Conventional Fuzzy Logic Controller Research Articles

Enhanced Hybrid Robust Fuzzy-PID Controller for Precise Trajectory Tracking Electro-Hydraulic Actuator System

The Electro-Hydraulic Actuator (EHA) system integrates electrical and hydraulic elements, enabling it to generate a rapid reaction, a high power-to-weight ratio, and significant stiffness. Nevertheless, EHA systems demonstrate non-linear characteristics and modeling uncertainties, such as friction and parametric uncertainty. Designing a controller for accurate trajectory tracking is greatly challenging due to these limitations. This paper introduces a hybrid robust fuzzy proportional-integral-derivative (HFPID) and (HF+PID) controller. The controller is designed to effectively control a third-order model of an EHA system for trajectory tracking. It is a significant contribution to the development of an intelligent robust controller that can perform well in different environments. Initially, a mathematical model for the EHA system was created using a first-principle approach. Subsequently, the Ziegler-Nichols method was employed to fine-tune the PID controller, while a conventional Fuzzy Logic Controller (FLC) was constructed in MATLAB Simulink utilizing linguistic variables and rule-based control. Without further tuning, the FL and PID controller are combined as a hybrid controller with different structures: Hybrid Fuzzy-PID (HFPID) and Hybrid Fuzzy+PID (HF+PID) controller. The Mean Square Error (MSE) and Root Mean Square Error (RMSE) are utilized as indices to assess the tracking accuracy and robustness of the four controllers. A greater value of MSE and RMSE indicates poorer performance of the controller. The results demonstrate that the HF+PID controller surpasses the other controllers by reaching the lowest MSE and RMSE values. It showcases the efficacy and accuracy in monitoring sinusoidal, multi-sinusoidal, and point-to-point trajectory tracking. Future work should focus on implementing the designed controller on hardware for real-time performance and experimenting with various types of FLC or Hybrid controllers, such as self-tuning fuzzy-PID, to further explore their potential.

Advanced active suspension control: A three-input fuzzy logic approach with jerk feedback for enhanced performance and robustness

Active suspension systems aim to increase ride comfort by reducing body acceleration due to road disturbances. This research proposes a new fuzzy logic controller (FLC) for such systems which includes acceleration error as third input. The objective of the proposed 3-inputs FLC is to improve vehicle comfort and ensure passenger safety. To assess the performances of the newly introduced controller, a theorical study is conducted on a quarter-car system with a four-wheel independent suspension design. Simulation results demonstrate high performances of the proposed 3-inputs FLC compared with conventional PID and 2-inputs FLC. The acceleration error as an additional FLC input was, to the best of our knowledge, not explored yet. Hence, this contribution offers a new method for the design of more effective active suspension systems.

DSP-based Control of Standalone PV/Battery Hybrid System

This paper investigates and presents the control and energy management of a typical standalone photovoltaic/battery hybrid system. The studied system in this paper includes two DC-DC converters. The first is a DC-DC boost converter works as a maximum power point tracker for the PV array, while the other is a bidirectional DC-DC converter used to regulate the DC bus voltage and to provide the necessary energy to the load by compensating the power deficit continuously. Generally, the developed control strategies in literature for such a system are based on proportional-integral (PI) controllers due to their simplicity of implementation, or by using some advanced nonlinear control concept, however this latter requires knowledge of system parameters and exact mathematical model for the design phase, which is sometimes difficult to obtain and makes the control technique sensitive to parameter variations. In order to overcome the previously mentioned drawbacks, this paper presents a model-free control technique based on a simplified version of fuzzy logic controller (FLC), this approach allows to keep the same implementation simplicity of PI controller and to considerably reduce the complexity of the conventional FLC. Experimental studies on a test bench were conducted to prove the effectiveness of the proposed approach.

Islanding Detection in Grid-Connected Urban Community Multi-Microgrid Clusters Using Decision-Tree-Based Fuzzy Logic Controller for Improved Transient Response

The development of renewable-energy-based microgrids is being considered as a potential solution to lessen the unrelenting burden on the centralized utility grid. Furthermore, recent studies reveal that integrated multi-microgrid cluster systems developed in urban communities maximize the effectiveness of microgrids and greatly decrease the utility grid dependence. However, due to the uncertain nature of renewable energy sources and frequent load variations, these systems face issues with unintentional islanding operations. This can create severe damage to the microgrid’s performance in its stable operating condition and lead to undesired transient responses. Hence, islanding must be identified rapidly to take preventive measures to address the issue. This requires the development of a suitable anti-islanding technique that is faster in terms of accuracy and timely detection. With this intention, this paper proposes a decision-tree-based fuzzy logic (DT-FL) controller for the rapid identification of islands in an urban community multi-microgrid cluster. The DT-FL controller’s operation includes two steps. First, the decision tree extracts the electrical parameters at the point of common coupling of the multi-microgrid system. Second, these extracted parameters are utilized for the online tuning of the fuzzy logic controller, for the fast detection of islanding. The multi-microgrid cluster under study, along with the proposed islanding technique, is implemented in the MATLAB-2021a software. The efficacy of the proposed DT-FL controller is validated by comparing its performance with that of the conventional fuzzy logic controller under different test scenarios. From the results, it is observed that the proposed DT-FL controller shows superior performance in terms of the islanding detection time as well as the transient response of the system when compared with the conventional controller.

Research on multilayer fast equalization strategy of Li-ion battery based on adaptive neural fuzzy inference system

In this paper, an adaptive neuro fuzzy inference system (ANFIS) based multilayer fast equalization strategy for Li-ion batteries is developed to solve the problem of wrong fuzzy rule settings affecting the equalization speed in traditional fuzzy logic methods. Firstly, a multilayer equalization circuit is designed to perform both intra-group and inter-group equalization, and a Graph theory analysis shows that this circuit topology can perform fast equalization while ensuring better equalization efficiency. Then an adaptive neuro fuzzy inference system is designed based on the multilayer equalization circuit designed in this paper to achieve more accurate adaptive adjustment of the equalization current and reduce time consumption, and finally a comparative study of the equalization circuit and equalization method under static and charging/discharging conditions is conducted using the Matlab/Similink platform. The experimental study shows that the speed of the multilayer equalization circuit designed in this study is increased by about 62 % and 37.55 % compared with the traditional single-inductor equalization circuit and adjacent-inductor circuit, respectively. Compared with the conventional fuzzy logic control (FLC) equalization strategy, the speed of the proposed equalization strategy is improved by about 10.98 %. It effectively speeds up the equalization process of Li-ion batteries.

Mobile Robot Path Following Controller Based On the Sirms Dynamically Connected Fuzzy Inference Model

In this paper, the single input rule modules (SIRMs) dynamically connected fuzzy inference model is employed to design a path following controller for a unicycle wheeled mobile robot(WMR).The SIRMs model is mainly used to reduce the number of fuzzy inference rules and consequently reduce the processing time of the conventional Fuzzy Logic Control (FLC) schemes. The adopted path following strategy uses two control unites working in parallel to drive the WMR to follow a path composed of a succession of discrete waypoints. The first unit is a heading controller charged of generatingthe angular velocity command and thesecond one is a linear velocity controller charged of the generation the speed control signal.For the headingcontroller, all input variables are assigned with two fuzzy inference modules: a SIRM and a dynamic importancedegree (DID). The structure of the SIRMs based linear velocitycontrolunitwas modified to simplify the designand to fulfill the requirements of the path following strategy. It contains two SIRM modules andonecommonDID. To ensure the stability of the WMR'smotion, the SIRMs and the DIDs in the two control units were designedin such a manner that the control of the robot's orientationgets higher priority over the control of the WMRspeed. The use of the SIRM fuzzy inference model allows the online automatic adjustment of priority orders of thedifferent control actions. No optimizationof the controller parameters is required since thatall base values areset to 0 and all the breadthsare set to 1 for the DID modules. This means that all the input items play equal rolesin the control of the WMR.The structure of the proposedpath following controller is simple and the inferencerules are intuitively understandable because they are inspired from the knowledge of a human expert. Numericalsimulations conducted in the MATLABenvironment show that the proposed SIRMs based controller outperforms apath followingcontroller that uses the conventional FLC scheme.

Synthesis of Hybrid Fuzzy Logic Law for Stable Control of Magnetic Levitation System

In this paper, we present a method to design a hybrid fuzzy logic controller (FLC) for a magnetic levitation system (MLS) based on the linear feedforward control method combined with FLC. MLS has many applications in industry, transportation, but the system is strongly nonlinear and unstable at equilibrium. The fast response linear control law ensures that the ball is kept at the desired point, but does not remain stable at that point in the presence of noise or deviation from the desired position. The controller that combines linear feedforward control and FLC is designed to ensure ball stability and increase the system's fast-response when deviating from equilibrium and improve control quality. Simulation results in the presence of noise show that the proposed control law has a fast and stable effect on external noise. The advantages of the proposed controller are shown through the comparison results with conventional PID and FLC control laws.

Improved Fuzzy MPPT to Solve Partial Shading Conditions Problem in a Photovoltaic System

This paper presents a hybrid maximum power point tracker for a photovoltaic (PV) system. The proposed technique combines between the advantage of fuzzy logic flexibility and the linearity of the β parameter method to override the problem of Partial Shading Conditions (PSC). The proposed approach can resolve the problem of PSC by tracking the Global Maximum Power Point (MPP) regardless of the shading conditions to which the PV system is submitted. For evaluating the presented technique, the Perturb and Observe command and a conventional FLC have been implemented too. Extended simulations with different meteorological conditions and scenarios of PSC are performed. The obtained results clearly show the superiority of the β-FLC tracker presented in terms of response time in the transient state and the steady-state error around MPP. The tracker presents also better effectiveness in the presence of PSC because it can reach up to 20.51% of the surplus of transmitted power compared to the other trackers presented.

Fuzzy Bang-Bang Relay Control of a Rigid Rotor Supported by Active Magnetic Bearings

Active magnetic bearings, which are open-loop and unstable, require a feedback control system to ensure stable operation of the rotating machines that they support. Proportional-integral-derivative (PID) controllers are widely used in field applications of these bearings for this purpose. PID controllers are designed to work effectively within the linear region of operation of the rotating machines. Due to the inherent nonlinearity of the active magnetic bearings, large unbalance forces that may occur in these machines result in nonlinear vibration responses. Therefore, the PID controller’s effectiveness to control the vibration of the rotating machines is considerably reduced when the unbalance forces in these machines become large. Other control strategies, such as the fuzzy logic and the sliding mode control schemes, are more apt to deal with the nonlinear responses of the rotating machines supported by active magnetic bearings. The present work proposes an integrated fuzzy bang-bang relay controller for a rigid rotor mounted on active magnetic bearings. The effectiveness of this controller to suppress rotor vibrations is examined numerically. Performance comparison of this controller with the conventional fuzzy logic and PD controllers are made for different initial conditions, rotor imbalance magnitudes, and rotor angular speeds. At extreme operating conditions due to large rotor unbalance forces, where the magnetic bearings are highly nonlinear, the proposed integrated fuzzy bang-bang relay controller proved to be more superior over the conventional fuzzy logic and PD controllers.

A Type-2 Fuzzy Controller to Enable the EFR Service from a Battery Energy Storage System

The increased use of distributed energy resources, especially electrical energy storage systems (EESS), has led to greater flexibility and complexity in power grids, which has led to new challenges in the operation of these systems, with particular emphasis on frequency regulation. To this end, the transmission system operator in Great Britain has designed a control scheme known as Enhanced Frequency Response (EFR) that is especially attractive for its implementation in EESS. This paper proposes a Type-2 fuzzy control system that enables the provision of EFR service from a battery energy storage system in order to improve the state-of-charge (SoC) management while providing EFR service from operating scenarios during working and off-duty days. The performance of the proposed controller is compared with a conventional FLC and PID controllers with similar features. The results showed that in all scenarios, but especially under large frequency deviations, the proposed controller presents a better SoC management in comparison without neglecting the EFR service provision.

Position Control of a Pneumatic Drive Using a Fuzzy Controller with an Analytic Activation Function.

The fuzzy logic controller, which uses an analytic activation function for the defuzzification procedure, was applied to the position control of a servo pneumatic drive controlled by a proportional valve. The Gaussian shape of input fuzzy sets, with the possibility of their modification, was used to fuzzify the input signal. The control signal was determined by introducing an analytic function instead of defining the fuzzy rule base. In this way, a conventional 2-D fuzzy rule table base is modified into 1-D fuzzy defuzzification based on an analytic function to calculate the controller output. In this control algorithm, the problem of conventional fuzzy logic control, in terms of the exponential growth in rules as the number of input variables increases, is eliminated. The synthesis controller procedure is adjusted to the flow rate characteristic of the proportional valve. The developed control algorithms are verified by computer simulation and by testing on a real pneumatic rodless cylindrical drive.

A Novel PSO Based Fuzzy Controller for Robust Operation of Solid-State Transfer Switch and Fast Load Transfer in Power Systems

This paper proposes a novel particle swarm optimization (PSO) algorithm based fuzzy logic controller (FLC) for improving the performance of automatic solid-state transfer switch (SSTS) with respect to transfer time. The proposed technique generates adaptive membership functions (MFs) of voltage error and rate of change of voltage error for input and output based on the fitness function formulated by the PSO. An optimal PSO-based FLC (PSOF) fitness function is further employed to tune and minimize the mean absolute error (MAE) to improve the performance of the load transfer in a short duration. Results obtained from the proposed PSOF are compared with those obtained with the conventional FLC to validate the developed controller. It is observed that the proposed PSOF optimized controller can transfer the load faster than the conventional FLC controller. The accuracy of the developed PSOF is illustrated and investigated via simulation tests for SSTS in the IEEE 9-bus system. It can be concluded that the PSOF controller is better than the only fuzzy controller in all tested cases in terms of transfer time.

Design and Implementation of Modified INC, Conventional INC, and Fuzzy Logic Controllers Applied to a PV System under Variable Weather Conditions

Maximum power point tracking (MPPT) algorithms are used in photovoltaic applications to extract the maximum power that the photovoltaic (PV) panel can produce, which depends on two inputs that are: temperature and irradiance. A DC-DC converter is inserted between the photovoltaic panel and the load to obtain the desired voltage level on the load side. In this paper, incremental conductance (INC) algorithm, modified INC, and fuzzy logic controller (FLC) are designed and assessed to improve energy conversion efficiency. These algorithms are applied to the control of boost converter for tracking the maximum power point (MPP). The modified INC offers fast response and good performance in terms of oscillations than conventional INC and FLC. The Matlab/Simulink environment is used to analyze, interpret the simulation results, and show the performances of each algorithm; and Proteus-based Arduino environment is used to implement the three methods in order to compare the Matlab simulation results with measurements acquired during implementation that is similar to real experiment.

Modeling and design of an automatic generation control for hydropower plants using Neuro-Fuzzy controller

This paper presents the modeling, design, and experimental analysis of an Automatic Generation Control (AGC) for a hydropower plant using Adaptive-Neuro-Fuzzy Inference system (ANFIS). This was aimed at reducing the frequency deviations which occur during power generation. The Adaptive-Neuro-Fuzzy Inference system (ANFIS) was used to intelligently control the selection of parameters for the effective control of power in the hydropower plant. The proposed ANFIS was trained with input–output data of the fuzzy logic controller (FLC). The ANFIS model is used as a hybrid learning model which includes the Least Square Estimate (LSE) and back propagation algorithm (BPA). The conventional PID, FLC, and ANFIS controllers were investigated using MATLAB. In order to determine the best controller, the controllers were experimented and compared to determine the controller with the best performance. The results show that frequency deviations occur as a result of a continuous variation of loads, which make the deviations difficult to control when a governor is not applied. Furthermore, the response of the AGC of the hydropower plant (in the single and double area) with step load changes was studied. The simulation results show that the ANFIS controller performs better compared to the PID as well as the FLC. Further results indicate that the proposed ANFIS controller helps to speed up the performance of the AGC of the hydropower plant. The ANFIS controller not only improved the performance but also made the fuzzy inference system (FIS) less dependent on the expert system.

POSITION CONTROL OF VTOL SYSTEM USING ANFIS VIA HARDWARE IN THE LOOP

Electric motors have been widely applied in various equipment. One application is found in Unmanned Aerial Vehicles (UAVs). An electric motor speed control system that can balance the aircraft's position is one of the mandatory features that must be owned by the aircraft. The position balancer control also supports the Vertical Take-Off Landing (VTOL) system. This study's VTOL position control system uses Hardware-in-the-loop (HIL) method with MATLAB Simulink and Arduino. ANFIS (Adaptive Neuro-Fuzzy Inferences System) is used as a position control algorithm. The controller performance is compared with conventional PID and FLC (Fuzzy Logic Controller). The system is tested as an initial position variation and loading test. The experiment shows that HIL can help fast prototyping by faster changes in the controller algorithms and is easy to program. The result is varied in each experiment. In the ISE (Integral Square of Error) point of view, ANFIS is better than PID by 100 % and has a very small difference from FLC in the initial position test. ANFIS is better by 95.44% and 4.56% compared with PID and FLC in the loading test, respectively.

A deep learned fuzzy control for inertial sensing: Micro electro mechanical systems

This study presents a new fuzzy logic controller (FLC) for micro-electro-mechanical-system gyroscopes (MEMS-Gs). The nonlinearities and unknown dynamics are modeled by the designed non-singleton type-3 fuzzy system (NT3FS) and an adaptive control scheme is presented. To improve the control accuracy, the tracking error dynamics are identified by a deep learned Boltzmann machine (RBM) and the nonlinear model predictive controller (NMPC) is designed by the optimized RBM model. Finally, the approximation errors are tackled by an adaptive compensator. The parameters of RBM are learned by contrastive divergence (CD) method and rules of NT3FS are optimized by the tuning laws that are obtained through the stability investigation. In various simulations and comparisons with some other conventional FLCs the superiority of the designed controller is shown. A good tracking accuracy of chaotic references and well robustness performance are obtained by the suggested control scheme.

Improving Self-Balancing and Position Tracking Control for Ball Balancer Application with Discrete Wavelet Transform-Based Fuzzy Logic Controller

The steady-state operation and controlled position tracking in ball balancer applications are largely affected due to their nonlinear and underactuated behavior. To overcome these drawbacks, this paper develops a wavelet-based fuzzy controller which operates in closed loop with the system by measuring the ball position and the plate angle. The proposed approach adapts discrete wavelet transform for denoising the error signal and tuning the weights of the fuzzy controller. To test the effectiveness of the proposed approach, numerical simulations are performed by modeling a two degree of freedom ball balancer system. The eminence of the controller is assessed by comparing its operation on the modeled system with conventional fuzzy logic controller and by calculating the root mean square error, and time response analysis. The results showed a steady and precise response of the proposed approach to the framework of positioning ball on the plate.

Power quality improvement using fuzzy logic controller based unified power flow controller

<p>The Power quality of the electrical system is an important issue for industrial, commercial, and housing uses. An increasing request for high quality electrical power and an increasing number of distorting loads had led to increase the consideration of power quality by customers and utilities. The development and use of flexible alternating current transmission system (FACTs) controllers in power transmission systems had led to many applications of these controllers. A unified power flow controller (UPFC) is one of the FACTs elements which is used to control both active and reactive power flow of the transmission line. This paper tried to improve power quality using a fuzzy logic controller (FLC) based UPFC, where it used to control both active and reactive power flow, decreas the total harmonic distortion (THD), correct power factor, regulate line voltage and enhance transient stability. A comparison study of the performance between the system with a conventional PID controller and FLC has been done. The theoretical analysis has been proved by implementing the system using MATLAB/SIMULINK package.The Power quality of the electrical system is an important issue for industrial, commercial, and housing uses. An increasing request for high quality electrical power and an increasing number of distorting loads had led to increase the consideration of power quality by customers and utilities. The development and use of flexible alternating current transmission system (FACTs) controllers in power transmission systems had led to many applications of these controllers. A unified power flow controller (UPFC) is one of the FACTs elements which is used to control both active and reactive power flow of the transmission line. This paper tried to improve power quality using a fuzzy logic controller (FLC) based UPFC, where it used to control both active and reactive power flow, decreas the total harmonic distortion (THD), correct power factor, regulate line voltage and enhance transient stability. A comparison study of the performance between the system with a conventional PID controller and FLC has been done. The theoretical analysis has been proved by implementing the system using MATLAB/SIMULINK package.</p>

A New design of fuzzy logic controller optimized By PSO-SCSO applied To SFO-DTC induction motor drive

In this paper, a new strategy for the design of fuzzy logic controllers (FLC) is proposed. This strategy is based on the optimization, by the particle swarm optimization (PSO) Algorithm and by sine-cosine based swarm optimization (SCSO) Algorithm, of the input-output gains and the geometric forms of the triangular membership functions of the proposed FLC. This latter is obtained by the minimizing a cost function based on combining two criterions, the Integral Time Absolute Error (ITAE) and Integral Absolute Error (IAE). A comparison, between the conventional FLC and the proposed FLC is carried out. The PSO-FLC shows a remarkable improvement in the performances of the controlled induction motor.

Experimental simplified rule of self tuning fuzzy logic-model reference adaptive speed controller for induction motor drive

<span>Fuzzy logic controller (FLC) has shown excellent performance in dealing with the non-linearity and complex dynamic model of the induction motor. However, a conventional constant parameter FLC (CPFL) will not be able to provide–good coverage performance for a wide speed range operation with a single tuning parameter. Therefore, this paper proposed a self tuning mechanism FLC approach by model reference adaptive controller (ST-MRAC) to continuously allow to adjust the parameters. Due to real time hardware application, the dominant rules selection method for simplified rules has been implemented as part of the reducing computational burden. Experiment results validate a good performance of the ST-MRAC compared to the CPFL for the speed performance in terms of the wide range of operations and disturbance showed remarkable performance.</span>