Fuzzy PID Controller Research Articles

Design of sliding mode controller with improved reaching law through self-learning strategy to mitigate the torque ripple in BLDC motor for electric vehicles

Brushless Direct Current (BLDC) motors and their evolution in electric vehicles (EVs) garner attention for their streamlined operation and control methods, enhancing driving range while reducing energy consumption. The primary objective is integrating sliding mode control to enhance BLDC motor capabilities, efficiently regulating speed and torque. The proposed sliding mode (SM) controller mitigates torque ripple and improves dynamic performance amidst fluctuating loads. Speed stabilization and torque control involve comparing reference and predicted speeds, with error feedback integrated into the SM controller. Integrating real-time rules and numerical data with an ANN (Artificial Neural Network)-Fuzzy self-learning algorithm empowers the SM controller to anticipate and mitigate error rates in the BLDC motor, efficaciously modulating motor torque with minimal ripple. Extensive MATLAB/Simulink model-in-loop (MIL) and hardware-in-loop (HIL) simulations demonstrate SM controller superiority over conventional PID, Fuzzy, and PID-Fuzzy controllers, with a rise time of 0.01 s, steady-state error of 0.001 %, settling time of 0.02 s, and peak overshoot of 0.066 %. Moreover, the experiment results show an impressive 29.41 % reduction in torque ripple and the development of efficiency maps to attain the maximum efficiency of 96.12 %. This initiative substantiates that the proposed SM controller to enhance BLDC motor efficiency and EV driving range across real-time conditions.

XK-III: A Spherical Robot with Redundant Degrees of Freedom

The spherical robot XK-III, designed with redundant degrees of freedom, addresses the limitations of existing pendulum spherical robot structures by enhancing mobility and environmental adaptability. A nonlinear dynamic model is developed for XK-III’s new drive structure, along with a nonlinear disturbance observer (NDOB) to mitigate perturbations. Additionally, a Fuzzy PID controller (FPID) is implemented to further enhance XK-III’s environmental adaptability. Experimental results confirm the effectiveness of the new design, showing that XK-III equipped with FPID and NDOB outperforms traditional control systems in terms of anti-disturbance capabilities. This research provides valuable insights for the use of spherical robots in complex environments.

Load Frequency Control of Interconnected Power System with non-linearities using PSO-based Fuzzy PID Control Approach

Load Frequency Control of Interconnected Power System with non-linearities using PSO-based Fuzzy PID Control Approach

Research on Gate Opening Control Based on Improved Beetle Antennae Search.

To address the issues of sluggish response and inadequate precision in traditional gate opening control systems, this study presents a novel approach for direct current (DC) motor control utilizing an enhanced beetle antennae search (BAS) algorithm to fine-tune the parameters of a fuzzy proportional integral derivative (PID) controller. Initially, the mathematical model of the DC motor drive system is formulated. Subsequently, employing a search algorithm, the three parameters of the PID controller are optimized in accordance with the control requirements. Next, software simulation is employed to analyze the system's response time and overshoot. Furthermore, a comparative analysis is conducted between fuzzy PID control based on the improved beetle antennae search algorithm, and conventional approaches such as the traditional beetle antennae search algorithm, the traditional particle swarm algorithm, and the enhanced particle swarm algorithm. The findings indicate the superior performance of the proposed method, characterized by reduced oscillations and accelerated convergence compared to the alternative methods.

Compound Attitude Control Strategy for Reusable Launch Vehicle Based on Improved Particle Swarm Optimization Algorithm

This study introduces an advanced dual-mode compound attitude control framework for reusable launch vehicles (RLVs), underpinned by an enhanced particle swarm optimization (PSO) algorithm. This innovative strategy is tailored to meet the stringent demands for precision and robust anti-interference capabilities across the entire flight envelope of RLVs. The research commences with the formulation of a comprehensive attitude dynamics model and diverse heterogeneous actuator representations, meticulously crafted to reflect the distinct phases of RLV flight. Building upon this foundation, a synergistic control paradigm is engineered, integrating PID and fuzzy PID controllers and dynamically adjusting the inertia weights and learning factors of the PSO algorithm to achieve the balance between global and local optimization performance, complemented by a refined fitness evaluation function. The crux of the study is the application of an upgraded PSO algorithm to fine-tune the controllers’ weighting coefficients, culminating in an optimized dual-mode compound attitude control system. A series of comparative simulation analyses are thoroughly executed to appraise the system’s responsiveness, stability, precision, and resilience to interference. The simulation outcomes demonstrate an average reduction of 42.21% in step response overshoot, an 18.52% decrease in settling time, a 53.18% decline in steady-state error, a 56.80% drop in the maximum deviation value, a 55.82% improvement in recovery speed, and a 75.61% enhancement in tracking precision for the proposed controller. The findings clearly verify the superior performance of the proposed control system, affirming its contribution to the advancement of RLV attitude control. The proposed controller holds promising potential for real application in attitude control systems and is poised to augment the reliability and mission success rate of RLVs under intricate flight scenarios.

Unified Paradigm of Start-Up Strategy for Pumped Storage Hydropower Stations: Variable Universe Fuzzy PID Controller and Integrated Operation Optimization

Unified Paradigm of Start-Up Strategy for Pumped Storage Hydropower Stations: Variable Universe Fuzzy PID Controller and Integrated Operation Optimization

Energy Saving Research on Temperature Control in Data Center Server Rooms

Abstract To tackle the challenge of inefficient traditional control methods and energy wastage caused by temperature disparities within the data center’s server room cooling system, this research initiative initially installed temperature and humidity sensors in each cabinet to gather comprehensive data. Subsequently, a detailed mathematical model of the room’s temperature system was formulated to assess the influence of air supply airflow distribution on the temperature distribution. Following this, through the integration of fuzzy theory with the PID control strategy, a sophisticated fuzzy PID controller was developed. Utilizing Simulink, an in-depth simulation analysis of both PID and fuzzy PID temperature control strategies was executed. The outcomes of the simulations underscored that fuzzy PID control effectively addresses the time delay and stability concerns present in conventional PID temperature control systems. Lastly, empirical validation confirmed that the fuzzy PID control algorithm can proficiently manage the server room’s temperature while concurrently decreasing the intake of cold air, thus promoting energy conservation. This control approach showcases commendable adaptability and robust performance characteristics.

Research on the speed control system of brushless DC motor based on fuzzy PID control

Research on the speed control system of brushless DC motor based on fuzzy PID control

Comparing Temperature and Humidity Control Using PID and Fuzzy Logic in a Climatic Chamber

The presence of a thermohygrometer is important in some places, especially in hospitals and climate room equipment. A climate chamber is an enclosed space or isolated environment, which will provide the environmental conditions of relative humidity and temperature. In accordance with the Decree of the Minister of Health of the Republic of Indonesia. Certain rooms such as rooms in hospitals require special attention to environmental conditions, such as the surgical process that occurs in the operating room. A thermohygrometer is a tool used to monitor room conditions. The thermoygrometer used must be able to trace the measurement results using certain media. A climate chamber is a device that provides the desired climate regardless of the external environment. The purpose of this study was to analyze the comparison of PID and Fuzzy Logic temperature control systems on the Climatic Chamber (Fuzzy Logic System) device which plays a role in the process of measuring room temperature and humidity in the field. The method used in this study was to compare directly with a previously calibrated thermohygrometer. The measurement results at 25°C have a response time of 7 minutes 30 seconds and an overshoot of 0.1°C, at a temperature of 30°C has a response time of 5 minutes 15 seconds and an overshoot of 0.1°C, at a temperature of 35°C has a response time of 5 minutes 30 seconds and 0.2°C overshoot. At 50%RH Humidity has a response time of 13 minutes 30 seconds, at 60%RH Humidity has a 12 minute response time, At 70%RH Humidity has a 6 minute response time. The measurement results show that fuzzy logic control has more advantages than PID control. Fuzzy logic control has a faster response time to setpoint than PID control and fuzzy logic control has smaller overshoot compared to PID control.

Optimization of a fractional-order interval type-2 fuzzy PID controller based on BBO for real-time applications

This paper addresses the development of a robust controller for a nonlinear system to minimize the tracking errors and reduce the effects of external noise and disturbances. To achieve this, the biogeography-based optimization (BBO) algorithm is utilized for an optimally robust interval type 2 fractional-order fuzzy proportional integral derivative controller (BB0-IT2FO-FPID) is proposed. The proposed method offers several advantages. Firstly, the proposed controller is optimized to achieve robustness and minimize tracking errors. Secondly, it effectively handles external noise and disturbances, making it suitable for real-world applications. Thirdly, the use of the BBO algorithm enhances the controller's performance by dynamically adjusting its parameters based on system requirements. The performance of the proposed controller is validated by applying it to control a robotic manipulator, which presents challenges due to its nonlinear characteristics and interacting multi-input multi-output (MIMO) dynamics. Additionally, a real-time evaluation of the proposed controller is conducted by applying it to the speed control of a direct current (DC) machine. The effectiveness of the proposed controller is verified through simulation and practical experiments and comparisons with other optimized controllers, namely FO fuzzy type 1 PID (T1FO-FPID) and FOPID. The simulation and practical results demonstrate the superior performance of the BBO-IT2FO-FPID controller in the presence of system uncertainties and different types of disturbances.

Super-Twisting Sliding Mode and Fuzzy PID Hybrid Control System Integration for Electric Dynamic Load Simulation

In this paper, we present a control approach for an electric dynamic load simulator (EDLS) to obtain fast reaction times and accurate loading. We create an equation for the EDLS and use it to create a controller with the super-twisting sliding mode (STSM) method. The design is based on the constant monitoring inaccuracy of load. To allow dynamic and precise loading, the convergence condition for the finite duration of the STSM control law is established. We experiment with various frequencies and compare the performance of our suggested control method with a Fuzzy PID Control technique to validate its efficacy. The outcomes support the efficiency of our suggested control strategy.

SIMULATION STUDY ON PARAMETRIC MODELLING OF MAGNETORHEOLOGICAL DAMPER SYSTEM

The Magnetorheological (MR) damper system serves as a semi-active suspension system designed to absorb road disturbances. This research aims to replicate the MR damper system through a parametric modeling approach, specifically employing the Simple Bouc-Wen Model, Spencer Model, and Hyperbolic-Tangent Model. The objective of this paper is to assess the effectiveness of these modeled MR damper systems with the utilization of a Fuzzy-PID controller. The simulation process is conducted within MATLAB Simulink, whereby a block diagram is devised based on equations related to the suspension system and the parametric models. The inputs for the system encompass sinusoidal and bump road profiles. The outcomes from the modeled MR damper systems reveal that the semi-active suspension system outperforms the passive suspension system. The primary simulation metrics under consideration include sprung displacement, sprung acceleration, and unsprung acceleration. Among the three MR damper models, the Hyperbolic-tangent model displays the most favorable performance, with a mean squared error of 51.44%, while the Bouc-Wen model exhibits the least favorable performance, registering a mean squared error of 25.96%. The integration of the Fuzzy-PID controller demonstrates an enhancement in the sprung suspension system, though the sprung acceleration remains relatively unchanged.

Research on control strategy of PEMFC air supply system for power and efficiency improvement

Proton exchange membrane fuel cell (PEMFC) is considered as a type of clean and efficient energy device. Fuel cell air supply system is a key component to maintaining the system output efficiency. In this paper, a dynamic control model for PEMFC system oxygen excess ratio (OER) management is built, and an OER control strategy is designed based on Fractional-Order PID. An OER control index mapping the relationship between the OER and the optimal net power under different load currents is proposed to improve the output performance of the system. The results show that Fractional-Order PID can achieve high accuracy and fast response real-time OER regulation under the step current in comparison to the traditional PID and Fuzzy PID control. Furthermore, under the dynamic load current of vehicle, the average relative error of OER is 2.81 % under optimal OER control based on Fractional-Order PID and the power generation efficiency of the PEMFC system can be increased to 52.55 %.

Research on charging strategy based on improved particle swarm optimization PID algorithm

Aiming at the electric vehicle charging pile control system has the characteristics of multi-parameter, strong coupling and non-linearity, and the existing traditional PID control and fuzzy PID control methods have the problems of slow charging speed, poor control performance and anti-interference ability, as well as seriously affecting the service life of the battery, this paper designs a kind of improved particle swarm algorithm to optimize the PID controller of the charging control system for electric vehicle charging piles, and utilizes the improved particle swarm algorithm to Adaptive and precise adjustment of proportional, integral and differential parameters, so that the system quickly reaches stability, so as to improve the accuracy of the system control output current or voltage. Simulation results show that the optimized system response speed of the improved particle swarm algorithm is improved by 3.077 s, the overshooting amount is reduced by 1.01%, and there is no oscillation, which has strong adaptability and anti-interference ability, and can significantly improve the control accuracy and charging efficiency of the charging pile control system.

BA-Optimized Variable Domain Fuzzy PID Control Algorithm for Water and Fertilizer Ratio Control System in Cotton Field

Due to the time-varying, hysteresis and nonlinear characteristics of fertilizer concentration control in the water–fertilizer ratio control system, common control algorithms such as PID and fuzzy PID cannot obtain the expected control effect. In order to accurately control the cotton field water–fertilizer ratio regulation system drip irrigation process of the water–fertilizer ratio that will be controlled within a reasonable range, it is needed to design a bat-optimized variable-domain fuzzy PID water–fertilizer ratio control strategy, through the use of bat algorithm to find out the optimal expansion factor and the best domain of the current conditions, and then according to the changes in working conditions to automatically adjust the fuzzy control of the domain, through the control of the valve openings to change the fertilizer pump back to the amount of water. Realize the fast and precise control of fertilizer concentration in the water–fertilizer ratio control system. Comparative tests were conducted to verify the traditional PID, fuzzy PID, variable domain fuzzy PID and bat-optimized variable-domain fuzzy PID control algorithms. The results show that: if the water–fertilizer ratio is adjusted to 50:1 from the startup, the adjustment time required to reach the target water–fertilizer ratio under the bat-optimized variable-domain fuzzy PID control is 15.29 s, and the maximum overshooting amount is 16.28%, which is a smaller adjustment time and overshooting amount; if the water–fertilizer ratio is adjusted to 40:1 from 50:1, the advantages of bat-optimized variable-domain fuzzy PID are more obvious, with the best balance of response speed, overshooting amount and optimal control effect. In terms of response speed, overshooting amount and regulation time, the optimal balance is achieved, showing the optimal control effect. It is proved that the performance of the water–fertilizer ratio regulation system in cotton field under bat-optimized variable-domain fuzzy PID control designed in this paper can meet the actual production requirements, and these findings can help to develop precise irrigation technology for cotton cultivation under drip irrigation conditions.

Research and Experiment on Cruise Control of a Self-Propelled Electric Sprayer Chassis

In order to address the issues of poor stability in vehicle speed and deteriorated spraying quality caused by changes in road slope and the decrease in overall mass due to liquid spraying, this study focuses on analyzing the structure and longitudinal dynamic characteristics of a 4WID high ground clearance self-propelled electric sprayer. By utilizing MATLAB/Simulink software, three subsystems, namely, the inverse longitudinal dynamics model, torque distribution model, and motor model, are established. The model takes into account the effects of longitudinal driving resistance, slope, and vehicle roll angle on the distribution of loads among the four wheels during slope driving. A seven-degrees-of-freedom dynamic model is developed. A hierarchical control structure is designed, incorporating an upper-level PID controller and a lower-level fuzzy PID controller, to control the overall system. The control algorithms are tailored to the specific characteristics of the sprayer’s operation, and simulation experiments are conducted under the corresponding operating conditions. Building upon this, a sensor-equipped experimental platform is set up in the self-propelled sprayer manufactured by the team in the preliminary stage. Real vehicle tests are conducted in two scenarios: transition transportation and field operations, with the evaluation of the overall vehicle speed serving as the performance metric to validate the correctness of the model and the control theory.

Depth control of bionic robotic fish based on fuzzy PID algorithm

Abstract This paper proposes a staged depth control method based on fuzzy PID control to improve the execution of various underwater tasks by robotic fish in pectoral and caudal fin co-propulsion modes in complex underwater environments. The method obtains appropriate PID parameters and regulates the corresponding CPG models by introducing a fuzzy controller into the PID control framework. The method follows the fuzzification, fuzzy rule setting, and defuzzification steps. During the approach phase, the robotic fish adjusts the amplitude and offset of its pectoral fin. In the cruise phase, it adjusts the angle of attack of the pectoral fin to achieve staged depth control. Simulation and analysis results indicate that the staged fuzzy PID control is more effective than pure fuzzy control and PID control. It reduces delay time by 38.5% and 59.4%, optimizes rise time by 31.1% and 44.1%, reduces peak time by 49.7% and 44.6%, and reduces steady-state error by 42.4% and 76.6%. Therefore, the staged fuzzy PID control provides a faster response and greater stability, making it an effective method for depth control of underwater robots.

Design of fuzzy controller for dynamic positioning based on improved particle swarm algorithm

Abstract The traditional PID controller commonly used in ship dynamic positioning has the defects of difficult online adjustment and weak anti-interference ability. Combining fuzzy control with PID can be a good way to improve the adaptability and accuracy of the controller. However, the parameters of fuzzy PID need to be adjusted by human beings, and there exists a certain uncertainty, which has a great influence on the robustness of the controller. Therefore, the fuzzy PID is optimized using an improved particle swarm algorithm to parameterize the quantized scale factor and rules of the fuzzy PID. Particle swarm optimized fuzzy PID was simulated, and the control of PID and fuzzy PID controllers was compared under different operating conditions, as shown by the experimental results: Under the hydrostatic condition, the convergence speed of the control system is faster; under the load condition, the overshooting amount of the control system is smaller, and the robustness and adaptability have been significantly improved.

Enhancing water level system based on Fuzzy PID control

Enhancing water level system based on Fuzzy PID control

Real time adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural network proportional-integral-derivative controller for nonlinear systems

This paper presents an adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural PID controller for handling the problems of uncertainties in nonlinear systems. The proposed controller combines probabilistic processing with a Takagi-Sugeno-Kang fuzzy neural system to proficiently address stochastic uncertainties in controlled systems. The stability of the controlled system is ensured through the utilization of Lyapunov function to adjust the controller parameters. By tuning the probability parameters of the controller design, an additional level of control is achieved, leading to enhance the controller performance. Furthermore, it can operate without relying on the system's mathematical model. The proposed control approach is employed in nonlinear dynamical plants and compared to other existing controllers to validate its applicability in engineering domains. Simulation and experimental investigations demonstrate that the proposed controller surpasses alternative controllers in effectively managing external disturbances, random noise, and a broad spectrum of system uncertainties.