Proportional Integral Derivative Controller Parameters Research Articles

Adjusting laser power to control the heat generated by nanoparticles at the site of a patient's cells.

Cancer treatment often involves heat therapy, commonly administered alongside chemotherapy and radiation therapy. The authors address the challenges posed by heat treatment methods and introduce effective control techniques. These approaches enable the precise adjustment of laser radiation over time, ensuring the tumour's core temperature attains an acceptable level with a well-defined transient response. In these control strategies, the input is the actual tumour temperature compared to the desired value, while the output governs laser radiation power. Efficient control methods are explored for regulating tumour temperature in the presence of nanoparticles and laser radiation, validated through simulations on a relevant physiological model. Initially, a Proportional-Integral-Derivative (PID) controller serves as the foundational compensator. The PID controller parameters are optimised using a combination of trial and error and the Imperialist Competitive Algorithm (ICA). ICA, known for its swift convergence and reduced computational complexity, proves instrumental in parameter determination. Furthermore, an intelligent controller based on an artificial neural network is integrated with the PID controller and compared against alternative methods. Simulation results underscore the efficacy of the combined neural network-PID controller in achieving precise temperature control. This comprehensive study illuminates promising avenues for enhancing heat therapy's effectiveness in cancer treatment.

Tuning the Proportional-Integral-Derivative Control Parameters of Unmanned Aerial Vehicles Using Artificial Neural Networks for Point-to-Point Trajectory Approach.

Nowadays, trajectory control is a significant issue for unmanned micro aerial vehicles (MAVs) due to large disturbances such as wind and storms. Trajectory control is typically implemented using a proportional-integral-derivative (PID) controller. In order to achieve high accuracy in trajectory tracking, it is essential to set the PID gain parameters to optimum values. For this reason, separate gain values are set for roll, pitch and yaw movements before autonomous flight in quadrotor systems. Traditionally, this adjustment is performed manually or automatically in autotune mode. Given the constraints of narrow orchard corridors, the use of manual or autotune mode is neither practical nor effective, as the quadrotor system has to fly in narrow apple orchard corridors covered with hail nets. These reasons require the development of an innovative solution specific to quadrotor vehicles designed for constrained areas such as apple orchards. This paper recognizes the need for effective trajectory control in quadrotors and proposes a novel neural network-based approach to tuning the optimal PID control parameters. This new approach not only improves trajectory control efficiency but also addresses the unique challenges posed by environments with constrained locational characteristics. Flight simulations using the proposed neural network models have demonstrated successful trajectory tracking performance and highlighted the superiority of the feed-forward back propagation network (FFBPN), especially in latitude tracking within 7.52745 × 10-5 RMSE trajectory error. Simulation results support the high performance of the proposed approach for the development of automatic flight capabilities in challenging environments.

Improving precision and robustness in level control of coupled tank systems: A tree seed optimization and [formula omitted]-analysis approach

Background:Efficient control of liquid levels in interconnected tank systems is a fundamental challenge in various industries, including chemical processes, wastewater treatment, and manufacturing. The traditional approach to achieving precise control has relied on Proportional–Integral–Derivative (PID) controllers, whose performance largely depends on tuned parameters. However, tuning PID controllers for complex and nonlinear systems like coupled tanks remains a challenging task. In recent years, nature-inspired optimization algorithms have gained prominence in control system design. The tree seed optimization (TSO), inspired by the dispersion of tree seeds in search of optimal growth conditions, has shown promise in solving complex optimization problems. This study seeks to explore the application of TSO in tuning PID controllers for coupled tank systems. Methodology:This research employs the TSO to optimize PID controller parameters for enhanced liquid-level control in coupled tank systems. The optimization process involves integrating performance metrics and closed-loop gain constraints to achieve optimal control of coupled tank systems. The imposed constraints aim to ensure robustness and system stability in the face of uncertainties and disturbances. Besides, μ analysis quantifies the robustness of the system by assessing its ability to tolerate uncertainties. Findings:Simulation studies conducted in this research verify the efficacy of the proposed TSO-based approach for tuning PID controllers in coupled tank systems. Compared with various methods, the TSO consistently yields better control performance, reduces settling time, and minimizes overshoot. The robustness of the optimized controllers is also evaluated, showing the ability to handle varying operating conditions effectively.

Development of AVR controller performance using exponential distribution and transit search optimization techniques

This paper attempts to obtain the optimal solution to enhance the performance of the Automatic Voltage Regulator (AVR) Controller, as it is an essential tool to control the synchronous generator output voltage. The controller improves AVR system stability and response time; moreover, it is demonstrated that the Proportional Integral Derivative (PID) controller achieves the goal by applying two artificial intelligence techniques to design the optimal values of the Automatic Voltage Regulator (AVR) PID controller for a single area model. The first is the Exponential Distribution Optimization Algorithm (EDO), and the second is the Transit Search Optimization Algorithm (TS). EDO and TS are used to determine the best PID controller parameters and have also recently been developed in the breadth of optimization problems and associated computational complexities field. The objective function, Integrated Square Error (ISE), minimizes the error voltage for improved stability and response. The outcomes are compared to various optimization techniques to prove the validation of the two proposed methods. The results show that the EDO and TS proved their superiority through their stability level to the AVR system and their steady-state error improvement. Moreover, the dominant effect of damping frequency decreases the oscillation and the reduced maximum overshoot that protects the loads from being subjected to non-permissible over-voltage levels. Finally, a robustness test is applied to the two proposed optimization methods to prove their reliability and effectiveness.

An Automatic Control Perspective on Parameterizing Generative Adversarial Network.

This article presents a new perspective from control theory to interpret and solve the instability and mode collapse problems of generative adversarial networks (GANs). The dynamics of GANs are parameterized in the function space and control directed methods are applied to investigate GANs. First, the linear control theory is utilized to analyze and understand GANs. It is proved that the stability depends only on control parameters. Second, a proportional-integral-derivative (PID) controller is designed to improve its stability. GANs can be controlled to adaptively generate images by an overshoot rate that is only related to the PID control parameters. Third, a new PIDGAN is derived with a theoretical guarantee of stability. Fourth, to exploit the nonlinear characteristics of GANs, the nonlinear control theory is applied to further analyze GANs and develop a feedback linearization control-based PIDGAN named NPIDGAN. Both PIDGAN and NPIDGAN not only improve stability but also prevent mode collapse. With five datasets covering a wide variety of image domains, the proposed models achieve superior performance with 1024 × 1024 resolution compared with the state-of-the-art GANs, even when data are limited.

Forecasting Tariff Rates and Enhancing Power Quality in Microgrids: The Synergistic Role of LSTM and UPQC

The current paper presents an original approach into the microgrid control framework by incorporating LSTM-based optimization with specific emphasis on refining the gain parameters of a Proportional-Integral-Derivative (PID) controller. This integration represents a significant advancement in improving the overall efficiency of microgrid control systems. By creatively applying LSTM optimization, the paper achieves dynamic adjustments of the PID controller's parameters, resulting in more precise regulation of output power quality. Through the utilization of the Unified Power Quality Conditioner (UPQC) in conjunction with LSTM-based optimization, the paper establishes a compelling link between improved power quality and the resultant tariff rates. This highlights their combined influence on enhancing power quality and calibrating tariff rates, providing a fresh perspective on optimizing microgrid operations.

A Multi-Source Power System’s Load Frequency Control Utilizing Particle Swarm Optimization

Electrical power networks consist of numerous energy control zones connected by tie-lines, with the addition of nonconventional sources resulting in considerable variations in tie-line power and frequency. Under these circumstances, a load frequency control (LFC) loop gives constancy and security to interconnected power systems (IPSs) by supplying all consumers with high-quality power at a nominal frequency and tie-line power change. This article proposes employing a proportional–integral–derivative (PID) controller to effectively control the frequency in a one-area multi-source power network comprising thermal, solar, wind, and fuel cells and in a thermal two-area tie-line IPS. The particle swarm optimization (PSO) technique was utilized to tune the PID controller parameters, with the integral time absolute error being utilized as an objective function. The efficacy and stability of the PSO-PID controller methodology were further tested in various scenarios for proposed networks. The frequency fluctuations associated with the one-area multi-source power source and with the two-area tie-line IPS’s area 1 and area 2 frequency variations were 59.98 Hz, 59.81 Hz, and 60 Hz, respectively, and, in all other investigated scenarios, they were less than that of the traditional PID controller. The results clearly show that, in terms of frequency responses, the PSO-PID controller performs better than the conventional PID controller.

RED FOX-BASED FRACTIONAL ORDER FUZZY PID CONTROLLER FOR SMART LED DRIVER CIRCUIT

Recently, traditional lighting has been replaced by high-power LED (HPLED) due to its significant developments, prolonged lifetime, high brightness, high reliability, and high energy efficiency. Even though conventional converters can precisely match each LED's current, they have some limitations when driving a long string of LEDs. To solve this issue, this paper presents a novel red fox optimized fractional order fuzzy PID Controller (RF-FOFPID) based high gain improved transformerless dc-dc (ITDC) boost converter for a smart LED driver circuit with optimal voltage regulation capability. The error bias is increased to zero by applying FOPID to the fuzzy logic-based compensation steps. The Red Fox optimization algorithm is used to adjust the control parameters of the fractional-level fuzzy PID controller with higher precision to solve limited problems with diverse search spaces. The Simulink model of the proposed converter was created using the MATLAB software tool Simulink and compared with controllers using fractional order PID, fuzzy PID, and conventional proportional integral derivative (PID). The results demonstrated that in terms of minimum overshoot, settling time, rise time, and steady-state error, the proposed converter based on RF-FOFPID outperforms current converters in voltage regulation.

Performance analysis of voltage profile improvement in AVR system using zebra optimization algorithms based on PID controller

Many problems are associated with dynamic changes in the power system, mainly because the number of switching loads has increased. These loads induce ineffective system behaviour, reduce the system voltage profile, and lead to improper voltage regulation. The proposed Automatic Voltage Regulator (AVR) system is intended to eliminate the above problem and preserve the system voltage at the generator terminal within the allowable range. In this work, Proportional-Integral-Derivative (PID) controllers are applied to enhance the dynamic behaviour of the AVR system. The Zebra Optimization Algorithm (ZOA) and Osprey Optimization Algorithm (OOA) algorithms are implemented to optimize the system's transient response by modifying the gain of PID controller parameters, which include proportional, integral, and derivative parameters, to preserve a stable voltage profile. To achieve an optimal solution, the mathematical model of the proposed system is realized in the Internet of Things (IoT). The connected loads in the system can operate at the nominal voltage level through PID controller tuning using ZOA and OOA. The proposed system's performance is evaluated and verified with MATLAB R2023a. ZOA showed an improvement of 18.5 % with a peak overshoot of 0.01 % during the system-transient condition. Compared to conventional optimization algorithms implemented in AVR systems, ZOA provides faster responsiveness and enhanced system stability.

Optimal control of DC motor using leader-based Harris Hawks optimization algorithm

Direct Current (DC) motor is considered a very critical component of various industrial drive equipment. This is due to their unique advantages including reasonable cost, speed-torque characteristics, ease of control, etc. While most DC motor drive applications often employ PID controllers to regulate the speed of the machine, selecting the optimal design parameters for the controllers used in these applications often posed a serious challenge. Moreover, as the complexity of the industrial process increases, the need for precise speed and position tracking becomes necessary. This current study proposes a novel Leader-based Harris Hawks Optimization (LHHO) algorithm for the design of Proportional-Integral-Derivative (PID) and Fractional Order Proportional-Integral-Derivative (FOPID) controllers to achieve optimal speed regulation of DC motors. The LHHO algorithm is an innovative meta-heuristic algorithm that draw inspiration from the cooperative hunting behavior and leadership prowess of the Harris Hawks called the “seven pounds”. While several error functions were tested in this study, the integral of time multiplied absolute error (ITAE) has been adopted as the error function for obtaining the parameters of PID and FOPID controllers using the LHHO algorithm. Through quantitative evaluations and comparisons with existing techniques, the proposed controllers (LHHO-PID and LHHO-FOPID) have revealed significant improvements in key performance metrics, including rise time, settling time, and maximum overshoot during transient periods when ITAE is used as the error function. Furthermore, the stability response and robustness analyses were carried out by varying the parameters of the DC motor under eight scenarios, which confirmed competitive performance in system response and transient behavior.

Ant colony optimization-based adjusted PID parameters: a proposed method

The ant colony algorithm (ACA) is a heuristic algorithm that resolves the optimality problem by simulating an ant’s foraging process, which finds the shortest path. The connotation of the ACA is to find the optimal solution. The Proportional Integral Derivative (PID) parameter tuning is an essential tool in the control field and includes three parameters, Kp, Ki, and Kd, to achieve the best control effect. Besides, tuning the PID parameters is closely related to finding the “optimal” solution that can be attained based on the feasible combination of the two. This article transforms the PID parameter tuning problem into an ACA that finds the optimal solution called ACA-based PID parameters tuning. Furthermore, PID control is simulated by setting the parameters of ACA, such as ant colony size, iteration times, nodes, paths, path evaluation criteria, pheromone concentration, heuristic function, weight factor, and decision function. Eventually, the two PID controller parameter tuning strategies are compared and analyzed, and the advantages and disadvantages of each are obtained. Compared with the 4:1 attenuation curve method, the proposed method can significantly reduce the MP score of the overshoot of the system, increase the time, and improve the dynamic and steady-state performance of the system, but reduce the steady-state error of the system. Therefore, the feasibility and effectiveness of the proposed method is verified.

PID-Based control of sepic power stage with QS bacterial-based search algorithm for renewable energy applications

In response to the growing need for efficient power conversion in renewable energy applications, this study addresses the issue of harmonic distortion and low Power Factor (PF) values caused by non-linear loads, such as electronic devices. Low PF results in reduced efficiency, increased energy consumption, and higher costs for both consumers and utilities. Active correction, typically involving an active circuit implemented using a power converter, provides an optimal solution by offering high performance, precise regulation, and low harmonic distortion. This research proposes a Power Factor Correction (PFC) solution utilizing an Interleaved DC-DC Single Ended Primary Inductance Converter (SEPIC) for the power stage and a Proportional Integral Derivative (PID) controller for the control block, tuned by a Quorum Sensing (QS) bacterial-based search algorithm. The PID controller parameters are derived using a frequency-domain approach, which presents advantages such as reduced design complexity, improved stability margins, and enhanced SEPIC performance. Simulation results indicate reduced overshoot, settling time, rise time, and Total Harmonic Distortion (THD) percentage, as well as increased efficiency and power factor nearing unity. The proposed scheme also demonstrates improved dynamic performance for variations in load, line, and reference voltage. Owing to its high efficiency, precise voltage regulation, and minimized impact on the electrical grid, this solution is well-suited for renewable energy applications, where optimizing energy conversion and ensuring stable operation are crucial for the overall performance of renewable energy systems.

Performance analysis of PID controller-based metaheuristic optimisation algorithms for BLDC motor

ABSTRACT Today, the use of permanent magnet brushless DC (PMBLDC) motors in vehicles is increasing due to the characteristics of sensorless operation. PMBLDC motor controllers can control the speed and position in the closed-loop feedback system without the need for a position sensor mounted on the shaft. Proportional – Integral – Derivative (PID) controller is one of the most common feedback control algorithms used in PMBLDC motor controllers. Optimizing problems using deterministic methods such as Lagrange and simplex methods requires basic information and complex calculations. Meta-heuristic algorithms are a type of stochastic algorithm that is used to find the optimal solution. Meta-heuristic algorithms are divided into three general categories: evolutionary algorithms, swarm intelligence algorithms, and stochastic algorithms. In this paper, using 14 metaheuristic optimisation algorithms, PID control parameters including settling time, time rise, overshoot, and stability of step response of the mentioned system are optimised. In this paper, 14 meta-heuristic algorithms are simulated and evaluated to optimise the PID coefficients of the controller, including settling time, rising time, excessive increase, and step response stability. The simulation result shows that the genetic algorithm (GA) has the best performance in terms of cost function and biogeography-based optimisation (BBO) in terms of settling time and rising time parameters. Finally, the simulation results are confirmed using experimental results.

Numerical analysis of the performance of a PID-controlled air curtain for fire-induced smoke confinement in a tunnel configuration

In order to effectively meet the requirements of smoke confinement in different fire scenarios, this paper proposes an adaptive intelligent smoke control method combining PID (Proportional Integral Derivative) system with a traditional air curtain. By taking a tunnel fire configuration as an example, the feasibility of PID-controlled air curtains is explored and the calculation method of PID control parameters is clarified. The stability and reliability of the PID-controlled air curtain in realizing the smoke control effect in different complex fire scenarios are studied. The method of determining optimal PID coefficients is first proposed. Then, the robustness of the PID-controlled air curtain has been assessed by discussing the sensitivity of these parameters with different fire scenarios. The results show that the PID-controlled air curtains can effectively meet the requirements of smoke control in different complex fire scenarios. The impact of different fire scenarios on the PID-controlled air curtain system lies in the pre-control period, such as the system features of delay time, response time, overshoot, etc.

Real time adaptive PID controller based on quantum neural network for nonlinear systems

In this paper, an adaptive proportional-integral-derivative (PID) controller based on a quantum neural network (APIDC-QNN) is introduced. In the proposed neural network structure, three neural layers are used namely: input layer, hidden layer, and output layer. The input and hidden layers use three and six quantum-processing neurons respectively. In contrast, in the output layer, the multiplication processing is used as an activation function free of the output weights which aims to reduce the number of tunable parameters. The output layer with three neurons represents the adaptive PID controller parameters. In this work, the tunable parameters are updated using the Lyapunov stability criterion to guarantee optimization and learning stability. In addition, the plant identification based on the Wiener-type model and quantum neural network is introduced to estimate the sensitivity of the plant output to the control signal. The proposed neural network structure yields only 30 tunable parameters for the proposed controller and 13 tunable parameters for the proposed identifier while the conventional neural network structure in other works requires 90 tunable parameters just for the controller. To evaluate the performance of the proposed APIDC-QNN, it is practically operated on a non-holonomic two-wheel mobile robot (NHWMR). Moreover, to investigate the robustness, superiority, and convergence speed of the proposed APIDC-QNN, some experimental tasks are discussed. The simulation and practical results show the powerful processing and the superiority of the proposed APIDC-QNN over that designed based on conventional neural networks by recording the minimum values of the performance indices.

Pulse charging based intelligent battery management system for electric vehicle

Electric vehicles (EVs) are now an important part of the automotive industry for two main reasons: decreased reliance on oil and reduced air pollution, which helps us contribute to the development of an environmentally friendly environment. EV buyers examine overall vehicle mileage, recharge time, vehicle mileage after every charge, batteries charging/discharging security, lifespan, charged rate, capability, and temperature increase. A new improved pulse charging technique is proposed, in which the battery is charged using proportional integral derivative (PID) control action and a neural network. A PID controller is used to develop the charging unit in this design. The feed forward neural network was used to determine the values of the PID control parameters. The battery management system (BMS) ensures that this designed battery charging system takes less time to charge the battery efficiently. The system is built with MATLAB/Simulink.

Pulse charging based intelligent battery management system for electric vehicle

Electric vehicles (EVs) are now an important part of the automotive industry for two main reasons: decreased reliance on oil and reduced air pollution, which helps us contribute to the development of an environmentally friendly environment. EV buyers examine overall vehicle mileage, recharge time, vehicle mileage after every charge, batteries charging/discharging security, lifespan, charged rate, capability, and temperature increase. A new improved pulse charging technique is proposed, in which the battery is charged using proportional integral derivative (PID) control action and a neural network. A PID controller is used to develop the charging unit in this design. The feed forward neural network was used to determine the values of the PID control parameters. The battery management system (BMS) ensures that this designed battery charging system takes less time to charge the battery efficiently. The system is built with MATLAB/Simulink.

Attitude Control and Parameter Optimization: A Study on Hubble Space Telescope

Abstract In this work, we build a satellite attitude Proportional-Integral-Derivative (PID) controlled system by using the Hubble Space Telescope (HST) parameters as a reference and tune its controller parameters using various tuning methods. First, we give the equations for the motion of a satellite. We elaborate the control structure as controller, actuator, dynamics, and kinematics subsystems and construct an external disturbance model. We use a reaction wheel assembly used in the HST with the same configuration as the actuator. We evaluate the performance of the linearization by comparing it with the nonlinear model output. By working on the linearized model, we tune the PID controller parameters using two different methods: “Model-Based Root Locus Tuning” and “Genetic Algorithm Based Tuning”. First, we obtain the controller parameters by manipulating the poles on the root locus plot of the linearized system. In addition, we use genetic algorithms to find the optimized controller values of the system. Finally, we compare the performances of the two methods based on their cost function values and find that the Genetic Algorithm-based tuned parameters are more fruitful in terms of the cost function value than the parameters obtained by the Root Locus-based tuning. However, it is found that the Root Locus-based tuning performs better in disturbance rejection.

Improved Nelder-Mead algorithm for output voltage control of a synchronous generator

It is known that the standard Nelder-Mead algorithm (NMA) is subject to the problem of local minimum. This paper proposes an improved Nelder-Mead algorithm (INMA) based on dynamically varying the reflection, expansion, contraction and the shrinkage coefficients, following a cosine function. Dynamical coefficients intensify the search space exploration and also increase the ability of quickly finding the optimal solution. Firstly, the effectiveness and validity of this approach are confirmed by using some benchmark functions. Secondly, the approach is also verified by applying it for tuning a Proportional–Integral–Derivative (PID) controller used to regulate the voltage of a synchronous generator (SG) under different loading conditions. The PID controller parameters are obtained by a minimisation of the quadratic output error between the reference and the SG output voltage calculated from the adopted model. A comparison is established between INMA, standard NMA, genetic algorithm (GA) and particle swarm optimisation (PSO). The Obtained results show that the INMA has a better performance compared to other algorithms.

Load frequency control in three-area single unit power system considering non-linearities effect

Load frequency control is always a crucial aspect in delivering quality power in integrated power system. This paper proposes an optimally tuned Proportional-Integral-Derivative (PID) controller to remove frequency errors caused by unexpected load fluctuations while ensuring tie-line power exchange. Moreover, the threearea power system with non-linearities such as Generation Rate Constraint and Governor Dead Band has been investigated. For tuning the parameters of PID controller Improved Grey Wolf Optimization (IGWO) has been used. The dynamic response of optimized PID controller have been compared with the result obtained from Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Sine Cosine Algorithm (SCA).