Fuzzy PID Controller Research Articles

The fuzzy system based on vague partitions and its application to path tracking control for autonomous vehicles

As significant carriers of the application of fuzzy set theories, fuzzy systems have been widely used in many fields. However, selecting fuzzifications, fuzzy reasoning engines, and defuzzifications is subjective for Mamdani fuzzy systems, and the fuzzy rule of Takagi-Sugeno-Kang fuzzy systems is less of a linguistic interpretation. Regarding these shortcomings, this paper proposes a fuzzy system based on vague partitions processing information directly from the fuzzy rule base, in which fuzzy rules have explicit semantics. Firstly, the n-dimensional vague partition of the n-dimensional universe is defined based on 1-dimensional vague partitions and the aggregation function, and its properties are discussed. Based on these, we design the new fuzzy system, and investigate its approximation properties which is the theoretical guarantee for applying the fuzzy system. As an application, we combine the fuzzy system with PID control system to deal with autonomous vehicle path tracking control problems. A series of experiments are constructed, and experimental results indicate that the fuzzy system based on vague partitions makes the fuzzy PID control system strong robustness, and has obvious advantages compared with other traditional fuzzy systems for path tracking control problems.

Liquid Level Control System for Degassing Chamber of Vacuum Oil Filter Based on Fuzzy PID

This paper proposed a fuzzy PID control method to address the complexities associated with the inertia and nonlinear variations in liquid level control within the degassing chamber of vacuum oil filter. Tailored to meet practical control demands, a fuzzy control rule table was devised. This solution was simulated and applied in MATLAB/Simulink. The results indicated that, in comparison to classic PID, fuzzy PID exhibited faster adjustment speed and reduced overshoot, making it a more suitable option for vacuum oil filter applications.

Design and implementation of facility intelligent carbon dioxide incubator control system based on POA optimized fuzzy PID

To solve the problem that traditional carbon dioxide incubators are difficult to accurately control the required carbon dioxide environment to the detriment of experimental cultivation, and given the difficulties of time lag, nonlinearity, time-varying, and poor adaptive ability of the incubator control system, this study designs a set of intelligent carbon dioxide incubator control system suitable for laboratory cultivation. The system is based on the POA (Pelican optimization algorithm) to optimize the fuzzy PID (Proportional Integral Derivative) control to regulate the opening degree of the solenoid valve to accurately control the carbon dioxide concentration in the incubator to the set range. In this study, the simulation and comparison tests of POA-based optimized fuzzy PID control, PID control, and fuzzy PID control are carried out by MATLAB/Simulink, and it is found that the overshooting amount of the system is reduced by 11.6%, the regulating time is shortened by 69.3 s, and the steady state error is reduced by 0.1% compared with that of the traditional fuzzy PID control. It can be obtained through practical tests that the accuracy, response, and stability of the optimized fuzzy PID control of the pelican algorithm are better than those of the fuzzy PID control method, and it has better robustness. Therefore, the carbon dioxide incubator control system has high practical application value.

Impact and Integration of Electric vehicles on renewable energy based Microgrid: Frequency profile improvement by a-SCA optimized FO-Fuzzy PSS approach

The modelling of an electric vehicle along with its integration and impact over a renewable energy based microgrid topology is well addressed in this manuscript. The frequent charging and discharging of the electric vehicle makes an oscillation over grid frequency. The performance especially frequency of an islanded AC microgrid is also affected seriously under the actions of different uncertainties like load dynamics, wind fluctuation in wind plant, solar intensity variation of PV plant etc. In order to maintain standard frequency, this research work aims to regulate the net power generation of the system in response to total demand. To monitor net generation, this work has intended a fractional order fuzzy power system stabilizer (FO-Fuzzy PSS) control scheme in several dynamic situations. The proposed FO-Fuzzy PSS control scheme acts as most potential candidate to pertain stability in system frequency in above discussed disturbances. The controller gains are tuned optimally with suggesting an advanced- Sine Cosine Algorithm (a-SCA) under different conditions. The performance of the optimal FO- Fuzzy PSS controller is compared over standard fuzzy controller and PID controller in regard to frequency regulation of microgrid system. It is observed that proposed FO-Fuzzy PSS control scheme has the credential to reduce settling time of ΔF1 (area1 microgrid frequency) by 98.60% and 250.82% over fuzzy controller & PID controller correspondingly. Further, the dynamic optimal performance of the proposed a-SCA is compared over original SCA and PSO techniques to justify its superiority.

An innovative fuzzy logic frequency regulation strategy for two-area power systems

Modern environmentally friendly power system designs offer several application benefits, but they also generate losses. In order for this structured power system to operate reliably, the total generation, total load demand, and system losses must be balanced. Changes in load demand disrupt both the real and reactive power balances. As a result, the system frequency and tie-line interchange power differ from their planned values. A large variance in system frequency can cause the system to crash. Multiple connected area systems use clever load frequency control techniques in this scenario to deliver dependable and high-quality frequency and tie-line power delivery. In this case, a freestanding hybrid power system is considered, with generated power and frequency intelligently managed. In addition to the unpredictability of the wind, frequent changes in the load profile can result in significant and damaging power variations. The output power of such renewable sources may fluctuate to the point where major frequency and voltage variations occur in the system. The fuzzy logic PID controller (FLPIDC) is an intelligent approach recently proposed to address the load frequency control (LFC) issue of an interconnected power system. Standard proportional integral derivative (PID) controllers operate each section of the system.

Design and modelling of an AI governed type-2 Fuzzy tilt control strategy for AGC of a multi-source power grid in constraint to optimal dispatch

The robust design and application of a novel type-2 fuzzy tilt controller (Type2-FTC) has been well demonstrated in this manuscript. This controller is anticipated to build automatic generation control (AGC) of a multi-area hybrid power system in concern to the optimal power generation scheduling. The combined action of AGC and optimal scheduling of a power system is referred as economic AGC or commonly ELD-AGC. At the initial stage of the analysis, the concept of only AGC has been discussed through various robust approaches. Then the extensive study on ELD-AGC of the proposed power system has been progressed under various controller actions. Further, the proposed Type2-FTC controller is designed ideally with employing an innovative quassi-opposition path finder algorithm (QO-PFA) in various operating conditions. The effectiveness and the viability of the proposed Type2-FTC controller has been verified over type-I fuzzy PID and conventional PID controllers for AGC as well as ELD-AGC of the multi area power system. In controller comparison study, it has been revealed that proposed Type2-FTC is a potential candidate to improve frequency stability of the system. Further, the analysis over technique study justifies supremacy of the suggested QO-FPA algorithm over standard PFA and PSO algorithms for optimal designing all implemented controllers.

A Wearable Upper Limb Exoskeleton System and Intelligent Control Strategy.

Heavy lifting operations frequently lead to upper limb muscle fatigue and injury. In order to reduce muscle fatigue, auxiliary force for upper limbs can be provided. This paper presents the development and evaluation of a wearable upper limb exoskeleton (ULE) robot system. A flexible cable transmits auxiliary torque and is connected to the upper limb by bypassing the shoulder. Based on the K-nearest neighbors (KNN) algorithm and integrated fuzzy PID control strategy, the ULE identifies the handling posture and provides accurate active auxiliary force automatically. Overall, it has the quality of being light and easy to wear. In unassisted mode, the wearer's upper limbs minimally affect the range of movement. The KNN algorithm uses multi-dimensional motion information collected by the sensor, and the test accuracy is 94.59%. Brachioradialis muscle (BM), triceps brachii (TB), and biceps brachii (BB) electromyogram (EMG) signals were evaluated by 5 kg, 10 kg, and 15 kg weight conditions for five subjects, respectively, during lifting, holding, and squatting. Compared with the ULE without assistance and with assistance, the average peak values of EMG signals of BM, TB, and BB were reduced by 19-30% during the whole handling process, which verified that the developed ULE could provide practical assistance under different load conditions.

Ant colony algorithm based fuzzy PID control of unmanned aerial vehicle under wind disturbance conditions

A drone is an unmanned aerial vehicle that has been widely used in military, civil and commercial fields. UAVs need to maintain a smooth and stable flight state during flight to accomplish various tasks, such as reconnaissance, scouting, aerial photography, transportation, and so on. In this paper, both the ant colony algorithm and fuzzy PID control are utilized to investigate the control of quadrotor UAVs under wind disturbance conditions. The optimization of the fuzzy PID control algorithm is conducted through the application of a convolutional neural network under wind disturbance conditions.The system construction and simulation test are conducted using MATLAB and Simulink. The experimental results are analyzed, experimental conclusions are drawn, and the results are compared with those obtained using the traditional PID control algorithm and fuzzy PID control algorithm. This comparison helps demonstrate the extent of optimization achieved by the convolutional neural network on the fuzzy PID control algorithm.The results obtained from comparing the performance with the traditional PID control algorithm and fuzzy PID control algorithm demonstrate the degree of optimization achieved by applying the convolutional neural network to the fuzzy PID control algorithm. The findings indicate that the fuzzy PID control, optimized by the ant colony algorithm, can effectively be utilized for controlling quadrotor UAVs under wind disturbance conditions.

Open Thermal Control System for Stable Polymerase Chain Reaction on a Digital Microfluidic Chip.

In this paper, a digital microfluidic thermal control system was introduced for the stable polymerase chain reaction (PCR). The system consists of a thermoelectric cooler unit, a thermal control board, and graphical-user-interface software capable of simultaneously achieving temperature control and on-chip droplet observation. A fuzzy proportional-integral-derivative (PID) method was developed for this system. The simulation analysis was performed to evaluate the temperature of different reagents within the chip. Based on the results, applying fuzzy PID control for PCR will enhance the thermal stability by 67.8% and save the time by 1195 s, demonstrating excellent dynamic response capability and thermal robustness. The experimental results are consistent with the simulation results on the planar temperature distribution, with a data consistency rate of over 99%. The PCR validation was carried out on this system, successfully amplifying the rat GAPDH gene at a concentration of 193 copies/μL. This work has the potential to be useful in numerous existing lab-on-a-chip applications.

Design of fuzzy PID control system for quad-rotor UAV based on particle swarm algorithm under wind disturbance conditions

This project proposes an intelligent control method that employs a particle swarm algorithm to optimize the rules of a fuzzy controller. The rules of the fuzzy controller are continuously adjusted based on feedback data on the attitude changes of a quadrotor unmanned aerial vehicle and the enhanced particle swarm algorithm. This enables the controller to learn autonomously, thereby enhancing its performance under various conditions. In addition to optimizing the rules of the fuzzy controller, an analysis and modeling process is conducted for the characteristics of wind speed changes under natural conditions. The resulting model is introduced into the system as environmental noise, thereby improving the controller’s performance under different conditions. Experiments are conducted in the Matlab/Simulink simulation environment to test the performance of the control algorithm. The algorithm’s anti-disturbance capability and control accuracy are compared when facing complex disturbances. This research methodology offers new possibilities for precise control of quadrotor unmanned aerial vehicles and provides valuable references for future drone technology development.

Design and stabilization of a Coandă effect-based UAV: Comparative study between fuzzy logic and PID control approaches

Recent years have experienced a notable surge in unmanned aerial vehicles (UAV) research, prompting exploration into innovative concepts. This paper introduces a compact UAV harnessing the Coandă effect, an underexplored phenomenon in fluid mechanics. Featuring a single lift motor and two types of flaps, this UAV offers exceptional maneuverability, presenting significant challenges compared to conventional multi-rotor UAVs. To address these challenges, we explore the theoretical study, mechatronic design, and manufacturing complexities of the Coandă UAV. Emphasizing the distinctiveness of our work, we assess a Fuzzy Logic Controller (FLC) for UAV stabilization, marking the first application of such techniques to a Coandă-effect UAV, in contrast to the Proportional-Integral-Derivative (PID) control employed by other researchers. This innovative application of Fuzzy logic, particularly the Sugeno model, proves advantageous, offering faster and more robust control in uncertain or noisy environments. The proposed FLC strategy is systematically compared against a classical PID control approach, formulated based on the Mamdani and Sugeno models, optimized and manually tuned using a genetic algorithm. Our results showcase a significantly improved settling time of 0.417 s with the FLC strategy, surpassing the PID control approach by 35.23%. To substantiate our findings, we present comprehensive experimentation conducted at both software and hardware levels using Matlab® and Simulink for a microcontroller-based UAV. This groundbreaking fusion of novel design and advanced control techniques not only addresses the unique challenges posed by the Coandă UAV's aerodynamics but also contributes significantly to the field of UAV research.

Modeling, design and control of a 4-arm delta parallel manipulator employing type-1 and interval type-2 fuzzy logic-based techniques for precision applications

This paper presents the virtual design of a four-arm Delta parallel robot for potential applications in the food industry, specifically for the automated preparation of fast foods. Kinematic and dynamic models were developed based on the morphology of this type of robot. Cutting-edge and previously unexplored control strategies for these types of manipulators are then designed and implemented based on Fuzzy PD and Fuzzy PID configurations. Several performance indicators, such as the Integral Square Error (ISE), Integral Time Absolute Error (ITAE) and Integral Time Square Error (ITSE), were used to conduct a performance comparison of the control techniques, considering type-1 and interval type-2 fuzzy sets. In all the analyzed scenarios, the fuzzy controllers correspond to the Takagi–Sugeno–Kang model using linear functions of the inputs in the outputs. Among the main contributions of this work is the development of a detailed dynamic model of the robot in Simscape, incorporating realistic aspects that are often overlooked during analytical modeling. To ensure more accurate results, the rejection of external disturbances is thoroughly analyzed in simulations, considering elements such as time delays and control signal saturations. The results demonstrate the veracity of the proposed design for a four-arm Delta robot, both in models and in the Simscape/Simulink implementation. In the trajectory tracking task and external disturbance rejection, the superiority of the Fuzzy PID controller with interval type-2 fuzzy sets over fuzzy controllers with PD structure and over type-1 fuzzy sets is evidenced.

Research on variable universe fuzzy PID control for semi-active suspension with CDC dampers based on dynamic adjustment functions

The vehicle suspension system is a complex system with multiple variables, nonlinearity and time-varying characteristics, and the traditional variable universe fuzzy PID control algorithm has the problems of over-reliance on expert experience and non-adaptive adjustment of the contracting-expanding factor parameters, which make it difficult to achieve a better control effect. In this paper, the system error e(t) and its change rate ec(t) are introduced into the contracting-expanding factor as dynamic parameters to realize the adaptive adjustment of the contracting-expanding factor parameters, and propose a variable universe fuzzy PID control based on dynamic adjustment functions (VUFP-DAF), which uses the real-time contracting-expanding factor to realize the adaptive adjustment of the fuzzy universe, so as to improve the ride comfort of vehicles. The research results show that the proposed VUFP-DAF has strong adaptability and can effectively improve the ride comfort and handling stability of vehicles under different speeds and road excitations, providing a certain technical basis for the development of the semi-active suspension system.

Stability analysis of braking and control system of pipeline intelligent plugging robot

In the maintenance and emergency repair of oil and gas long-distance pipelines, the pipeline intelligent plugging robot is a new type of intelligent and efficient plugging device inside the pipeline. The stability of its braking and control system determines whether it can successfully seal high-pressure oil and gas inside the pipeline. This research is about intelligent plugging robots for pipelines that use the friction between the rubber hose and the pipe wall for braking. A hydraulic control system is designed, and a braking model for the robot is established. The hydraulic control system is simulated using a numerical simulation method, and the influence of different braking distances on system stability is investigated for various initial velocities of the robot. The results indicate that the fuzzy PID control hydraulic system exhibits a faster response with a maximum overshoot acceleration of −12.8 m/s2. The system achieves the desired acceleration of −0.14 m/s2 within 7 seconds and successfully completes the braking process. The fuzzy PID control approach effectively reduces parameter fluctuations, improves system stability, and decreases acceleration variations when faced with different initial speeds and braking distance signals. Consequently, the positioning accuracy of the robot is enhanced, and power consumption is reduced. This research provides important guidance for the development of new technologies in pipeline maintenance and emergency repair, particularly in the area of plugging operations under pressure.

Fuzzy PID control of permanent magnet synchronous motor electric steering engine by improved beetle antennae search algorithm.

The accuracy of control in permanent magnet synchronous motor system significantly affects overall mechanical structure safety. To satisfy high-performance control for the position servo of the electric steering engine, this study selects a suitable vector control model for permanent magnet synchronous motor. Additionally, an enhanced beetle antennae search algorithm is designed and employed to optimize the fuzzy proportional-integral-derivative controller. The hybrid fuzzy proportional-integral-derivative controller is then implemented in the control model of the permanent magnet synchronous motor, resulting in the establishment of a novel control model for the electric steering engine driven by the permanent magnet synchronous motor. The test results showed that root-mean-square error of this control model was 0.03 mm and 0.02 mm respectively under the conditions of sinusoidal response, square wave response and step response, which was obviously shorter than all the selected control models. In addition, the standard deviation of the control model designed in this study accounted for less than 4% of root-mean-square error of electric steering engine position under the sinusoidal response condition, so the calculation stability was high. The research results show that the designed control model has a certain reference value for improving servo control performance of permanent magnet synchronous motor.

Research and Simulation of Fuzzy Expert Anti-surge Control System for Compressor

In order to solve the problem of compressor surge caused by flow fluctuations that cannot be effectively solved by conventional PID control, a fuzzy expert PID controller was designed based on the analysis of the working principles of fuzzy controllers and expert controllers. This controller has the advantages of good stability and high control accuracy of fuzzy control, as well as the advantage of fast response time of expert controllers. The MATLAB simulation results show that the fuzzy expert PID control algorithm has the characteristics of high stability, fast response speed, and small overshoot compared to conventional PID.

Online optimal tuning of fuzzy PID controller using grey wolf optimizer for quarter car semi-active suspension system

In order to reduce vibration and increase ride comfort, this article utilizes a system of quarter-car suspension integrated with a Fuzzy PID controller. To build and improve the Fuzzy PID controller for the semi-active suspension system used in quarter cars, using a novel meta-heuristic technique known as Grey Wolf Optimizer (GWO). Here the magnetorheological damper (MR) fluid with the Fuzzy PID controller was examined to optimize using the GWO algorithm. With the GWO technique and the integral of time absolute error (IAE) as a fitness function, the three gain parameters of the Fuzzy PID controller – Kp, Ki, and Kd– have been optimally set. The suggested approach has additional advantages for the optimization of functions with three variables, including simplicity in implementation, quick convergence traits, and superior computational capabilities. This work is significant, to the best of the author’s knowledge there is no optimization method using GWO to online tune a Fuzzy PID controller for a semi-active suspension system. The optimal output parameters of the controller can be updated online in real-time by GWO. The performance of the proposed controller was examined by assessing the root mean square (RMS) values and peak-to-peak (PTP) values of body displacement and body acceleration under various road profiles. To ensure that the intelligent controller was of the highest caliber, an online test rig was constructed. Results from simulations and online experiments demonstrated that the Fuzzy GWO PID controller significantly improved ride comfort under a variety of road conditions when compared to the Fuzzy PID controller and passive suspension system.

Research on motor speed control based on the improved genetic algorithm to optimize fuzzy PID

This study aims to optimize the speed control effect of fuzzy PID controller in brushless DC motor control, and improve the traditional genetic optimization algorithm to improve fuzzy PID algorithm, mainly for genetic algorithm coding, crossover, and mutation process. In the three processes of the genetic algorithm, real number field coding is used, Taguchi orthogonal table is introduced into the cross-process, and Gaussian variation is applied to the mutation process. In the part of model construction and verification, this study uses Simulink software to build a brushless DC motor model and sets the same conventional parameters for simulation. The simulation results show that the algorithm overshoot and stability time of the fuzzy PID controller optimized by the improved genetic algorithm are obviously improved.

Modeling and Control of a Linear Piezoelectric Actuator

To improve the output displacement of piezoelectric actuators, a linear piezoelectric actuator based on a multistage amplifying mechanism with a small volume, large thrust, high resolution, high precision, and fast response speed is proposed. However, inherent nonlinear characteristics, such as hysteresis and creep, significantly affect the output accuracy of piezoelectric actuators and may cause system instability. Therefore, a complex nonlinear hysteresis mathematical model with a high degree of fit was established. A Play operator was introduced into the backpropagation neural network, and a genetic algorithm (GA) was used to reduce the probability of the fitting of the neural network model falling into a local minimum. Moreover, simulation and experimental test platforms were constructed. The results showed that the maximum displacement of the actuator was 558.3 μm under a driving voltage of 150 V and a driving frequency of 1 Hz. The complex GA-BP neural network model of the piezoelectric actuator not only exhibited high modeling accuracy but also solved the problems of strong randomness and slow convergence. Compared with other control algorithms, the GA-BP fuzzy PID control exhibited higher control precision.

Fractional-Order Fuzzy PID Controller with Evolutionary Computation for an Effective Synchronized Gantry System

Gantry-type dual-axis platforms can be used to move heavy loads or perform precision CNC work. Such gantry systems drive a single axis with two linear motors, and under heavy loads, a high driving force is required. This can generate a pulling force between the drive shafts in the coupling mechanism. In these situations, when a synchronization error becomes too large, mechanisms can become deformed or damaged, leading to damaged equipment, or in industrial settings, an additional power consumption. Effectively and accurately acquiring the synchronized movement of the platform is important to reduce energy consumption and optimize the system. In this study, a fractional-order fuzzy PID controller (FOFPID) using Oustaloup’s recursive filter is used to control a synchronous X–Y gantry-type platform. The optimized controller parameters are obtained by the measurement of control errors in a simulated environment. Four optimization methods are tested and compared: particle swarm optimization, invasive weed optimization, a gray wolf optimizer, and biogeography-based optimization. The systems were tested and compared in order to optimize the control parameters. Each of the four algorithms is simulated on four contour shapes: a circle, bow, heart, and star. The simulations and control scheme of the experiments are implemented using MATLAB, and the reference paths were planned using non-uniform rational B-splines (NURBS). After running the simulations to determine the optimal control parameters, each set of acquired control parameters is also tested and compared in the experiments and the results are recorded. Both the simulations and experiments show good results, and the tracking of the X–Y platform showed improved performance. Two performance indices are used to determine and validate the relative performance of the models and results.