Self-tuning Fuzzy Logic Controller Research Articles

Self fuzzy-genetic approach for boosting photovoltaic efficiency

The generation of power in Photovoltaic systems is reduced when they operate far from their maximum power point. For optimal operation, it is essential to continuously track the maximum power point of the PV solar array. However, identifying the maximum power point is a challenge due to the nonlinear relationship of electrical characteristics of PV panels with external factors. To address this issue, we present a novel design approach for a self-organizing, self-tuning fuzzy logic controller, applied to the problem of maximum power point tracking in photovoltaic systems. We outline the basic structure of the fuzzy logic controller and address the design problems typically associated with conventional trial-and-error schemes. We also discuss the suitability of the genetic algorithm optimization technique for determining and optimizing the fuzzy logic controller design. In our proposed approach, we translate the normalization factors, membership function parameters, and controller policy into bit-strings, which are then processed by the genetic algorithm to find a near-optimal solution. To achieve high dynamic performance, we choose a particular objective function as a performance index. We compare our approach with two variants of the maximum power point algorithm, one based on genetic algorithms and the other based on fuzzy logic, as well as with the methods described in references [34] and [35], in order to evaluate its effectiveness.

Enhancing Urban Mobility with Self-Tuning Fuzzy Logic Controllers for Power-Assisted Bicycles in Smart Cities.

In smart cities, bicycle-sharing systems have become an essential component of the transportation services available in major urban centers around the globe. Due to environmental sustainability, research on the power-assisted control of electric bikes has attracted much attention. Recently, fuzzy logic controllers (FLCs) have been successfully applied to such systems. However, most existing FLC approaches have a fixed fuzzy rule base and cannot adapt to environmental changes, such as different riders and roads. In this paper, a modified FLC, named self-tuning FLC (STFLC), is proposed for power-assisted bicycles. In addition to a typical FLC, the presented scheme adds a rule-tuning module to dynamically adjust the rule base during fuzzy inference processes. Simulation and experimental results indicate that the presented self-tuning module leads to comfortable and safe riding as compared with other approaches. The technique established in this paper is thought to have the potential for broader application in public bicycle-sharing systems utilized by a diverse range of riders.

Perancangan Self Tuning Dua Derajat Kebebasan dengan Metode Fuzzy Logic pada Pengendalian Kecepatan Motor Sinkron Magnet Permanen Menggunakan Metode Vector Control

Abstract — This paper proposes self tuning Proportional-Integral/Proportional gain controller parameter in a Two Degree of Freedom (2DoF) using fuzzy logic method for speed control of permanent magnet synchronous motor. The self tuning method can automatically determine the PI-P parameters using a fuzzy logic control system. Speed control of the 3-phase permanent magnet synchronous motor is carried out by changing the 3-phase system (a-b-c) to a 2-phase system (direct-quadrature) using the vector control method. SVPWM method is used to modulate six switches on a 3 phase inverter. The results obtained using MATLAB Simulink show that by using a self-tuning fuzzy logic control system, high motor performance results are obtained, where the speed response of the permanent magnet synchronous motor shows that the step response and disturbance response are close to the ideal response.
 Keywords— Permanent Magnet Synchronous Motor, Self Tuning, Two Degrees of Freedom, Fuzzy Logic, Vector Control.

Self-tuning fuzzy logic control of quarter car and bridge interaction model

In this study, active suspension control of the interaction between the bridge can be modeled according to the Euler-Bernoulli beam theory, and the quarter car model with three degrees of freedom is studied. The active suspension system consists of a spring, damper, and linear actuator. The active suspension control is designed using classical PID and self-tuning fuzzy PID (STFPID) to reduce the vehicle body's disruptive effects. To determine the performance of the designed controllers, two different road profiles with the bridge oscillations caused by the bridge flexibility were considered as the disruptive effect of the vehicle. When the simulation results were examined in terms of passenger seat displacement and acceleration, the proposed STFPID method significantly increased road holding and ride comfort.

Investigation of the Computational Burden Effects of Self-Tuning Fuzzy Logic Speed Controller of Induction Motor Drives With Different Rules Sizes

Investigation of the Computational Burden Effects of Self-Tuning Fuzzy Logic Speed Controller of Induction Motor Drives With Different Rules Sizes

A modified self-tuning fuzzy logic temperature controller for metal induction heating.

This paper presents a method to build a dynamic target curve producer corresponding to the rising time setting and the ultimate target temperature as a reference for a fuzzy logic controller that is used in the metal heating process application. To achieve this goal, there are some quantization factors in a fuzzy controller that must be set according to the system situation, as well as the experience of experts that will cause the controller to have a lack of adaptivity. To solve this problem, in this paper, all the quantization factors are analyzed thoroughly, and a self-tuning module is designed to make it possible for the controller to perform real-time adjustments based on the system situation and, eventually, make it more adaptive. During the design process, a simulation comparing the control capabilities of the conventional fuzzy logic controller and the self-tuning fuzzy logic controller (STFLC) is made using a finite element analysis. Finally, experiments are carried out on the induction heating system to verify the effect of the proposed STFLC. The results show that, with the proposed self-tuning module, the control capability and adaptivity of the controller were drastically improved.

Temperature regulation for distillation process using self-tuning fuzzy plus PID controller

This paper presents a self-tuning fuzzy logic controller plus PID controller that applied for essential oil extraction process. The most popular technique in extraction essential oil is using steam distillation. This method is lack to the process requirement and also towards parameter change. In order to regulate the steam distillation at desired extraction condition, the controller needs to integrate to the plant. By using auto regressive model with external input (ARX) model structure, the temperature of steam was successfully modelled with best fit 98.42%. This model was successfully representing the system during controller simulation work. There are several controllers that demonstrated in this project which are self-tuning fuzzy PID controller (STFPID) and conventional PID. Both controller will compare their performance in terms of system response which are percent overshoot (%OS), settling time (Ts) and also rise time (Tr). As the result, the STFPID with seven membership function gives the best performance compared to PID controller.

Implementation of Modified Field-Oriented Control Scheme for Improving the Fault Ride Through Ability of BDFIG System

This paper devises a design named brushless doubly fed induction generator (BDFIG) with a fault ride-through enhancement that employs upgraded field-oriented control (FOC) scheme. The DFIG is most suitable for wind energy conversion system (WECS) because it has an amicable establishment, economical operation and promising characteristics. A WECS based on two BDFIGs connected electrically in parallel and mechanically in series, excited by a three-phase inverter and controlled as variable speed, is described. For enhancing power quality and power flow capability, static compensator (STATCOM) has been incorporated in the proposed configuration. The comparative analysis on performance has been carried out with the existing proportional-integral (PI) controller and self-tuning fuzzy logic controller (STFLC) for the proposed configuration under varying wind speed. In this paper, the fuzzy controller is designed to adapt PI parameters Kp and Ki, in order to reduce at least some inherent characteristics (overshoot, response time, etc.) of the error between the reference and system response. The digital simulation results claim that the FLC-based controller can offer an attractive and feasible control for the proposed WECS integrating two BDFIGs.

Active vibration control of smart composite plates using optimized self-tuning fuzzy logic controller with optimization of placement, sizing and orientation of PFRC actuators

This paper deals with optimization of the sizing, location and orientation of the piezo-fiber reinforced composite (PFRC) actuators and active vibration control of the smart composite plates using particle-swarm optimized self-tuning fuzzy logic controller. The optimization criteria for optimal sizing, location and orientation of the PFRC actuators is based on the Gramian controllability matrix and the optimization process is performed by involving the limitation of the plates masses increase. Optimal configurations of five PFRC actuators for active vibration control of the first six modes of cantilever symmetric ((90°/0°/90°/0°)S), antisymmetric cross-ply ((90°/0°/90°/0°/90°/0°/90°/0°)) and antisymmetric angle-ply ((45°/-45°/45°/-45°/45°/-45°/45°/-45°)) composite plates are found using the particle swarm optimization. The detailed analysis of influences of the PFRC layer orientation and position (top or bottom side of composite plates), as well as bending-extension coupling of antisymmetric laminates on controllabilities is also performed. The experimental study is performed in order to validate this behavior on controllabilities of antisymmetric laminates. The particle swarm-optimized self-tuning fuzzy logic controller (FLC) adapted for the multiple-input multiple-output (MIMO) control is implemented for active vibration suppression of the plates. The membership functions as well as output matrices are optimized using the particle swarm optimization. The Mamdani and the zero-order Takagi–Sugeno–Kang fuzzy inference methods are employed and their performances are examined and compared. In order to represent the efficiency of the proposed controller, results obtained using the proposed particle swarm optimized self-tuning FLC are compared with the corresponding results in the case of the linear quadratic regulator (LQR) optimal control strategy.

Data-Driven Control Techniques for Renewable Energy Conversion Systems: Wind Turbine and Hydroelectric Plants

The interest in the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this end, data-driven control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes of working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Therefore, the paper aims at providing some guidelines on the design and the application of different data-driven control strategies to a wind turbine benchmark and a hydroelectric simulator. They rely on self-tuning PID, fuzzy logic, adaptive and model predictive control methodologies. Some of the considered methods, such as fuzzy and adaptive controllers, were successfully verified on wind turbine systems, and similar advantages may thus derive from their appropriate implementation and application to hydroelectric plants. These issues represent the key features of the work, which provides some details of the implementation of the proposed control strategies to these energy conversion systems. The simulations will highlight that the fuzzy regulators are able to provide good tracking capabilities, which are outperformed by adaptive and model predictive control schemes. The working conditions of the considered processes will be also taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many plants.

A Novel Self-Tuning Fuzzy Logic Controller Based Induction Motor Drive System: An Experimental Approach

High-performance induction motor (IM) drives require fast dynamic responses, robust to parameter variations, withstand load disturbance, stable control systems, and support easy hardware/software implementation. Fuzzy logic control (FLC) for speed controllers is garnering attention from researchers, since it is proven to produce better results compared with the conventional PI speed controllers. However, fixed parameter FLC experiences performance degradation when the system operates away from the design point or is affected by parameter variations or load disturbances. The purpose of this paper is to design and implement a simple self-tuning fuzzy logic controller (ST-FLC) for IM drives application. The proposed self-tuning mechanism is able to adjust the output scaling factor of the main FLC speed controller by improving the accuracy of the crisp output. The IM drive employed an indirect field-oriented control (IFOC) method fed by a hysteresis current controller (HCC). The fixed parameter FLC for the main speed controller comprises nine rules that are tuned to achieve the best performance. Then, a simple self-tuning mechanism is applied to the main fuzzy logic speed controller. All simulation work was done using Simulink and fuzzy tools in the MATLAB software. The effectiveness of the proposed controller was investigated by conducting a comparative analysis between fixed parameter FLC and ST-FLC in forward and reverse speed operations, with and without load disturbances. Finally, the experimental testing was carried out to validate the simulation results with the aid of a digital signal controller board, dSPACE DS1104, with an induction motor drive system. Based on the results, the ST-FLC showed superior performance in transient and steady-state conditions in terms of various performance measures, such as overshoot, rise time, settling time, and recovery time.

Self-tuning Fuzzy Logic Controller Based on Takagi-Sugeno Applied to Induction Motor Drives

<span>Fuzzy logic controller has been the main focus for many researchers and industries in motor drives. The popularity of Fuzzy Logic Controller (FLC) is due to its reliability and ability to handle parameters changes during load or disturbance. Fuzzy logic design can be visualized in two categories, mamdani design or Takagi-Sugeno (TS). Mamdani type can facilitate the design process, however it require high computational burden especially with big number of rules and experimental testing. This paper, develop Self-Tuning (ST) mechanism based on Takagi-Sugeno (TS) fuzzy type. The mechanism tunes the input scaling factor of speed fuzzy control of Induction Motor (IM) drives Based on the speed error and changes of error. A comparison study is done between the standard TS and the ST-TS based on simulations approaches considering different speed operations. Speed response characteristics such as rise time, overshoot, and settling time are compared for ST-TS and TS. It was shown that ST-TS has optimum results compared to the standard TS. The significance of the proposed method is that, optimum computational burden reduction is achieved.</span>

Magnetic induction of hyperthermia by a modified self-learning fuzzy temperature controller.

The aim of this study involved developing a temperature controller for magnetic induction hyperthermia (MIH). A closed-loop controller was applied to track a reference model to guarantee a desired temperature response. The MIH system generated an alternating magnetic field to heat a high magnetic permeability material. This wireless induction heating had few side effects when it was extensively applied to cancer treatment. The effects of hyperthermia strongly depend on the precise control of temperature. However, during the treatment process, the control performance is degraded due to severe perturbations and parameter variations. In this study, a modified self-learning fuzzy logic controller (SLFLC) with a gain tuning mechanism was implemented to obtain high control performance in a wide range of treatment situations. This implementation was performed by appropriately altering the output scaling factor of a fuzzy inverse model to adjust the control rules. In this study, the proposed SLFLC was compared to the classical self-tuning fuzzy logic controller and fuzzy model reference learning control. Additionally, the proposed SLFLC was verified by conducting in vitro experiments with porcine liver. The experimental results indicated that the proposed controller showed greater robustness and excellent adaptability with respect to the temperature control of the MIH system.

Self-Tuning Fuzzy Control for Seismic Protection of Smart Base-Isolated Buildings Subjected to Pulse-Type Near-Fault Earthquakes

Pulse-type near-fault earthquakes have obvious long-duration pulses, so they can cause large deformation in a base-isolated system in contrast to non-pulse-type near-fault and far-field earthquakes. This paper proposes a novel self-tuning fuzzy logic control strategy for seismic protection of a base-isolated system, which can operate the control force of the piezoelectric friction damper against different types of earthquakes. This control strategy employs a hierarchic control algorithm, in which a higher-level supervisory fuzzy controller is implemented to adjust the input normalization factors and output scaling factor, while a sub-level fuzzy controller effectively determines the command voltage of the piezoelectric friction damper according to current level of earthquakes. The efficiency of the proposed control strategy is also compared with uncontrolled and maximum passive cases. Numerical results reveal that the novel fuzzy logic control strategy can effectively reduce the isolation system deformations without the loss of potential advantages of base-isolated system.

Robust Longitudinal Speed Control of Hybrid Electric Vehicles with a Two-Degree-of-Freedom Fuzzy Logic Controller

This paper proposes a new robust two-degree-of-freedom (DoF) design method for controlling the nonlinear longitudinal speed problem of hybrid electric vehicles (HEVs). First, the uncertain parameters of the HEV model are described by fuzzy α-cut representation, in which the interval uncertainty and the possibility can be simultaneously indicated by the fuzzy membership function. For the fuzzy parametric uncertain system, the maximum uncertainty interval can be translated into the weighting matrix Q of the linear quadratic tracking problem to guarantee that the designed feedback controller is robust. Second, the fuzzy forward compensator is incorporated with a robust feedback controller to enhance the system tracking response. The simulation results demonstrate that the proposed controller has higher tracking performance compared to the single-DoF self-tuning fuzzy logic controller or conventional optimal H∞ controller.

Vibration mitigation of nonlinear crane system against earthquake excitations with the self-tuning fuzzy logic PID controller

This study aims to reduce the earthquake-induced vibrations of the container crane structures using an active vibration control. Vibration control using intelligent controllers such as fuzzy logic has attracted the attention of structural control engineers during the last decade. Fuzzy logic can handle uncertainties and heuristic knowledge and even nonlinearities effectively and easily. The improved seismic control performance can be performed by converting a simply designed static gain into a real time variable dynamic gain through a self-tuning mechanism. Because of the several advantages, the self-tuning fuzzy logic control algorithm is used in this study. Furthermore, with the aim of the increasing the performance of the designed controller, derivative and integral controller which are independent from the fuzzy logic controller are added to the architectural structure of the controller. This structure is called as self-tuning fuzzy logic PID controller (STFLPIDC). In this study, a six-degrees-of-freedom nonlinear mathematical model of the container crane structure is defined. Then, the controller is developed. Performance of designed controller is evaluated by numerical simulations. In the simulations, the ground motion of the Kobe earthquake \((M_w=7.2)\) in Japan on January 17, 1995, is used as earthquake excitation. The time history of displacements and accelerations and frequency responses of the both uncontrolled and controlled cases of the crane structure and control force are obtained. Performance of the designed controller is compared with the classical fuzzy logic controller (FLC). Comparison of the STFLPIDC and FLC in frequency and time domain is presented to evaluate the effect of performance of the controllers. Simulations with the STFLPIDC show better seismic performance than classical FLC. Besides, the robustness of the STFLPIDC was checked through the change in rigidity parameters. It is revealed that using designed controller and active vibration control system causes suppression of the earthquake-induced vibration.

Self-tuning fuzzy logic PID controller, applications in nuclear power plants

This paper presents intelligent control design using self-tuning fuzzy logic control technique. The proposed adaptive control design is used to maintain system parameters to be within design limits, which provided means to improve the efficiency of the overall control system. The proposed self-tuning control system is applied on breathing air control systems in nuclear power plant which was recommended to maintain system pressure within design limits. System dynamics were studied, transfer functions were built and controlling parameters were established to develop system model in Simulink. Relationship to system pressure, steady-state error, Kp, Ki and Kd was studied and used to build fuzzy controller. Controller was implemented and simulation results demonstrated that self-tuning fuzzy PID controller has better control, precision and performance in maintaining system pressure with reduced overshoot, rise time, settling time and steady state error compared against conventional PID controller. Fuzzy PID controller also proved to be huge cost saver for the company in long run.

Free vibration control of smart composite beams using particle swarm optimized self-tuning fuzzy logic controller

This paper deals with active free vibrations control of smart composite beams using particle-swarm optimized self-tuning fuzzy logic controller. In order to improve the performance and robustness of the fuzzy logic controller, this paper proposes integration of self-tuning method, where scaling factors of the input variables in the fuzzy logic controller are adjusted via peak observer, with optimization of membership functions using the particle swarm optimization algorithm. The Mamdani and zero-order Takagi–Sugeno–Kang fuzzy inference methods are employed. In order to overcome stability problem, at the same time keeping advantages of the proposed self-tuning fuzzy logic controller, this controller is combined with the LQR making composite controller. Several numerical studies are provided for the cantilever composite beam for both single mode and multimodal cases. In the multimodal case, a large-scale system is decomposed into smaller subsystems in a parallel structure. In order to represent the efficiency of the proposed controller, obtained results are compared with the corresponding results in the cases of the optimized fuzzy logic controllers with constant scaling factors and linear quadratic regulator.

Self-Tuning Closed-Loop Fuzzy Logic Control Algorithm for Adaptive Prestressed Structures

Constantly increasing traffic loads, unpredictable influences on and unplanned conditions of bridges represent new challenges in the field of engineering. Innovative strategies and construction techniques are required to meet these and future challenges. Adaptive prestressing offers great potential to increase a structure’s load-carrying capacity and to optimize its load-induced stresses. In this manner, cost efficiency and sustainability of constructions can be enhanced. Therefore, a control algorithm with high reliability and redundancy is required to meet the new challenges. In this paper, an adaptive fuzzy logic-based closed-loop control system for realization of adaptive prestressed structures is presented. Based on the new concept, two different specialized control algorithms are developed. In order to decrease deflections, the prestressing force of an aluminium truss is controlled. The algorithm includes a fuzzy system with model-based knowledge base. The learning process is established on direct synchronization of the calculated deflections of the model with the measured reactions of the structure. Another fuzzy system for adaptive prestressing of a reinforced concrete T-beam uses a model-free knowledge base. Optimization of load-induced stresses is accomplished utilizing expert knowledge. An incremental learning process is performed in accordance with the algorithm’s basic rules. Experiments on the two prototypes are conducted to investigate the functionality of the developed algorithms. In addition to efficiency, high reliability and redundancy are shown by the control system. In this article, the potential of fuzzy-controlled adaptive prestressing is illustrated.

Semiactive Self-Tuning Fuzzy Logic Control of Full Vehicle Model with MR Damper

Intelligent controllers are studied for vibration reduction of a vehicle consisting in a semiactive suspension system with a magnetorheological(MR) damper. The vehicle is modeled with seven degrees of freedom as a full vehicle model. The semiactive suspension system consists of a linear spring and an MR damper. MR damper is modeled using Bouc-Wen hysteresis phenomenon and applied to a full vehicle model. Fuzzy Logic based controllers are designed to determine the MR damper voltage. Fuzzy Logic and Self-Tuning Fuzzy Logic controllers are applied to the semiactive suspension system. Results of the system are investigated by simulation studies in MATLAB-Simulink environment. The performance of the semiactive suspension system is analyzed with and without control. Simulation results showed that both Fuzzy Logic and Self-Tuning Fuzzy Logic controllers perform better compared to uncontrolled case. Furthermore, Self-Tuning Fuzzy Logic controller displayed a greater improvement in vibration reduction performance compared to Fuzzy Logic controller.