Hybrid Fuzzy Proportional-integral-derivative Research Articles

Temperature Control System Design Via Fuzzy PID

This paper investigates the integration of Fuzzy Logic with PID (Proportional, Integral, Derivative) controllers to enhance the performance of temperature control systems. Traditional PID controllers, widely used in various applications, often encounter difficulties with nonlinear, time-varying, and uncertain systems, resulting in performance issues like oscillations and overshoots. To mitigate these challenges, the adoption of Fuzzy PID control, which improves adaptability, robustness, and responsiveness by incorporating fuzzy logic into the control mechanism. The study involves simulations using Simulink to compare three distinct control models: conventional PID, standalone Fuzzy Control, and the hybrid Fuzzy PID Control across standardized control scenarios. Our results show that Fuzzy PID controllers significantly outperform traditional methods, especially in complex environments lacking precise mathematical modeling. The enhanced system response times and stability demonstrated by Fuzzy PID controllers suggest substantial potential for broader application across diverse industries, promising considerable improvements in operational efficiency and reliability.

Review on PID, fuzzy and hybrid fuzzy PID controllers for controlling non-linear dynamic behaviour of chemical plants

The chemical production process is tedious due to the integration of different types of equipment and variables. Designing the controller is crucial in the chemical industry due to the interactive and non-linear system behaviour. An intelligent autonomous controller can improve the operating efficiency of the industry. Although several controllers have been developed, different system failures are frequently reported. Hence, controllers such as proportional integral derivative (PID), fuzzy logic controller (FLC), and hybrid fuzzy PID (F-PID) applied in the chemical industries are critically reviewed in the paper. Initially, the PID controller-based approaches are reviewed for different purposes in the chemical industry. After that, the FLC-based controllers-based papers are reviewed. In order to satisfy the issues in both controllers, the H-PID controllers have been reviewed. This review paper will provide an effective solution for operation control in the chemical industry under different operating conditions.

Control of Linear Electric Actuator with Hybrid Fuzzy PID Controller

In this study, the actuator, which converts circular motion to linear motion, is controlled with a smart hybrid controller. The model includes the analysis of electrical and mechanical parts. The controller, on the other hand, has a design methodology that cleverly blends classical PID (Proportional Integral Derivative) and fuzzy controllers. In this design methodology, classical PID and fuzzy controller have an error parameter dependent blending mechanism. In order to compare the performance of the hybrid controller, the coefficients of the classical PI and PID controllers were obtained by tuning and the coefficients with the best response were used. For performance comparison of conventional controllers and hybrid controllers; rise time, settling time, maximum overshoot and common performance indices values are used. The hybrid controller showed higher performance in terms of maximum overshoot, settling time, rising time and performance indices. The potential benefits of using a hybrid controller for the control of linear electric actuators were demonstrated by the fact that it performed better than conventional PID controllers on the majority of evaluation criteria.

Temperature control algorithm for polymerase chain reaction (PCR) instrumentation based upon improved hybrid fuzzy proportional integral derivative (PID) control

Here is reported an adaptive polymerase chain reaction (PCR) temperature control algorithm based on improved hybrid fuzzy proportional integral derivative (PID) control. The algorithm adopts fuzzy control in the rapid temperature changing stage for monitoring and reduces the overshoot. In the constant temperature stage, the PID controller's initial parameters are automatically calculated online through the relay self-tuning algorithm. The output of the system is pre-compensated by feedforward compensation algorithm, and adjusted by the variable universe fuzzy PID algorithm, which avoids the explosion of fuzzy rules to a certain extent. The experimental results show that the average heating rate of the improved hybrid fuzzy PID control algorithm is 4.2 °C/s, with an average cooling rate is 3.2 °C/s. The system stabilizes within 5 s with a maximum overshoot of less than 1.2 °C and a static error of ± 0.1 °C at various ambient temperatures.

Artificial Fuzzy-PID Gain Scheduling Algorithm Design for Motion Control in Differential Drive Mobile Robotic Platforms.

Mobile robots are promising devices which are dedicated to human comfort in all areas. However, the control algorithm of the wheels of mobile robot is entirely challenging due to the nonlinearity. Recently, the classical PID (proportional-integral-derivative) controllers are frequently used in robotics for their high accuracy and the smooth determination of their parameters. A robust approach called fuzzy control which is based on the conversion of linguistic inference sets in a suitable control value is a widely used method in industrial system control in our days. A new challenging method to solve the problem of intelligent navigation of nonholonomic mobile robot is suggested. In this work, the presented methodology is based on three hybrid fuzzy logic PID controllers which are adapted to guarantee target achievement and trajectory tracking. A fuzzy-PID control algorithm is designed with 2 inputs and 3 outputs. By the information given by the system response, error and error derivate can be used to extract and adopt the PID controller parameters: proportional, integral, and derivative gains. Besides, a tuning value A is introduced to improve the resulted response in terms of speeding up and reducing error, overshoot, and oscillation, as well as reducing ISE and IAE values. A modelization of a differential drive mobile robot is presented. The developed algorithm is tested and implemented to this mobile robot model via Simulink/MATLAB.

Selecting appropriate fuzzy PID control structure for power electronic applications

A comprehensive library of Fuzzy Proportional-Integral-Derivative (PID) controllers for power electronic systems has recently presented in the literature. In this library, the authors derived a 27 Fuzzy PID structures and many hybrid Fuzzy PID controllers. This paper is an extended research for the aforementioned work. Here and as a study case, a switched mode power converter (SMPC) application is considered showing the procedure for selecting the applicable structure from the derived FPID library. Such systems require robust, and highly dynamic performance, control schemes to facilitate optimum and high efficiency performance, often under a wide and challenging range of operating conditions. Here, a fuzzy PD + I (FPD + I) structure is directly selected from the derived FPID library. Simulation analysis and experimental validation on a DSP (TMS-320F28335) controlled prototype synchronous DC-DC buck converter demonstrate the superior dynamic performance and voltage regulation of the selected FPID structure compared to the conventional CPID control scheme.

Application of Hybrid Differential Evolution–Grey Wolf Optimization Algorithm for Automatic Generation Control of a Multi-Source Interconnected Power System Using Optimal Fuzzy–PID Controller

This paper deals with an optimal hybrid fuzzy-Proportional Integral Derivative (fuzzy-PID) controller optimized by hybrid differential evolution–Grey Wolf optimization algorithm for automatic generation control of an interconnected multi-source power system. Here a two area system is considered; each area is provided with three types of sources namely a thermal unit with reheat turbine, a hydro unit and a gas unit. The dynamic performance of the system is analyzed under two cases: with AC tie-line and with AC-DC tie-line. The efficiency and effectiveness of the proposed controller is substantiated equally in the two cases. The sturdiness of the system is proved by varying the values of the system parameters. The supremacy of the recommended work is additionally ascertained by comparison with the recently published results like differential evolution optimized PID Controller and hybrid Local Unimodal Sampling-Teaching Learning based Optimization (LUS-TLBO) optimized fuzzy-PID controller. The dynamic performance of the system is observed in terms of settling time, peak overshoot and peak undershoot. Finally the analysis is extended by applying the proposed control technique in two different models namely (i) A three area unequal thermal system considering proper generation rate constraints (GRC) and (ii) A three area hydro-thermal system with mechanical hydro governor. These test results reveal the adaptability of the proposed method in multi-area interconnected power system.

A novel optimized hybrid fuzzy logic intelligent PID controller for an interconnected multi-area power system with physical constraints and boiler dynamics

In the fast developing world nowadays, load frequency control (LFC) is considered to be a most significant role for providing the power supply with good quality in the power system. To deliver a reliable power, LFC system requires highly competent and intelligent control technique. Hence, in this article, a novel hybrid fuzzy logic intelligent proportional-integral-derivative (FLiPID) controller has been proposed for LFC of interconnected multi-area power systems. A four-area interconnected thermal power system incorporated with physical constraints and boiler dynamics is considered and the adjustable parameters of the FLiPID controller are optimized using particle swarm optimization (PSO) scheme employing an integral square error (ISE) criterion. The proposed method has been established to enhance the power system performances as well as to reduce the oscillations of uncertainties due to variations in the system parameters and load perturbations. The supremacy of the suggested method is demonstrated by comparing the simulation results with some recently reported heuristic methods such as fuzzy logic proportional-integral (FLPI) and intelligent proportional-integral-derivative (PID) controllers for the same electrical power system. the investigations showed that the FLiPID controller provides a better dynamic performance and outperform compared to the other approaches in terms of the settling time, and minimum undershoots of the frequency as well as tie-line power flow deviations following a perturbation, in addition to perform appropriate settlement of integral absolute error (IAE). Finally, the sensitivity analysis of the plant is inspected by varying the system parameters and operating load conditions from their nominal values. It is observed that the suggested controller based optimization algorithm is robust and perform satisfactorily with the variations in operating load condition, system parameters and load pattern.

Speed Control of Permanent Magnet Brushless DC Motor Using Hybrid Fuzzy Proportional plus Integral plus Derivative Controller

The main core of this work is to investigate two different digital controllers (Conventional PID Controller and Hybrid Fuzzy PID) on closed loop control of Permanent Magnet Brush Less Direct Current (PMBLDC) Motor. The application of intelligent control (fuzzy logic) technique is attempted in a Proportional –Integral- Derivative (PID) controller for tuning their parameters automatically in an online process. In-order to achieve desired speed, Hybrid Fuzzy-PID provides the finest set of solutions. The conventional PID controller and Hybrid Fuzzy-PID controller performances are analyzed both in steady state and dynamic operating condition with various set point speeds. The rise time, dead time, settling time and steady state error are the parameters considered for comparison. The results prove that, the Hybrid Fuzzy-PID controller offers better performance (Improvement in rise time, Reduction in settling time, No steady sate error) over the conventional PID controller. The simulation makes use of LabVIEW Fuzzy–PID tool and it is implemented in real time through NI-DAQ 6009 with PMBLDC motor which is rated at 36V, 4A, 4000 RPM.

Pitch Control of an Aircraft Using Artificial Intelligence

This paper presents the investigation into the design, simulation and analysis of two autopilots: a fuzzy Proportional-Integral-Derivative (PID) controller and, its hybrid with a PID controller for the control of pitch plane dynamics of an aircraft. The Mamdani-type fuzzy inference system is employed for the Fuzzy Inference System (FIS) in the fuzzy logic controller design. The dynamic modeling of system begins with a derivation of suitable mathematical model to describe the longitudinal motion of an aircraft. This research set the platform for thorough investigation into the various structures available for PID-FLC and its hybrids. Considering hardware implementation challenges and limitations, not all PID-FLC and its hybrid structures are viable. The PID-FLC is constructed as a parallel structure of a PD-FLC and a PI-FLC, with the output signal of the control loop, y serving as the input for the derivative parameter of the PD-FLC. The output of the PID-FLC is formed by algebraically adding the outputs of the two fuzzy control blocks as suggested in Guanrong et al., 2000. Also, the proposed hybrid fuzzy PID autopilot consists of the PID-FLC with a traditional PID controller structured by algebraically adding the outputs of the two control blocks. Result of simulation in MATLAB ® /Simulink ® shows that the proposed PID-FLC autopilot gave an unacceptable

Design and Implementation of Parallel Fuzzy PID Controller for High-Performance Brushless Motor Drives: An Integrated Environment for Rapid Control Prototyping

This paper presents an integrated environment for the rapid prototyping of a robust fuzzy proportional-integral-derivative (PID) controller that allows rapid realization of novel designs. Both the design of the fuzzy PID controller and its integration with the classical PID in a global control system are developed. The architecture of the fuzzy PID controller is basically composed of three parallel fuzzy subcontrollers. Then, the parallel subcontrollers are grouped together to form the overall fuzzy PID controller. The fuzzy proportional, integral, and derivative gains are direct output from the parallel fuzzy subcontrollers and are derived in the error domain. Thus, the proposed architecture presents an alternative to control schemes employed so far. The integrated controller is formulated and implemented in real time, using the speed control of a brushless drive system as a test bed. The design, analysis, and implementation stages are carried out entirely using a dSPACE DS1104 digital-signal-processor-based real-time data acquisition control system and MATLAB/Simulink environment. Experimental results show that the proposed hybrid fuzzy PID controller produces superior control performance than the conventional PID controllers, particularly in handling nonlinearities and external disturbances.