Designing fuzzy logic controllers for DC servomotors supported by fuzzy logic control development environment

In recent days, technology develops for the application in versatile fields. Mostly, soft computing techniques enhance the technological aspects in the field of engineering and medicine. In this paper, fuzzy logic-based controller is designed to support the surgeons in operation theater. At the time of surgical operation, the anatomical parameters are to be well controlled especially blood sugar and blood pressure. It provides intelligent control applied in the field of medicine. A fuzzy logic controller for mean arterial pressure (MAP) control is considered as limit for the depth of anesthesia. The fuzzy membership functions and the linguistic rules using fuzzy logic are developed for the design. The adaptive neuro-fuzzy inference system (ANFIS) has been applied for implementation and simulation of the controller. The performance of the fuzzy logic based PID controller is found excellent in computer simulations with respect to noise tolerance, tracking ability. Simulation result is shown that the exceptional regulation of blood pressure around set-point targets is realized with application of fuzzy logic controller.

Based on a low cost multi-switched inductor balancing circuit (MSIBC), a fuzzy logic (FL) controller is proposed to improve the balancing performances of lithium-ion battery packs instead of an existing proportional-integral (PI) controller. In the proposed FL controller, a cell’s open circuit voltages (OCVs) and their differences in the pack are used as the inputs, and the output of the FL controller is the balancing current. The FL controller for the MSIBC has the advantage of maintaining high balancing currents over the existing PI controller in almost the entire balancing process for different lithium battery types. As a result, the proposed FL controller takes a much shorter time to achieve battery pack balancing, and thus more pack capacity can be recovered. This will help to improve the pack performance in electric vehicles and extend the serving time of the battery pack.

High efficiency due to reduced losses, low maintenance and low rotor inertia of the brushless dc (BLDC) motor have increased the demand of BLDC motors in high power servo and robotic applications. Since fuzzy logic with human like but systematic property can convert the linguistic control rules based on expert knowledge into automatic control strategy, it can be well applied to control the systems with uncertain or unmodelled dynamics. In view of fuzzy logic merits, this paper proposes three types of fuzzy logic controllers for BLDC motor drive using advanced simulation model and presents a comparative study of performance specifications of classical PI and PID controllers and three fuzzy logic controllers. The three fuzzy logic controllers considered are PI-like fuzzy logic controller (PI-Like FLC), Hybrid fuzzy logic controller (HFLC) and integrated fuzzy logic controller (IFLC). The steady state and dynamic characteristics of speed and torque are effectively monitored and analyzed using the proposed model. The aim of fuzzy logic controllers is to obtain improved performance in terms of disturbance rejection or parameter variation than obtained using classical controllers. In the HFLC, the proportional term of the traditional PID controller is replaced with an incremental fuzzy logic controller. For the PI-Like FLC, the output of the controller is modified by a rule base with the error and change of error of the controlled variable as the inputs. The IFLC is constructed by using Fuzzy logic controller and PID controller. A performance comparison of classical and fuzzy logic controllers has been carried out by several simulations at different speeds and different load conditions.

Biodiesel reactor is the heart of biodiesel system. These reactors involve a highly complex set of chemical reactions and heat transfers. The high nonlinearity requires an efficient control algorithm to handle the variation of operational process parameters and the effect of process disturbances efficiently. In this paper, Fuzzy logic and Adaptive controllers are compared for advance microwave biodiesel reactor. The process control is complex and nonlinear, the Adaptive control have longer time and unreliability in dealing with the system parameters including temperature, microwave power, liquid flow rate as well as the prediction of chemical reaction. The proposed fuzzy logic control will provide precise temperature control and faster warm-up phase with quicker response to disturbances with minimal overshoot and undershoot where Adaptive control techniques can not meet these extra challenges. A closed loop fuzzy and adaptive controllers are used to automatically and continuously adjust the applied power of microwave reactor under different perturbations. Labview based software tool will be presented and used for measurement and control of the full system, with real time monitoring.

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.

This paper investigates new form of a hybrid model reference adaptive speed control (HMRASC) for vector controlled permanent magnet synchronous motor (PMSM) drive. In the proposed scheme, a direct fuzzy logic controller (FLC) is employed as the main controller, and model reference based proportional integral (PI) controller as the adaptation controller. The design and optimization of the FLC and the adaptation mechanism are presented. The efficiency and performance of the HMRASC is examined, by simulation at different operating conditions. The operating conditions studied include various step speed commands from standstill with nominal and changes in motor parameters and load torque. The results obtained are evaluated and compared with direct FLC without the adaptation mechanism. The results achieved show the superior of HMRASC over direct FLC especially for load disturbances and load inertia variations

Conventional control depends on the mathematical model of the plant being controlled. When this model is uncertain, intelligent controllers promise better performance. We aim in this paper to compare conventional control to fuzzy logic control (FLC) experimentally. This will be achieved by constructing a hardware station comprising a plant and implementing different control algorithms for the same load conditions or disturbances. FLC requires expertise knowledge of the process operation for FLC parameter setting, and the controller can be only as good as the expertise involved in the design. To make the controller less dependent on the quality of the expert knowledge, we investigate different adaptation schemes to compensate for this deficiency and propose a practical adaptive fuzzy logic controller (AFLC).

there is an increased need for analysing the effect of atmospheric variables on photovoltaic (PV) production and performance. The outputs from the different PV cells in different atmospheric conditions, such as irradiation and temperature , differ from each other evidencing knowledge deficiency in PV systems [14]. Maximum power point tracking (MPPT) methods are used to maximize the PV array output power by tracking continuously the maximum power point (MPP). Among all MPPT methods existing in the literature, perturb and observe (PO however, it presents drawbacks such as slow response speed, oscillation around the MPP in steady state, and even tracking in wrong way under rapidly changing atmospheric conditions. In order to allow a functioning around the optimal point Mopt, we have inserted a DC-DC converter (Buck–Boost) for a better matching between the PV and the load . This paper, we study the Maximum power point tracking using adaptive Intelligent fuzzy logic and conventional (P&O) control for stande-alone photovoltaic Array system .In particular, the performances of the controllers are analyzed under variation weather conditions with are constant temperature and variable irradiation. The proposed system is simulated by using MATLAB-SIMULINK. According to the results, fuzzy logic controller has shown better performance during the optimization.

This study explores the integration of the adaptive control approach with the Model Reference Adaptive Control (MRAC) method, with a view to examining the impact of this integration on the performance of dynamic systems. Adaptive control is highlighted as a versatile and robust methodology that enables systems to cope with uncertainties and adapt to rapidly changing conditions in their operating environments. As a branch of adaptive control, the MRAC method focuses on efficiently achieving desired performance objectives by adjusting system parameters based on a predefined reference model. The primary focus is on investigating how these methods can be applied to optimize the functionality of systems characterized by dynamic and variable nature, such as unmanned aerial vehicles. By addressing challenges related to uncertainties, disturbances, and adaptability, the integration of adaptive control with the MRAC method demonstrates significant potential in improving the robustness, responsiveness, and reliability of such systems. This research investigates the performance of adaptive techniques in enhancing the capabilities of modern control systems. The objective of the research is to assess the effectiveness of MRAC in terms of reference signal tracking. Simulation results demonstrate the ability of MRAC to maintain accurate tracking of the desired reference for key vehicle dynamics, including roll, pitch and yaw. The results demonstrate the robustness of the MRAC algorithm in attitude control, which may lead to support for sustainability issues such as energy consumption.

High performance position control can be obtained with dc servomotors and actuators by using efficient control strategies. For precise control of position, the control strategy employed should result in fast control of the output, with minimum overshoot and least steady state error. The advancement of control theory over the last 30 years resulted in a huge choice of control schemes, given a plant or a system to be controlled. Despite this, traditional PID (proportional integral and derivative) control scheme is the popular choice for implementation in industrial environment, as this scheme is simple and easy to implement and its design does not require an exact knowledge of the controlled plant dynamics. Fuzzy logic is recently finding wide popularity in various applications that include management, economics, medicine and process control systems. This paper presents the simulation and experimental results of the adaptive fuzzy logic control scheme proposed for a position control system, with cost-effective, real time implementation as the main objective. Traditional PID control and more recent fuzzy logic control schemes have been used for the studies. It is demonstrated both by simulation and experimental implementation on a prototype system that fuzzy logic control can provide better control, does not require mathematical modelling of the plant and yields better disturbance rejection properties.

The asynchronous machine has qualities that make it a preferred candidate for variable speed drives, but this simplicity is accompanied by great physical complexity due to the electromagnetic interactions between the stator and the rotor. This paper presented a comparison between fuzzy PI logic and PI traditional controllers based for the speed control of the asynchronous machine (ASM); the analysis was achieved in MATLAB/SIMULINK environment. At first, it described the ASM indirect vector control, which consists in eliminating the coupling between the armature and the inductor to reduce its operation comparable to that of a DC machine. In addition, it described the PI controller; the fuzzy logic controller that does not deal with a well-defined mathematical relationship, but uses inferences with multiple rules control, then, compared their performance. The results of all the controllers have been compared. The machine started at t = 0.1 s from zero speed to t = 0.4 s at no load, and at t = 5 s, a load torque of T = 10 N m was applied to observe the controllers’ performance. As for the comparison of the controllers’ speed response, it can be observed that the PI controller has the highest and that the fuzzy PI has the lowest overshoot of (rad/s). Besides, the settling time for the fuzzy PI is lower than that of PI. The results showed the effectiveness and robustness of the fuzzy control system compared to PI controller for indirect vector control of asynchronous machine.KeywordsAsynchronous machine (ASM)Indirect field-oriented control (IFOC)PI controllerFuzzy logic controllerVector control

Intelligent mobile robots move on uncertain grounds, thus requiring good navigation strategies for things like path tracking and obstacle avoidance. This research uses an Omni-drive mobile robot to autonomously approach given objectives in different situations encountered in static and dynamic environments. The paper compares two distinct controllers – fuzzy logic controller and neural network controller- that lead the mobile robot towards its destination without hitting obstacles. These are responsible for adjusting the linear and angular velocities of a mobile robot which makes adaptive navigation possible during real-time. The experimental results have depicted the adaptability of each controller as well as its efficiency especially when dealing with uncertainties involved with the mobile robot navigation system. By systematically evaluating and contrasting them, this study brings out the best performance between Fuzzy Logic and Neural Network Controllers regarding enhancing the autonomy and robustness of Mobile Robots. This research helps to advance knowledge in autonomous systems for practical applications, which will give rise to more efficient navigational techniques for mobile robots; thus, efficient systems that are autonomous become more reliable today. The results show that these controllers are effective in safely steering the robot from its starting point to a specified destination without hitting obstacles.

Braking system plays a major safety feature in any vehicle; it is considered as one of the key points in safe motoring. The main objective of a good braking system is to provide safe stopping, in addition to the fact that it provides repeatable stopping. Anti-lock Braking System (ABS) is one of the recently developed safety feature used in a vehicle in order to prevent wheels from locking. This article proposes an intelligent adaptive fuzzy logic (FL) genetic algorithm (GA) controller for the Anti-Lock Braking System (ABS) to control the braking of a vehicle by controlling the slip ratio based on quarter car model. The main objective of the proposed controller is to prevent wheels from locking, which is a major safety issue in any braking system for different road conditions. The proposed controller consists of a Fuzzy Logic (FL) Genetic Algorithm (GA) based controller. The Genetic Algorithm is utilized in tuning the membership functions of the Fuzzy logic controller, which is developed to calculate the amount of reduction of the applied torque due to applying brake based on the error of the measured slip ratio signal compared with the desired one. Then a Genetic Algorithm (GA) is utilized in tuning the Fuzzy Logic parameters, which are the center of the Gaussian membership function, and the spread (б). The simulation results of the proposed fuzzy logic genetic algorithm controller are compared with both the results of the Fuzzy logic controller and the PD controller developed in previous work of the authors. The simulation results present many advantages of using Fuzzy Logic Genetic Algorithm based controller over both the Fuzzy Logic and the PD controllers, and the simulation is carried out at different road conditions, namely dry, wet and icy road surface.

Model reference adaptive fuzzy control (MRAFC) is proposed to control the active power filter (APF) and model the APF system caused by nonlinear characteristics, variable parameters and external interferences. T-S fuzzy theory is utilized on the approximation of nonlinear model and the current tracking control. APF system based on MRAFC can be guaranteed to be asymptotical stable. Simulation results are included to confirm the effectiveness of the proposed controller and comparisons with APF system based on model reference adaptive control (MRAC) demonstrate that MRAFC method is capable of reducing the modeling error and tracking reference current signal accurately. Moreover, APF based on the proposed method shows the dynamic and static control performance of stronger robustness, better suitability and higher accuracy than the one based on MRAC modus.

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.