoretical study on a model-reference adaptive fuzzy logic controller for vector-controlled permanent magnet AC servomotor drives. In the proposed system, fuzzy logic is used to implement the direct controller as well as the adaptation mechanism. The operation of the direct fuzzy controller and the fuzzy logic based adaptation mechanism is studied. The control performance of the adaptive fuzzy controller is evaluated by simulation for various operating conditions. The results are compared with that provided by a nonadaptive fuzzy controller. The implementation of proposed adaptive fuzzy controller is also discussed.

In this research study, direct torque and flux control of an Induction Motor Drive (IMD) using Sliding-Mode Speed Controllers (SMSC) and Fuzzy-Logic Speed Controller (FLSC) are presented to achieve high performance of stator current, electromagnetic torque and motor speed in both transient as well as in steady-state conditions. The PI Speed Controller (PISC) gives good steady-state performance, but poor dynamic response. Therefore, the SMSC is considered to achieve continuous control of motor speed and also electromagnetic torque. Furthermore, FLSC is considered to get high performance, dynamic tracking and speed accuracy at various load torque conditions. The performances of each speed controller techniques are tested under various load toque and speed condition for its robustness. A detailed comparison of different control approaches are carried out in a MATALB/Simulink environment at different operating conditions, such as forward and reversal motoring with no-load, load, sudden change in speed and sudden zero speed.

This paper presents simplified fuzzy logic speed control (FLSC) for vector controlled permanent magnet synchronous motor (PMSM) drives. The fuzzy logic controller is a multi-parameter controller where the performance depends upon the chosen shape of membership function (MF), rule base and scaling factor. In this paper, rule base is viewed as a control strategy to design the simplified FLSC. The FLSC simplification is designed by rule base simplification through two diferrent approaches. The first approach implements the rule simplification by reducing the number of MF used and adjusting the arrangement of chosen MFs for each fuzzy variable. The second approach obtains the rule simplification by eliminating the rules that are infrequently fired by the PMSM drives while maintaining the dominant rules. In doing so, the standard 49 rules is simplified become 9 rules by first approach while the other approach produces 7 rules. The efficacies of the FLSC simplifications are investigated based on comparative studies of speed performances with the standard FLSC which consisting of 49 rules. The standard FLSC is designed based on common criteria from numerous literatures. The mathematical model of the PMSM drive system is simulated in MATLAB environment with Simulink and Fuzzy Logic Toolbox. The simulation results are validated with the experimental results for various operating conditions, load disturbances and inertia variations. The simulation and experimental results showed that the simplified FLSCs obtain nearly equivalent performance with the standard FLSC, thus indicating the effectiveness of the rule simplification

The present paper describes the development of a new adaptive fuzzy logic controller (AFLC), which can directly employ an adaptive fuzzy/neuro/neuro-fuzzy function approximator to develop a desired controller, based on an ideal controller input-outpu

In this paper, a fault diagnosis method is presented in order to detection the inter-turn short circuit fault in permanent magnet synchronous motor (PMSM) based on the comparative analysis of two types of controllers. This study consists in the sliding mode control (SMC) of the (PMSM) and the two controllers (fuzzy logic controller (FLC) and Adaptive fuzzy logic controller (AFLC) by taking account of the presence of inter-turn short circuit fault. This study aims to select the most robust controller against the stator faults, load torque variation and reversing rotation speed. Tests regarding these two controllers associated with the sliding mode control were carried on a PMSM 5KW under to different loads, speed and severity degrees of failures for a functioning in healthy and degraded state. The obtained simulation results confirm the superiority and robustness of AFLC-SMC (insensitive to the load torque and the rotation direction, faster response time, decrease the oscillations, and minimization of THD) compared to FLC-SMC.

An adaptive fuzzy controller has been designed to develop a high-performance fault-tolerant SRM drive. The fuzzy controller continuously adapts its properties to regulate the machine torque as desired by the drive system even under fault conditions. The adaptation of the fuzzy membership functions results in extended conduction period and increased peak current of the healthy phases to deliver the commanded torque, as much as possible. The adaptive fuzzy controller provides smooth torque output with minimum ripple, even under fault conditions, yielding a high-performance SRM drive with fault-tolerant capability.

Harmony search algorithm and Lyapunov theory based hybrid adaptive fuzzy controller for temperature control of air heater system with transport-delay

This paper presents a fuzzy logic controller for the closed loop control of BLDC motor. The fuzzy logic controller is developed using a fuzzy logic toolbox in MATLAB. Also here a single current sensor technique is proposed for closed loop current control. For closed loop current control of BLDC motor, the motor phase currents are measured using current sensors. These sensors are expensive and the use of different current sensors can cause undesirable imbalance in phase currents due to differences in current sensor sensitivities. These drawbacks can be avoided by using a single current sensor placed on the DC link. Here an algorithm is presented to obtain the phase current values from the DC link current. The proposed system was simulated using MATLAB/Simulink, with PI and fuzzy logic speed controllers. The simulation result shows that, the fuzzy logic controller gives better performance than PI controller. The hardware implementation of the fuzzy logic controller is done using PIC16F877A. The hardware results show that the fuzzy logic controller gives a smooth speed control.

Direct Torque Control (DTC) technique is one of the most effective strategies for an electric machines. It aims to provide a decoupled control of torque and flux via a simple command structure. The aim of this paper is the study of the problems that can affect the operation of the speed loop for Induction Motor Open-End Winding (IM-OEW) through failures, especially broken bars fault, its impact on the control and operation of the motor. This paper presents a comparative study of the most powerful speed controllers for motor: proportional plus integral (PI) controller and a fuzzy logic (FL) controller. The awaited goal is to evaluate the speed controller is capable to improve the performance of the machine under faulty conditions. The obtained results proved the effectiveness of the DTC strategy with a fast dynamic, better load disturbances rejection and better tracking performance without overshoot when the FL speed controller employed, which ensures robust speed control under broken bars fault.

In this paper, real-type tabu search (RTS) is proposed, which adaptively changes the range of the neighbor solution to be searched each iteration according to an objective function. We design-the adaptive fuzzy logic controller (AFLC) for speed control of DC servomotor using RTS and neural network (NN). In this paper, the operation of the AFLC for speed control of a DC servomotor is composed of two steps. The first step is for optimizing the weighting value of NN and the initial value of input/output gain needed in real-time adjusting input/output gain of a fuzzy logic controller using a NN using the improved RTS. The second step is for real-time changing of the input/output gain of FLC using NN. We apply the proposed AFLC to the speed control of an actual DC servomotor system. Experimental results show that AFLC has the better control performance than a PI controller in terms of settling time, rise time and overshoot.

This paper demonstrates tracking control of a class of feedback linearizable unknown nonlinear dynamical systems, such as a robotic systems, using a discrete-time fuzzy logic controller (FLC). Designing a discrete-time FLC is significant because almost all FLC's are implemented on digital computers. A repeatable design algorithm and a stability proof are examined for an adaptive fuzzy logic controller that uses fuzzy basis functions based on the fuzzy system, unlike most standard adaptive control approaches which use basis vectors depending on the unknown plant. An /spl epsiv/-modification sort of approach to adapt the fuzzy system parameters is examined. Using this adaptive fuzzy logic controller, uniform ultimate boundedness of the closed-loop signals is presented and that the controller achieves tracking. In fact, the fuzzy system designed is a model-free universal fuzzy controller that works for any system in the given class of systems.

Attempts are being made to enhance the drive performance by intelligent control using fuzzy logic (FL) and neural network techniques. One of the frequently discussed applications of artificial intelligence in control is the replacement of a standard proportional plus integral (PI) speed controller with an FL or artificial neural network (ANN) speed controller. Regardless of all the work, it appears that a thorough comparison of the drive behavior under PI, FL, and ANN speed control is necessary. This article attempts to compare PI, fuzzy, and ANN controllers that are implemented in an embedded system for closed-loop speed control of DC drive fed by a buck-type DC–DC power converter. The PI controller is designed based on the small signal modeling of the system. The PI-like fuzzy controller structure is considered for comparison. Two ANN controllers are designed. One controller uses training data obtained from the simulation of a fuzzy controller and the other uses training data from the simulation of a PI controller. The performance of the controllers is studied for a variety of operating conditions, such as step change in speed command and step change in load torque. The parameters selected for the comparison are the steady-state error and the rise time of the response. It is shown that ANN speed controllers provide a superior speed response in terms of rise time and the steady-state error compared to PI and FL controllers. This advantage arises from the fact that the neural network has the property of generalization and the control surface of the neural controller is smooth. The designed neural network controller is simple, with three neurons only, and so it is best suited for embedded system implementation. It is also found that the ANN controller trained with the training data from a PI controller has a better response compared to the ANN controller trained with data from a fuzzy controller.

This study presents a novel optimization algorithm known as the Energy Valley Optimizer Approach (EVOA) designed to effectively develop six optimal adaptive fuzzy logic controllers (AFLCs) comprising 30 parameters for a grid-tied doubly fed induction generator (DFIG) utilized in wind power plants (WPP). The primary objective of implementing EVOA-based AFLCs is to maximize power extraction from the DFIG in wind energy applications while simultaneously improving dynamic response and minimizing errors during operation. The performance of the EVOA-based AFLCs is thoroughly investigated and benchmarked against alternative optimization techniques, specifically chaotic billiards optimization (C-BO), genetic algorithms (GA), and marine predator algorithm (MPA)-based optimal proportional-integral (PI) controllers. This comparative analysis is crucial in establishing the efficacy of the proposed method. To validate the proposed approach, experimental assessments are conducted using the DSpace DS1104 control board, allowing for real-time application of the control strategies. The results indicate that the EVOA-AFLCs outperform the C-BO-based AFLCs, GA-based AFLCs, and MPA-based optimal PIs in several key performance metrics. Notably, the EVOA-AFLCs exhibit rapid temporal response, a high rate of convergence, reduced peak overshoot, diminished undershoot, and significantly lower steady-state error. The EVOA-AFLC outperforms the C-BO-AFLC and GA-AFLC in terms of efficiency, transient responses, and oscillations. In comparison to the MPA-PI, it improves speed tracking by 86.3%, the GA-AFLC by 56.36%, and the C-BO by 39.3%. Moreover, integral absolute error (IAE) for each controller has been calculated to validate the system wind turbine performance. The EVOA-AFLC outperforms other approaches significantly, achieving a 71.2% reduction in average integral absolute errors compared to the GA-AFLC, 24.4% compared to the C-BO-AFLC, and an impressive 84% compared to the MPA-PI. These findings underscore the potential of the EVOA as a robust and effective optimization tool for enhancing the performance of adaptive fuzzy logic controllers in DFIG-based wind power systems.