Fuzzy PID Controller Research Articles

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.

Application of PID and self-tuning fuzzy PID control methods in the control of non-linear magnetic levitation system

Because of the Magnetic Levitation System's (MLS) low energy consumption and little friction two factors that are deemed crucial for unstable and nonlinear systems MLS research is now being conducted in the engineering area. This article discusses the comparison of the performance of control theories used by applying advanced control theories to cope with the complexity of the structure and controllability difficulties of MLS. The control methods compared are Proportional–Integral–Derivative (PID) and Self-Tuning Fuzzy PID (STFPID) methods. These methods were developed in the MATLAB environment. The MLS model created in the MATLAB environment was subjected to the suggested control methods, and the outcomes were compared. The outcomes unequivocally demonstrate that MLS location control may make use of PID and STFPID techniques. Four criteria were used to compare the developed control approaches performances. These are the criteria; rise time, settling time, percent maximum overshoot and overshoot value. It is clearly seen in the results that the STFPID control method provides control of the MLS with greater stability than the PID control method.

Hybrid minigrid system comprising energy storage systems with optimal frequency control empowering the new Egypt large optical telescope site

Proposed hybrid minigrid two area system simulation model encompassing Photo Voltaic (PV) system, Wind Turbines (WT), diesel generators and Energy Storage Systems with intelligent based optimal Frequency Controllers (FCs) empowering the new Egypt large optical telescope (ELOT) site is presented in this research. Technical study and specifications for a PV solar system of 1000 Kw is introduced. Intelligent fine optimized Proportional Integral Derivative (PID) and Fuzzy self-tuned PID (FSTPID) controllers are designed through applying Harris Hawk optimizer (HHO). The proposed HHO employed method performance is validated under number of eventualities which include vacillations of load, sun radiation and wind speed. The objective function and control parameters are the integral time summation of absolute deviations and the parameters of controllers, consecutively. The system dynamic response and simulation results show that the proposed HHO based FSTPID type FCs are effective in reducing frequency and tie line power signals’ deviations in a diminutive time for such minigrid hybrid system. For supplemental validations, simulation results obtained are compared with genetic algorithm to get the proposed controllers’ gains.

Overview and development of PID control

Proportional integral differential control, referred to as PID control, is one of the earliest developed control methods, which is widely used in industrial process control because of its simple control algorithm, high reliability and good robustness. The classic PID controller is the simplest control system, but the actual control object usually has the characteristics of strong interference, transient uncertainty, nonlinearity, etc, so it is difficult to achieve the ideal control effect with the classic PID controller. In addition, with complex parameter tuning methods, it is often difficult to find an optimal result for the parameters of the classical PID controller. These factors lead to the fact that classical PID control cannot be applied to complex systems and high-performance systems, so people continue to develop PID control methods, introduce new models, and develop fuzzy PID controllers, expert PID controllers, neural network PID controllers, and PID controllers based on genetic algorithms. This paper will review the development history of PID controllers, and introduce fuzzy PID controllers, expert PID controllers, neural network PID controllers, and PID controllers based on genetic algorithms. The research status and future development prospects of these controllers are also prospected.

Steering control for handling and stability and wear resistance of multi-axle vehicles

The steering performance of a vehicle directly affects its handling and stability, and tire wear during steering. For multi-axle vehicles equipped with a mechanical hydraulic steering system (MHS), this paper describes a steering-by-wire hydraulic system (SHS) for a third axle that can reduce the steering wear of third-axle tires while improving the handling and stability. A hydraulic steering system based on a reliable self-locking alignment cylinder was designed in this study, and a nonlinear dynamics model of a multi-axle vehicle was developed. In terms of the control strategy, the upper controller was a decision selector with a lateral acceleration threshold of 0.3 g , at which point the system entered the nonlinear kinematic state as a criterion for decision-making. The suboptimal linear time-varying model predictive control (S-LTV-MPC) method or Ackermann steering theory was used to calculate the target steering angle of the third axle. The lower controller was a fuzzy proportional–integral–derivative controller for tracking the steering angle of the third axle. The simulation and test results showed that the S-LTV-MPC method can meet the requirements of real-time control and accuracy. The designed SHS exhibited good handling and stability, and anti-wear properties during steering.

Innovative Fuzzy PID Algorithm: Advancing Quadcopter Control with Adaptive Precision

The integration of fuzzy logic techniques into PID controllers shows promising advantages in quadcopter control. By incorporating fuzzy logic, the PID controller achieves enhanced adaptability and robustness, resulting in more dynamic and resilient control performance. This is achieved through the automatic adjustment of PID parameters based on real-time system feedback, enabling a control strategy that closely resembles human reasoning. Overall, the combination of fuzzy logic and PID control offers significant potential for improving quadcopter control in various environmental conditions.

LQR and Fuzzy-PID Control Design on Double Inverted Pendulum

Double inverted pendulum is a non-linear and unstable system. Double inverted pendulum can be stabilized in the upright position by providing control to the system. In this research we compare two types of controllers namely Linear Quadratic Regulator (LQR) and Fuzzy-PID. The objective is to determine the control strategy that provides better performance on the position of the cart and pendulum angle. We modelled the system which is then linearized and given control. From the simulation results, it is proven that LQR and Fuzzy-PID controllers have been successfully designed to stabilize the double inverted pendulum. However, when given a disturbance in the form of noise step, the LQR controller has not been able to achieve the desired reference for up to 20 seconds. In another hand, the Fuzzy-PID controller is able to achieve the desired reference after 8 seconds. Therefore, it can be concluded that the Fuzzy-PID controller when applied to the Double Inverted pendulum system has better performance than the LQR controller.

A design an optimized fuzzy adaptive proportional-integral-derivative controller for anti-lock braking systems

This paper introduces a new control theory for optimizing anti-lock braking systems (ABS) in automotive applications. Anti-lock systems play a critical role in ensuring vehicle safety during braking by preventing wheel lock-up. However, conventional ABS control algorithms often struggle to adapt to changing road conditions and vehicle dynamics. The error tuning mechanism was not perfect because the occurrence of error is unique based on each application. In response to this challenge, we propose a new approach that combines fuzzy logic and adaptive proportional-integral-derivative (PID) control. The controller more robust to noise and uncertainties in the system. The present research work has concentrated on designing a novel Wavelet Fuzzy adaptive-hybrid Lion-strawberry Proportional-Integral-Derivative (WFA-HLSPID) for the ABS to maximize the control performance while it applied in the vehicle. Simulation results and comparative analysis demonstrate the superior performance of the proposed control theory when compared to traditional ABS systems. The optimized fuzzy adaptive PID control not only enhances vehicle stability during braking but also improves stopping distance. In all cases, the stopping distance, slip rate, and friction have been validated using MATLAB/Simulink.

Enhancing Hybrid Power System Performance with GWO-Tuned Fuzzy-PID Controllers: A Comparative Study

This study explores the implementation of a novel control strategy within hybrid power systems, leveraging a Grey Wolf Optimization (GWO)-tuned Fuzzy Proportional-Integral-Derivative (Fuzzy-P.I.D.) controller to enhance the integration of renewable energy sources. By addressing the critical challenge of grid frequency deviations, this approach significantly bolsters the stability and efficiency of power flow, ensuring a more reliable electricity supply. Employing MATLAB simulations, the research underscores the superior performance of the GWO-tuned Fuzzy-P.I.D. controller, which necessitates fewer control interventions and yields lower oscillation frequencies than its conventional P.I.D. and Fuzzy-P.I.D. counterparts. The robustness of this optimized controller is further validated through extensive tests, demonstrating its resilience across a spectrum of parameter adjustments and operational scenarios, including the hypothetical removal of system components. The findings reveal that this advanced control method markedly surpasses traditional solutions in maintaining stable electricity flow and enhancing the system's overall resilience and adaptability to the variable nature of renewable energy. Thus, the GWO-tuned Fuzzy-P.I.D. controller emerges as a significant innovation in hybrid power system management, heralding a new era of optimization and efficiency in renewable energy integration.

Research on Multi-Mode Control of Electro-Hydraulic Variable Displacement Pump Driven by Servo Motor

The electro-hydraulic power source with an electro-hydraulic variable pump driven by a servo motor is suitable for electrified construction machinery. To achieve better energy efficiency in different working conditions, the multi-mode control scheme was proposed for the electro-hydraulic power source. The control scheme includes pressure control, flow control, and torque control modes. The switching rule among the three control modes was formulated based on the minimum pump pressure. The fuzzy PID controller was designed, and a composite flow regulation strategy was formulated, including the load-sensitive adaptive displacement regulation and servo motor variable speed regulation. The AMESim-Simulink co-simulation model of multi-mode control was established. The test platform was built, and the experimental study was carried out. The results indicate that the fuzzy PID control has a shorter response time and a more stable control effect compared with PID control. Additionally, the composite flow regulation strategy improves the flow regulation range by 36% and reduces the flow overshoot by 20% compared with the load-sensitive adaptive displacement regulation. As the main control valve received an opening step signal, the full flow regulation (7~81 L/min) of the power source took approximately 0.5 s to rise and 0.2 s to fall. The relative error of pressure difference for the main control valve was 0.63%. When receiving the pressure and torque step signal, the pump pressure and pump input torque both took approximately 0.45 s to rise and 0.2 s to fall. The relative errors of pump pressure and torque control were 0.2% and 0.16%, respectively. In the multi-mode control, the electro-hydraulic power source could switch smoothly between flow control mode, pressure control mode, and torque control mode. These results provide a reference for the multi-mode control of an electro-hydraulic power source with an electro-hydraulic variable pump driven by a servo motor.