Conventional Fuzzy Controller Research Articles

A Novel Approach to a Recurrent Fuzzy Logic Controller Design Applied to Systems with Multiple Disturbances

In any process, control plays a vital role in the quality of the product. The influence of a controller's operation on a process is linked to its success or failure. However, using a controller that works properly presents several obstacles, such as non-linearity or the lack of a model that adequately represents the behavior of the process. Different approaches have been devised and studied to deal with these difficulties. One of the most promising and widely applied and studied in recent years is fuzzy control due to its ability to operate systems with no precise plant model. Herein, a set of rules and a fuzzy control structure are proposed, with fewer design parameters than a conventional fuzzy controller and better performance. The global stochastic optimization tool Differential Evolution is used to obtain the design parameters of the proposed fuzzy controller.

Adaptive neuro fuzzy technology to enhance PID performances within VCA for grid-connected wind system under nonlinear behaviors: FPGA hardware implementation

Wind Energy Conversion Systems (WECSs) exhibit nonlinear behavior due to internal and external disturbances, which can significantly impact the control performance. Firstly, this paper proposes a Vector Control Approach (VCA) based on Adaptive Neuro Fuzzy (ANF) of Proportional Integral Derivative (PID) controllers for a variable speed Permanent Magnet Synchronous Generator (PMSG)-based WECS connected to the grid to address these issues. The proposed ANF-PID controller combines the robustness of the fuzzy logic with the neural network's performance to ensure high accuracy, excellent tracking, and robustness against the WECS's nonlinear behavior. The neural network automates the placement of fuzzy logic membership functions. Secondly, this paper suggests a real-time hardware implementation of the VCA based on ANF-PID using a Field Programmable Gate Array (FPGA) to reduce the processing time and the WECS's sampling period. The effectiveness of the VCA based on ANF-PID controllers is validated by conducting a numerical simulation within the Matlab-Simulink using the Xilinx System Generator tool, alongside an experimental in-the-loop hardware implementation using FPGA board. Different scenarios are conducted to validate the robustness and stability of the proposed VCA based on ANF-PID control schemes, which is compared to Conventional Fuzzy PID (C-FPID) and PID controllers. The results exhibit that the VCA based on ANF-PID controllers outperforms VCA-C-FPID and VCA-PID controllers in terms of precision, tracking accuracy, and robustness against the WECS's nonlinear behavior and the PMSG parameter variations. The performances of the proposed controllers are tested under some criteria. In fact, the proposed ANF-PID controller provides a Mean Absolute Error (MAE), a Mean Squared Error (MSE) and a Root Mean Squared Error (RMSE) of 8.1911 × 10−04, 8.4181 × 10−05 and 0.0092, respectively. Compared to the C-FPID controller, the proposed controller offers an improvement gain of 98.96 %, 99.61 %, and 93.79 % of MAE, MSE, and RMSE, respectively. When compared to the PID controller, its improvement is even more significant, with a reduction of 99.9919 %, 99.9999 %, and 99.9288 % of MAE, MSE, and RMSE, respectively. In addition, the ANF-PID-based VCA utilizes hardware resources on the FPGA efficiently, requiring only 65.60 % slices LUT, 0.28 % LUTRAM, and 22.99 % FF. This makes it a more cost-effective option compared to the VCA-C-FPID controller for diverse control systems.

Purity control of simulated moving bed based on advanced fuzzy controller

Simulated moving bed (SMB) technology is considered one of the most successful techniques in chromatographic separation. However, due to the nonlinearity caused by discrete events and sensitivity to numerous separation performance parameters, purity control in SMB systems has been a challenging issue. Fuzzy controllers are increasingly popular in industrial environments due to their simplicity and effectiveness in handling nonlinearity. However, traditional fuzzy controllers used in industry often overlook considerations of error acceleration, resulting in slight deviations from target values under steady-state conditions and oscillatory behavior when system parameters change. This study proposes an advanced fuzzy controller, where in a series of experiments, the purity control targets for component B are set at 94% and 96%, and for component A are set at 96% and 96%, respectively. Experimental results indicate that the advanced fuzzy controller achieves higher precision, with an average deviation of around 0.1%, for both components B and A. Importantly, under variations in adsorbent parameter(from 0.01 to 0.03), feed concentration(from 4.5 to 5.2), and switching time(from 178 to 182), the experimental results demonstrate smoother control with the advanced controller, particularly when oscillations occur with conventional fuzzy controllers due to switching time variations, indicating robust control with the advanced fuzzy controller.

Minimizing Voltage Ripple of a DC Microgrid via a Particle-Swarm-Optimization-Based Fuzzy Controller

DC microgrids play a crucial role in both industrial and residential applications. This study focuses on minimizing output voltage ripple in a DC microgrid, including power supply resources, a stochastic load, a ballast load, and a stabilizer. The solar cell serves as the power supply, and the stochastic load represents customer demand, whereas the ballast load includes a load to safeguard the boost circuits against the overvoltage in no-load periods. The stabilizer integrates components such as electrical vehicle batteries for energy storage and controlling long-time ripples, supercapacitors for controlling transient ripples, and an over-voltage discharge mechanism to prevent overcharging in the storage. To optimize the charging and discharging for batteries and supercapacitors, a multi-objective cost function is defined, consisting of two parts—one for ripple minimization and the other for reducing battery usage. The battery charge and discharge are considered in the objective function to limit its usage during transient periods, providing a mechanism to rely on the supercapacitor and protect the battery. Particle swarm optimization is employed to fine-tune the fuzzy membership function. Various operational scenarios are designed to showcase the DC microgrid’s functionality under different conditions, including scenarios where production exceeds and falls below consumption. The study demonstrates the improved performance and efficiency achieved by integrating a PSO-based fuzzy controller to minimize voltage ripple in a DC microgrid and reduce battery wear. Results indicate a 42% enhancement in the integral of absolute error of battery current with our proposed PSO-based fuzzy controller compared to a conventional fuzzy controller and a 78% improvement compared to a PI controller. This translates to a respective reduction in battery activity by 42% and 78%.

Research on roll temperature compensation of variable domain fuzzy controller based on improved cat swarm optimization

In the hot rolling process, the hot crown of the roll is determined by the roll temperature and the temperature distribution. Furthermore, the hot crown is an important factor, which causes variation in the strip gage along the plane perpendicular to the rolling direction, affecting the strip quality. However, the roll temperature response has the characteristics of nonlinearity, hysteresis and time-varying, which makes it difficult to control accurately by classical control theory and method. In order to accurately control the rolling temperature, a variable universe fuzzy controller based on improved cat swarm optimization (ICSO-VUFC) was established. Firstly, a dynamic mixture ratio and an improved tracking mode were used to improve the optimization capability of the cat swarm. In comparison with the conventional cat swarm optimization (CSO) controller, the proposed process showed better optimization performance. Secondly, a simulation analysis based on MATLAB was employed to compare the ICSO-VUFC with the conventional fuzzy controller (C-FC) and the fuzzy controller based on the improved cat swarm optimization (ICSO-FC). The results reveal that the ICSO-VUFC exhibits the best dynamic and steady performance. Finally, the temperature control accuracy of the rolled regions during different rolling passes under the three fuzzy controllers was examined and compared. The results show that the ICSO-VUFC exhibits the highest control accuracy and stability with a temperature error range of ±4 °C. Through the analysis of the strip crown, it can be seen that the control accuracy of the strip crown can be effectively improved by using ICSO-VUFC to control the roll temperature distribution.

Seismic control of adaptive variable stiffness intelligent structures using fuzzy control strategy combined with LSTM

A novel semi-active control algorithm for adaptive variable stiffness intelligent structures that combines the fuzzy control strategy with Long Short-Term Memory (LSTM) is proposed in this study, with the aim of mitigating structural responses under earthquake excitations. The combined Fuzzy-LSTM strategy utilizes an LSTM neural network to predict the displacement and velocity responses of the structure at the next moment, and uses the predicted results as input for fuzzy control to obtain active control forces. Due to the difference between the displacement-velocity response and the fuzzy control domain, the genetic algorithm is used to optimize scaling factors of displacement and velocity responses and the control force, which is implemented by exploiting stiffness variability and structural inter-shift. The Fuzzy-LSTM strategy can compensate for the time delay effect on active control caused by stiffness variations, algorithmic computations and sensor measurements, and therefore achieves a better control effect through prediction. The semi-active independently variable stiffness (SAIVS) device is used to implement the presented control algorithm, which is a diamond device consisting of four linear springs and an electromagnetic actuator. The SAIVS device can achieve a continuous and smooth stiffness variation and meet the application requirements of the developed algorithm. Therefore, active control forces can be obtained through structural stiffness variations and interlayer displacements under seismic excitation, to achieve an approximate active control effect through a semi-active method. A 10-story building is presented as a case study for numerical simulations. Results show that the Fuzzy-LSTM strategy can significantly improve the control effect compared to the conventional fuzzy control strategy, and therefore achieve a better seismic response mitigation performance.

A speed loop control method of a laser tracing measurement control system

In a laser tracking control system, a motor drives the load to achieve continuous forward and reverse rotation speed control, and the magnitude of the load changes periodically during the motor rotation to realize tracking. Based on these characteristics, we propose an integral-separated improved variable universe fuzzy proportional-integral (PI) control method with torque feedforward compensation method in this study. The proposed method achieves the control performances of fast response, high steady-state accuracy, and strong anti-interference. The conventional fuzzy PI control method achieves good performance only in the unidirectional acceleration of a motor; therefore, we have improved its fuzzy rules, and designed a novel universe contraction–expansion factor. Additionally, a load torque observer is introduced to observe and compensate for the external load and to further improve the anti-interference performance of the tracking control system. The experimental results demonstrate that the integral-separated improved variable-universe fuzzy PI control method achieves better performance in the forward and reverse speed regulation processes of the motor. The overshoot is 92% less than that of the conventional PI control when the target speed is set to ±500 rpm. The Luenberger observer is introduced to observe and compensate for the load torque. After the stabilizing the speed and applying an external load of 0.2 N m to the motor, the maximum speed reduction is observed to be 59% less than that obtained using the conventional PI control. The improved PI control method satisfies the speed loop control requirements of the laser tracking control system and can improve the steady-state tracking accuracy and robustness of the system.

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.

Fuzzy Cooperative Control for the Stabilization of the Rotating Inverted Pendulum System

The rotating inverted pendulum is a nonlinear, multivariate, strongly coupled unstable system, and studying it can effectively reflect many typical control problems. In this paper, a parameter self-tuning fuzzy controller is proposed to perform the balance control of a single rotating inverted pendulum. Particle swarm optimization is used to adjust its control parameters, and simulation experiments are performed to show that the system can achieve stability with the designed parametric self-tuning fuzzy controller, with control performance better than that of the conventional fuzzy controller. Furthermore, the leader-follower control strategy is used to realize the cooperative control of multiple rotating inverted pendulums. Two QUBE-Servo 2 rotating inverted pendulums are used for a cooperative pendulum swing-up experiment and stabilization experiment, and the effectiveness of the proposed cooperative control strategy is verified.

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.

Fuzzy Direct Torque Control of Surface Permanent Magnet Synchronous Motor

To solve the problems of large torque and flux ripple in direct torque control (DTC) of permanent magnet synchronous motors (PMSM), a fuzzy controller is used to replace hysteresis comparators and switching tables in the DTC. Different from the conventional fuzzy controller which only outputs the angle or amplitude of the voltage vector, the controller adjusts the angle and value of the voltage vector at the same time in the surface PMSM DTC system is proposed. The inputs and output of the fuzzy controller are: stator flux, torque error, angle and amplitude of voltage vector in stator flux coordinate system. The simulation results of proposed fuzzy control system work normally. Compared with conventional fuzzy control systems, stator flux and torque ripples are decreased and only when motor speed is negative, torque ripple is a little higher. To suppress torque ripple at negative speed, two membership functions of torque error are designed and the fuzzy control system can select the proper membership function according to motor speed. Simulation results show adaptive fuzzy control system can effectively reduce torque and flux ripple in four quadrants.

Performance enhancement and power management strategy of an autonomous hybrid fuel cell/wind power system based on adaptive neuro fuzzy inference system

In this paper, a hybrid wind/fuel cell generation system which can be used for loads in remote areas as a micro grid application is considered. This micro grid mainly includes fuel cell (FC), wind generator as electrical power suppliers, resistive-inductive impedance as static load, induction motor (IM) as a dynamic load, DC/AC converter and water electrolizer for supplying hydrogen gas. The Fuel cell is used to compensate the decrease in the power generated by wind, which leads to an increase in the system efficiency. Furthermore, an adaptive control model and achievement refinements of a micro-grid using Adaptive Neuro Fuzzy Inference System (ANFIS) controller has been utilized to regulate the load voltage and frequency. This suggested microgrid system is achieved so that the wind generation unit supplies the loads, while any additional energy needed by the loads will be offset by the fuel cell generator unit. Thus, the main objective of this work is to apply an adaptive control method for improving the proposed electrical micro grid performance. In addition, the performance of the considered system is compared with the proposed ANFIS control when applying the traditional fuzzy control. The outcomes also demonstrated a better reaction and durability to the chosen control model. The MATLAB/SIMULINK programming software tools have been used for carrying out case studies towards the evaluation and validation of the methodology developed in this work with applications. The proposed solution achieved improvement in transient performance. However, the settling time is decreased to 21% in the case of using the suggested ANFIS controller comparing with conventional fuzzy control.

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.

Performance Analysis of Electro-Hydraulic Thrust System of TBM Based on Fuzzy PID Controller

The tunnel boring machine (TBM) is widely used in tunnel construction projects. The thrust system plays a crucial role to drive the machine ahead and support gripper shoes stably while tunneling. More and more attention has been paid to the pressure and velocity regulation efficiency as the TBM advances in complex rock conditions to ensure the stabilization of the tunneling process. A thrust hydraulic control system, assembled with a proportional pressure reducing valve, is established with system operating parameters. The mathematical model of the thrust electro-hydraulic system is revealed. To improve the control characteristics of the thrust system, a self-tuning fuzzy PID controller is introduced in the pressure and velocity regulation procedures. After that, tests on a Φ2.5 m scaled TBM test rig are carried out. The test results show that the thrust system adopting the fuzzy PID controller results in less oscillation and a smoother regulation process. It takes less time to reach the target goal of pressure regulation with less vibration during the pressure regenerating periods, and both systems of conventional PID controller and fuzzy PID controller are qualified in velocity regulation movements. The proposed control methods show better benefits in reduction of vibrations and shorter time of regulation to stable conditions, which extends the machine’s life and affects the acceleration of the tunneling process.

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.

Traffic Control Prediction Design Based on Fuzzy Logic and Lyapunov Approaches to Improve the Performance of Road Intersection

Due to the increasing use of private cars for urbanization and urban transport, the travel time of urban transportation is increasing. People spend a lot of time in the streets, and the queue length of waiting increases accordingly; this has direct effects on fuel consumption too. Traffic flow forecasts and traffic light schedules were studied separately in the urban traffic system. This paper presents a new stable TS (Takagi–Sugeno) fuzzy controller for urban traffic. The state-space dynamics are utilized to formulate both the vehicle’s average waiting time at an isolated intersection and the length of queues. A fuzzy intelligent controller is designed for light control based upon the length of the queue, and eventually, the system’s stability is proved using the Lyapunov theorem. Moreover, the input variables are the length of queue and number of input or output vehicles from each lane. The simulation results describe the appearance of the proposed controller. An illustrative example is also given to show the proposed method’s effectiveness; the suggested method is more efficient than both the conventional fuzzy traffic controllers and the fixed time controller.

Using Sine Function-Based Nonlinear Feedback to Control Water Tank Level

This manuscript addresses the feasibility and significance of using a sine function to modify the system error of a normal linear feedback control to achieve more efficient capabilities in terms of energy-saving. The associated mathematic modeling and assessment were demonstrated by presenting a case analysis on the capabilities of controlling water level for a single tank. The principle of robust control and the theories and detailed algorithm of Lyapunov stability were applied to assess the result derived by novel nonlinear feedback in the form of sine function for optimizing the robustness of the PID (Proportional–Integral–Derivative) controller and economizing energy. Two control simulations are compared: nonlinear feedback control using a sine function and conventional fuzzy control. The results reveal that using the nonlinear feedback controller, a reduction of up to 32.9% of the average controlled quantity is achieved, and the performance index is improved by 24.0% with satisfactory robustness. The proposed nonlinear feedback control using a sine function provides simplicity, convenient implementation, and energy efficiency.

Advanced design and tests of a new electrical control seeding system with genetic algorithm fuzzy control strategy

In the existing soybean breeding and planting machinery, the power source of the metering device adopts the ground wheel transmission method mostly. However, this power transmission method is likely to cause slippage during the planting operation, which will cause problems such as the increase of the missed seeding index and the increase of the coefficient of plant spacing. It is not conducive for scientific researchers to carry out breeding operations. Aiming at this problem, an electronically controlled soybean seeding system is designed, and the power of the seed metering device is derived from the motor. In order to improve the control accuracy of the electronically controlled seeding system, the precise control of the soybean seeding rate is finally realized. The electric drive soybean seeding system adopts closed-loop control, the motor model of the electric drive seeding system is established, and the transfer function of the motor is obtained. PID control based on a genetic algorithm is adopted, and the corresponding parameters are substituted into the control system simulation model established in MATLAB/SIMULINK. Field verification tests have been carried out on the conventional fuzzy PID control system and the electric drive soybean planter of the fuzzy PID control system based on a genetic algorithm. The result showed that the average of the repeat-seeding parameter is 1.30% better than the average of conventional seeding system (1.40%), the average of the miss-seeding parameter is 1.08% better than the average of conventional seeding system (2.09%) and the average of row-spacing variation parameter is 2.79% better than the average of conventional seeding system (2.34%). In conclusion, the new seeding system is robust obviously. Field trial results show that seeding with Genetic Algorithm Fuzzy control is better.

Adaptive Fuzzy PID Based on Granular Function for Proton Exchange Membrane Fuel Cell Oxygen Excess Ratio Control

An effective oxygen excess ratio control strategy for a proton exchange membrane fuel cell (PEMFC) can avoid oxygen starvation and optimize system performance. In this paper, a fuzzy PID control strategy based on granular function (GFPID) was proposed. Meanwhile, a proton exchange membrane fuel cell dynamic model was established on the MATLAB/Simulink platform, including the stack model system and the auxiliary system. In order to avoid oxygen starvation due to the transient variation of load current and optimize the parasitic power of the auxiliary system and the stack voltage, the purpose of optimizing the overall operating condition of the system was finally achieved. Adaptive fuzzy PID (AFPID) control has the technical bottleneck limitation of fuzzy rules explosion. GFPID eliminates fuzzification and defuzzification to solve this phenomenon. The number of fuzzy rules does not affect the precision of GFPID control, which is only related to the fuzzy granular points in the fitted granular response function. The granular function replaces the conventional fuzzy controller to realize the online adjustment of PID parameters. Compared with the conventional PID and AFPID control, the feasibility and superiority of the algorithm based on particle function are verified.

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.