Performance Of Proportional Integral Derivative Controller Research Articles

Design a robust intelligent power controller for pressurized water reactor using particle swarm optimization algorithm

Abstract Proportional–Integral–Derivative (PID) controllers have been optimized and used to overcome many types of problems in nuclear reactor systems. The high performance of PID controllers depend on optimizing their gains. In this research, an optimized robust PID controller is proposed to control power perturbations in a pressurized water reactor (PWR). The optimization process of robust PID using particle swarm optimization (PSO) algorithm aims to adapt PID gains then after that, H-infinity controller is used. The results show a good performance when that suggested hybrid controller is applied to the nuclear power system since the suggested design makes the system robust due to applying H-infinity method, in addition to get the benefits of the optimized PID controller.

Fuzzy adaptive impedance control for the two-layered vertical cable-driven parallel robot

This study unveils a novel two-layered vertical octahedron cable-driven parallel robot (TLVO CDPR), distinctively engineered for effective force interactions with vertical surfaces while preventing collision with cables. It pioneers an innovative control strategy integrating a position-based fuzzy adaptive impedance controller with a fuzzy Proportional – Integral – Derivative (PID) controller, adeptly managing both the pose and contact force of the robot. While dual control application is often found in rigid-link robots, it remains a largely unexplored frontier in the realm of CDPRs, despite its critical importance in sectors like manufacturing and assembly. The fuzzy adaptive mechanism significantly boosts impedance control efficacy in the face of unpredictable, non-uniform working surfaces, ensuring algorithmic stability and convergence. Concurrently, fuzzy logic is harnessed to optimize PID controller performance. The forward kinematics challenge is efficiently tackled using a least squares method coupled with an Inertial Measurement Unit (IMU), ensuring swift and precise solutions. The robustness and adaptability of the robot and its control systems are thoroughly validated through extensive experimental trials, involving diverse trajectories and varying uncertainties on vertical working surfaces.

Neural network-based self-tuning control for hybrid electric vehicle engines

Combustion engine speed control faces significant uncertainties in parameters like mass equivalent coefficient and efficiency. To address these challenges, this research develops an adaptive, self-tuning artificial neural network (ANN) control design that aims to optimize engine speed despite load torque and model uncertainties. We integrate three controllers—linear quadratic regulator (LQR), proportional-integral-derivative (PID), and sliding mode controller (SMC)—with machine learning techniques to evaluate their performance in hybrid electric vehicle engines. The ANN-based self-tuning controller identifies the most effective method for maintaining idle engine speed with minimal deviation. The results indicate that the ANN can accurately predict settling times and enhance controller performance, leading to reduced settling time errors, improved fuel economy, and better emissions performance. The study highlights LQR's high sensitivity in the lower settling time range and reduced sensitivity in the higher range, the PID controller's varied sensitivity, and SMC's consistent sensitivity across both ranges. A well-trained ANN is essential for optimizing control parameters and maintaining system stability. The ANN was trained and validated using the nntool MATLAB toolbox, with tests conducted across lower and higher settling time ranges. Results showed that the PID controller's performance was overfitted by insufficient training in the lower range, while the LQR and SMC demonstrated potential for more efficient control in both ranges. Overall, the study underscores the importance of a proficiently trained ANN for achieving precise control in hybrid electric vehicle engines.

Modeling and Implementation of PID Controller in Vehicle Ride Comfort Improvement Using Bond Graph

The proportional integral derivative (PID) controller method is a famous and influential approach used in a variety of engineering domain systems. This paper presents Bond Graph modelling and implementation of PID controller in the active quarter-vehicle suspension system for the purpose of achieving better ride comfort. Bond graph is a better tool for modelling multi-domain energy systems with control hardware associated with it. SYMBOL SHAKTI software is used to simulate the active vehicle suspension system, which is theoretically represented as a 4-DOF human Bio-dynamic model combined with a 3-DOF quarter automobile system. The suggested PID Controller's performance is contrasted with that of the passive model. To measure the effectiveness of the suggested PID controller suspension system, time domain evaluations of systems performance criteria are conducted. The findings show that a significant improvement in ride comfort is offered by the suggested Bond graph PID controller model of the active vehicle suspension.

Pole placement tuning of proportional integral derivative feedback controller for knee extension model

Functional electrical stimulation (FES) has shown potential in rehabilitative exercises for patients recovering from spinal cord injuries. In recent developments, conventional open-loop FES control techniques have evolved into closed-loop systems that employ feedback controllers for automation. However, closed-loop FES systems often face challenges due to muscle non-linear effects, such as fatigue, time delays, stiffness, and spasticity. Therefore, an accurate non-linear knee model is required during the design stage, and precise tuning of the feedback controller parameters is vital. A proportional– integral–derivative (PID) controller is commonly used as a feedback controller due to its simplicity and ease of implementation. However, most PID tuning methods are complex and time consuming. This paper investigates the viability of employing the pole placement technique for tuning a PID controller that regulates the non-linear knee extension model. The pole placement method aims to improve the control and adaptability of the PID controller in closed-loop FES systems, specifically by facilitating knee extension exercises. MATLAB Simulink was used to assess the effectiveness of this tuning approach. Results showed that the PID controller performed satisfactorily without non-linearities, but performance varied with the inclusion of specific non-linearities. The pole placement tuning method facilitated preliminary assessments of PID controller performance, preceding highly advanced optimization.

Nonlinear Optimized PID Vibration Control of Thermal-Dependent FG Composite Porous Plates Reinforced by Agglomerated CNTs

This research study aims to delve into the effects of carbon nanotubes (CNTs) agglomeration on the dynamic characteristics of smart functionally graded (FG) porous sandwich plates. The structure of the sandwich plate comprises a core layer that incorporates dispersed CNTs within a polymer matrix. In addition, two layers are equipped with piezoelectric sensors and actuators. The agglomeration of CNTs is mathematically modeled using the Eshelby–Mori–Tanaka approach, accounting for both complete and partial agglomeration states. Moreover, the study takes into account the thermal-dependent behavior of CNTs. Subsequently, an optimal nonlinear proportional-integral-derivative (PID) control scheme based on the Bat optimization algorithm is applied to mitigate vibrations within the composite structure. Unlike the fixed gains of the classical PID, the nonlinear version dynamically adjusts its parameters in real time, ensuring enhanced responsiveness and stability. Furthermore, a comprehensive numerical investigation is conducted to assess the impact of several parameters on the natural frequencies in the frequency domain. These parameters encompass porosity distributions, porosity coefficients, reinforcement patterns, weight fractions of nanofillers, temperature, and the agglomeration of CNTs. The vibration attenuation performance of both nonlinear and classical PID controllers is evaluated through numerical simulations. The findings indicate the robustness and rapid disturbance rejection of the proposed control scheme.

A Method of Anti-Windup PID Controller for a BLDC-Drive System

This research aims to enhance control systems for Brushless DC (BLDC) motors by introducing Proportional-Integral-Derivative (PID) control as a straightforward yet reliable solution, known for its precision, quick responsiveness, and stability. Emphasizing its suitability for BLDC motor speed control, the study addresses PID controller windup challenges, highlighting anti-windup techniques crucial for maintaining system stability. The primary focus is on improving the performance of an anti-windup PID controller for BLDC motor speed control in electric vehicles. Through simulations and analyses, the research aims to minimize steady-state error and overshooting, contributing to overall operational efficiency. Practical implementation involves optimizing the PID anti-windup controller's gain using the MATLAB PID Tuner and implementing it in the Arduino IDE. Experimental tests, which cover constant and varying step function scenarios, are conducted on the designed hardware. Results show the PID anti-windup controller's superiority, exhibiting significantly lower overshoot values of 5.42% and 3.35% compared to 13.26% and 23.76%, respectively. Despite its higher control action, the traditional PID controller displays a more pronounced overshoot. This research is a significant step toward advancing control systems for electric vehicles and optimizing BLDC motor performance in practical applications.

An Adaptive PID Control System for the Attitude and Altitude Control of a Quadcopter

Abstract In adaptive model-based control systems, determining the appropriate controller gain is a complex and time-consuming task due to noise and external disturbances. Changes in the controller parameters were assumed to be dependent on the quadcopter mass, which was the process variable. A nonlinear model of the plant was used to identify the mass, employing the weighted recursive least squares (WRLS) method for online identification. The identification and control processes involved filtration using differential filters, which provided appropriate derivatives of signals. Proportional integral derivative (PID) controller tuning was performed using the Gauss–Newton optimisation procedure on the plant. Differential filters played a crucial role in all the developed control systems by significantly reducing measurement noise. The results showed that the performance of classical PID controllers can be improved by using differential filters and gain scheduling. The control and identification algorithms were implemented in an National Instruments (NI) myRIO-1900 controller. The nonlinear model of the plant was built based on Newton’s equations.

Compressor speed control design using PID controller in hydrogen compression and transfer system

Hydrogen is an energy carrier which can be processed by high pressure compressor and they can be transported, stored and converted to electricity for later use. This paper proposes a hydrogen compression model development and modeling of hydrogen transportation between two tanks using MATLAB software version 22. The proposed model provides amount of hydrogen required in volumes (m3) and compressor power required in (KW) for compressor speed of 500 rad/s, 1000 rad/s and 1500 rad/s. This model provides hydrogen volume of 1 m3 and 10 KW compressor power requirement at 500 rad/s compressor speed. For compressor speed of 1000 rad/s, the proposed model provides hydrogen volume of 10 m3and 20 KW compressor power requirements and for 1500 rad/s this model provides volume of 30 m3and 30 KW compressor power requirements which indicates that the increase in compressor speed increases hydrogen volume generated and increase the power requirement also. For maintaining compressor speed at desired value, a PID (Proportional + Integral + Derivative) controller has been designed and the results were compared with Proportional (P), PI (Proportional + Integral), and PD (Proportional + Derivative) controllers. PID controller performance can be measured using the parameters delay time and settling time. Low values of settling time and delay time indicate the better performance of PID controller. P controller achieves the desired compressor speed with delay time of 228 ms; settling time of 1250 s. PI controller achieves the desired compressor speed with delay time of 210 ms, settling time of 1210 s. PD controller achieves the desired compressor speed with delay time of 185 ms, settling time of 1280 s. PID controller provides better speed regulation with low delay time of 110 ms and settling time of 1120 s when compared with P, PI, PD controllers. From the simulation results it is observed that PID controller can be a good option for slow process like hydrogen gas flow through pipeline for effective speed regulation.

Design of the PID temperature controller for an alkaline electrolysis system with time delays

Electrolysis systems use proportional–integral–derivative (PID) temperature controllers to maintain stack temperatures around set points. However, because of heat transfer delays in electrolysis systems, the manual tuning of PID temperature controllers is time-consuming, and temperature oscillations often occur. This paper focuses on the design of the PID temperature controller for an alkaline electrolysis system to achieve fast and stable temperature control. A thermal dynamic model of an electrolysis system is established in the frequency domain for controller designs. Based on this model, the temperature stability is analysed by the root distribution, and the PID parameters are optimized considering the temperature overshoot and the settling time. The performance of the optimal PID controllers is experimentally verified. Furthermore, the simulation results show that the before-stack temperature should be used as the feedback variable for small lab-scale systems to suppress stack temperature fluctuations, and the after-stack temperature should be used for larger systems to improve the economy. This study helps ensure the temperature stability and control of electrolysis systems.

A self-tuning PID controller based on analog-digital hybrid computing with a double-gate SnS2 memtransistor.

Most commercial drones utilize a traditional proportional-integral-derivative (PID) controller because of its design simplicity. However, the traditional PID controller has certain limitations in terms of optimality and robustness; it is difficult to actively adjust the PID gains under some disturbances. In this study, we demonstrated an analog-digital hybrid computing platform based on double-gate SnS2 memtransistors to implement a self-tuning/energy-efficient PID controller in drones. The customized analog circuit with memtransistors executes the PID control algorithm with low power consumption; we experimentally verified that the energy consumption of the proposed hybrid computing-based PID controller is only 63% of that of the traditional PID controller. In addition, the precise tunability of analog conductance states in the memtransistor proved to be capable of reconfiguring the performance of the PID controller, where the developed self-tuning algorithm can automatically find the optimal PID control performance.

Combined strategy for tuning sensor-less brushless DC motor using SEPIC converter to reduce torque ripple

The brushless DC motor (BLDCM) is widely used in computer numerical control (CNC) machines, aerospace applications and auto industry applications in the field of robotics. But it is still affected by the transmission torque ripple, which mostly depends on the speed and the transient line current at the transmission interval. This manuscript proposes a combined approach for tuning sensor-less brushless DC (BLDC) motors using a single-ended primary-inductor converter (SEPIC). The proposed technique is a combination of Golden Eagle Optimization (GEO) and Radial Basis Function Neural Network (RBFNN), hence it is called GEO-RPFNN. The control of speed and torque is to reduce the torque ripple in the motor. Here, the modified bridgeless single-ended primary-inductor converter is proposed to improve speed and torque control. The proposed method is used to adjust the parameters of proportional integral derivative (PID) controller and to improve the performance of PID controller. Therefore, the GEO–RBFNN technique is proposed to recover the control loop function. The proposed algorithm is explored to control the speed and torque error as BLDC motor. Nevertheless, the output of the proposed approach is subject to the input of speed and torque controllers. The proposed method is executed in MATLAB Simulink site. The performance of the proposed system is compared with existing FA and PSO methods. As per the state of comparison outcomes, the GEO–RBFNN gives better result than the existing techniques which has higher ability to conquer the related issues. The THD in stator current, power factor and torque ripple gives the value using proposed method is 1.26%, 0.9951 and 7.4.

Evaluation of a Proportional-Integral-Derivative Controller for Hemorrhage Resuscitation Using a Hardware-in-Loop Test Platform.

Hemorrhage is a leading cause of preventable death in trauma, which can often be avoided with proper fluid resuscitation. Fluid administration can be cognitive-demanding for medical personnel as the rates and volumes must be personalized to the trauma due to variations in injury severity and overall fluid responsiveness. Thus, automated fluid administration systems are ideal to simplify hemorrhagic shock resuscitation if properly designed for a wide range of hemorrhage scenarios. Here, we highlight the development of a proportional–integral–derivative (PID) controller using a hardware-in-loop test platform. The controller relies only on an input data stream of arterial pressure and a target pressure; the PID controller then outputs infusion rates to stabilize the subject. To evaluate PID controller performance with more than 10 controller metrics, the hardware-in-loop platform allowed for 11 different trauma-relevant hemorrhage scenarios for the controller to resuscitate against. Overall, the two controller configurations performed uniquely for the scenarios, with one reaching the target quicker but often overshooting, while the other rarely overshot the target but failed to reach the target during severe hemorrhage. In conclusion, PID controllers have the potential to simplify hemorrhage resuscitation if properly designed and evaluated, which can be accomplished with the test platform shown here.

Kalman Filter to Improve Performance of PID Control Systems on DC Motors

A proportional–integral–derivative (PID) controller is a type of control system that is most widely applied in industrial world. Various tuning models have been developed to obtain optimal performance in PID control. However, the methods are designed under ideal circumstances. This means that the control system which has been built will not work optimally when noise exists. Noise can come from electrical vibrations, inference of electronic components, or other noise sources. Thus, it is necessary to design PID control system that can work optimally without being disturbed by noise. In this research, Kalman filter was used to improve the performance of PID controllers. The application of Kalman filter was used to reduce the noise of the input signal so that it could generate output signal which is in accordance with the expected output. Simulation result showed that the PID performance with Kalman filter was more optimal than the ordinary one to minimize the existing noise. The resulting speed of DC motor with Kalman filter had a lower overshoot than PID control without Kalman filter. This method resulted lower integral of absolute error (IAE) than ordinary PID controls. The IAE value for the PID controller with the Kalman filter was 25.4, the PID controller with the observer was 31.0, while the IAE value in the ordinary controller was only 60.9.

Deep Reinforcement Learning Based Controller Design for Model of The Vertical Take off and Landing System

In this study, the Deep Deterministic Policy Gradient (DDPG) algorithm, which consists of a combination of artificial neural networks and reinforcement learning, was applied to the Vertical Takeoff and Landing (VTOL) system model in order to control the pitch angle. This algorithm was selected because conventional control algorithms such as Proportional Integral Derivative (PID) controllers which cannot always generate a suitable control signal eliminating the disturbance and unwanted environment effects on the considered system. In order to control the system, training was carried out for a sinusoidal reference in the mathematical model of the VTOL system in the Simulink environment, through the DDPG algorithm with continuous action space from deep reinforcement learning methods that can produce control action values that take the structure that can maximize the reward according to a determined reward function for the purpose of control and the generalization ability of artificial neural networks. For sinusoidal reference and a constant reference, tracking error performances obtained for the pitch angle, which is the output for the specified VTOL system, were compared with the conventional PID controller performance in terms of mean square error, integral square error, integral absolute error, percentage overshoot and settling time. The obtained results are presented via the simulations studies.

Quadratic Model-Based Dynamically Updated PID Control of CSTR System with Varying Parameters

In this paper, we discuss an improved version of the conventional PID (Proportional–Integral–Derivative) controller, the Dynamically Updated PID (DUPID) controller. The DUPID is a control solution which preserves the advantages of the PID controller and tends to improve them by introducing a quadratic error model in the PID control structure. The quadratic error model is constructed over a window of past error points. The objective is to use the model to give the conventional PID controller the awareness needed to battle the effects caused by the variation of the parameters. The quality of the predictions that the model is able to deliver depends on the appropriate selection of data used for its construction. In this regard, the paper discusses two algorithms, named 1D (one dimensional) and 2D (two dimensional) DUPID. Appropriate to their names, the former selects data based on one coordinate, whereas the latter selects the data based on two coordinates. Both these versions of the DUPID controller are compared to the conventional PID controller with respect to their capabilities of controlling a Continuous Stirred Tank Reactor (CSTR) system with varying parameters in three different scenarios. As a quantifying measure of the control performance, the integral of absolute error (IAE) metric is used. The results from the performed simulations indicated that the two versions of the DUPID controller improved the control performance of the conventional PID controller in all scenarios.

Performance and Configuration Analysis of Tracking Time Anti-Windup PID Controllers

As popular as the application of Proportional Integral Derivative (PID) controller is, issues relating to saturation effects are still being addressed using different techniques. Amongst such techniques are clamping anti-windup technique and back-calculation anti-windup techniques which primary prevent the integral term of the PID control action from reaching saturation. Separate tracking time technique was applied to both cases of anti-windup techniques investigated in this research unlike the conventional tracking time. These anti-windup controllers were used to control the operation of a motorized globe valve. The results obtained after simulation in MATLAB Simulink environment showed that both techniques gave similar outputs with a stable response of magnitude 0.95 at 1.5 seconds settling time when a unit step reference input signal was applied as compared to conventional PID controller that had an overshoot of 1.04 before settling to a magnitude of 1.0 at 1.5 seconds. Vibration, instability, and operational distortion were experienced when the anti-windup techniques were cascaded. The same responses were obtained when their outputs were combined to control the motorized globe valve. Other interesting mathematical models of important components are contained in the full paper.

Bulanık Mantık Denetleyicisiyle Sürü İnsansız Hava Aracıları (İHA)’nın Rota Takip Performansı

Swarm Unmanned Aerial Vehicles (UAVs) comprise of a group of aircraft that come together to achieve a specific goal. In recent years, the Swarm UAVs have been used in commercial, civil and military fields such as search and rescue operations, cargo transportation, sensitive agricultural practices, and ammunition delivery to war zones. Swarm UAVs can scan large areas in a short time in both military and civilian use. Swarm UAVs, which have the ability to communicate synchronously with each other, can perform complex tasks in a minimum energy and time by collaborating with respect to a single UAV. It is very important that swarm UAVs can follow the desired route with minimum error in order to perform the task in the shortest time and with least energy. In this study, the fuzzy logic controller is proposed for swarm quadrotors to follow the desired route with minimum error. The system modeling and mathematical equations of quadrotor have been developed in simulation environment. The performance of swarm UAVs to follow the rectangular and circular routes with minimum error is analyzed in this simulation. The fuzzy logic controller proposed for route tracking of the swarm UAVs is handled comparatively with the classical proportional-integral-derivative (PID) controller. The fuzzy logic controller developed in this simulation study increases the UAV’s sudden maneuverability and ability to complete the task with minimum energy compared to the classical PID controller. The classical PID and fuzzy controller performance of each UAV in the swarm is analyzed graphically and it is observed that the performance of the fuzzy logic controller to follow the reference route is higher than the classical PID controller.

PID controller for microsatellite yaw-axis attitude control system using ITAE method

The need for effective design of satellite attitude control (SAC) subsystem for a microsatellite is imperative in order to guarantee both the quality and reliability of the data acquisition. A proportional-integral-derivative (PID) controller was proposed in this study because of its numerous advantages. The performance of PID controller can be greatly improved by adopting an integral time absolute error (ITAE) robust controller design approach. Since the system to be controlled is of the 4 th order, it was approximated by its 2 nd order version and then used for the controller design. Both the reduced and higher-order pre-filter transfer functions were designed and tested, in order to improve the system performance. As revealed by the results, three out of the four designed systems satisfy the design specifications; and the PD-controlled system without pre-filter transfer function was recommended out of the three systems due to its structural simplicity, which eventually enhances its digital implementation.

Enhanced performance of PID controllers for unstable time delay systems using direct synthesis method

ABSTRACT Design proportional–integral–derivative (PID) controllers for unstable first-order plus time delay (FOPTD) and unstable second-order plus time delay (SOPTD) systems with/without a zero is proposed by direct synthesis method. For unstable FOPTD system, a series PID controller is considered. The derivative time of series PID controller is made equal to 0.5 times time delay to cancel the numerator zero with the pole of the loop transfer function (product of process transfer function and controller transfer function). The reduced characteristic equation is compared with the desired characteristic equation to get PI settings. The conventional PID controller parameters are obtained from series PID controller. For unstable SOPTD systems, PID controller with a first-order lead/lag compensator is considered. The controllers designed by the proposed method are implemented on various transfer function models, nonlinear model equations of bioreactor and on inverted pendulum (IP) set-up to show the efficiency of the proposed method. The performance of the proposed controllers is compared with the controllers reported in the recent literature in terms of IAE. The robustness of the controller is determined by the maximum magnitude of sensitivity function, phase margin and gain margin.