Proportional Integral Derivative Controller Parameters Research Articles

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).

A novel chaotic chimp sine cosine algorithm part-II: Automatic generation control of complex power system

This is part II of two-paper set deals with the application of the designed algorithm in automatic generation control (AGC) problem of different structures, such as conventional and isolated. The companion paper (i.e., Part I) describes the designed algorithm in detail and shows the validation of the results in support of the proposed algorithm in a systematic manner. The main focus of this study is to apply and validate the algorithm for automatic generation control (AGC) in power systems. Specifically, the algorithm is tested on various AGC models, including a conventional four-area interconnected power system, deregulated power system and an isolated power system model withchange in operating conditions. The employed controller is a proportional-integral-derivative (PID) controller, and its parameters are tuned using the chimp optimization algorithm (CHOA), chaotic chimp optimization algorithm (C-CHOA), and chaotic chimp sine cosine optimization algorithm (C-CHOA-SC).The objective of this study is to assess the effectiveness of the proposed C-CHOA-SC optimization method in tuning the PID controller parameters for AGC in the considered power system models. Through simulations, the study evaluates the performance of the algorithm in terms of AGC efficiency and the ability to maintain dynamic responses within acceptable limits.The simulation results demonstrate that the proposed algorithm outperforms alternative methods in all the tested AGC models. It showcases superior performance while ensuring the desired dynamic response characteristics are maintained.

Optimum control of under‐grate pressure of clinker cooler by optimizing the proportional integral derivative controller parameters using honey badger algorithm technique

AbstractThe minimum air pressure necessary at the grate to penetrate a bed of heated clinker of a certain thickness and permeability is known as under‐grate pressure. It is vital to keep the under‐grate pressure consistent because excessive pressure causes the clinker to form an unstable suspension and minimum pressure allows heat from the clinker to harm the cooler grates. So it is necessary to obtain excellent performance through reasonable control methods. For this purpose, this article proposes an optimization approach for a proportional integral derivative (PID) controller to control the pressure below the grate of the clinker cooler. The proposed optimization approach is the honey badger algorithm (HBA), which mimics the feeding habits of honey badgers. The main aim of proposed approach is to maintain and regulate under‐grate pressure of clinker cooler using HBA and provide optimum cooling rate to clinker. The proposed approach based grate cooler also provide the less electricity consumption and stable temperature. The proposed technique is implemented in MATLAB and its efficiency is compared to various existing techniques. From the simulation, the electricity consumption of proposed approach RMSE is 158 and mean accuracy is 98.65. Under‐grate pressure based RMSE is 5.01 and the mean accuracy become 99.55. The outlet temperature based RMSE of proposed approach is 4.32 and mean accuracy is 81.21. These values are lower than existing approaches, it reveals the proposed approach is better than the existing one.

Approach in development and implementation of automatic control system in a smart building

The research shows an approach to develop and implement a control system in an observed building. For this purpose, two approaches with simulation results are presented. The first approach is theoretical in nature, with derived equations of a mathematical model. These models are presented in the program ©MATLAB Simulink with the results of temperature change simulations. The second experimental approach is the implementation of a real remote-control system developed with the ©Siemens TIA Portal software tool. It is based on derived system components from the mathematical model. The user interfaces of the system were created using the ©WinCC program. All this was done using the example of a family house with three residential units. The system was developed with the purpose of reducing the needs of thermal energy compared to the existing system. By measuring the heat energy needs, it was found that the new remote-control system reduces the heat energy needs by 12.4 % within one calendar year compared to the existing control system. The reason for this is the implementation of several operating modes in the system: night operation mode, working operation mode and rest operation mode. The system is implemented in such a way that the user can monitor and control the air temperature in the observed room from anywhere and at any time by using a cell phone, computer and Internet connection. It is possible that all housing units in the observed facility are controlled separately. The electric valves of the radiators are controlled by proportional–integral–derivative (PID) controllers. During the automatic adjustment of the PID controller parameters, the values were calculated: proportionality gain constant K = 1.26214 and gain constant of the integration part KI = 7.01238.

Desain Kendali Pembangkit Listrik Tenaga Pikohidro Berbasis Ant Colony Optimization (ACO)

Energy sources that are often used to generate electricity are non-renewable energy sources which, if used continuously, will run out, such as petroleum, natural gas and coal. So, renewable energy sources are needed which are in abundant supply and do not run out quickly, one of which is water energy. Picohydro power plants (PLTPH) are an alternative small-scale power plant that can be applied in rural areas where there is a river flow that has a continuous water discharge and a relatively low water fall to drive turbines that can produce electrical power. To be able to produce electrical power with such potential, a pico-hydro power plant is needed. To optimize the performance of a picohydro power plant, a controller called PID (Proportional Integral Derivative) is needed. Then this PID is combined with the ACO (Ant Colony Optimization) method. ACO is a method for optimizing PID control parameters in a system adapted from the ability of an ant colony to find the shortest path to a food source from its nest

Speed Control of Wheeled Mobile Robot by Nature-Inspired Social Spider Algorithm-Based PID Controller

Mobile robot is an automatic vehicle with wheels that can be moved automatically from one place to another. A motor is built in its wheels for mobility purposes, which is controlled using a controller. DC motor speed is controlled by the proportional integral derivative (PID) controller. Kinematic modeling is used in our work to understand the mechanical behavior of robots for designing the appropriate mobile robots. Right and left wheel velocity and direction are calculated by using the kinematic modeling, and the kinematic modeling is given to the PID controller to gain the output. Motor speed is controlled by the PID low-level controller for the robot mobility; the speed controlling is done using the constant values Kd, Kp, and Ki which depend on the past, future, and present errors. For better control performance, the integral gain, differential gain, and proportional gain are adjusted by the PID controller. Robot speed may vary by changing the direction of the vehicle, so to avoid this the Social Spider Optimization (SSO) algorithm is used in PID controllers. PID controller parameter tuning is hard by using separate algorithms, so the parameters are tuned by the SSO algorithm which is a novel nature-inspired algorithm. The main goal of this paper is to demonstrate the effectiveness of the proposed approach in achieving precise speed control of the robot, particularly in the presence of disturbances and uncertainties.

Optimal tuning of PID controller for speed control of DC motor using equilibrium optimizer

The efficient and smooth operation of direct current (DC) motors is of high significance in various industrial applications. This study proposed a recent optimization technique, named equilibrium optimizer (EO) for optimal tuning of the parameters of proportional integral derivative (PID) controller to provide accurate and robust control in DC motors. Mathematical modelling of the motor was carried out in Simulink environment using basic electric machine theory. Conventional Ziegler-Nichols (ZN) technique is initially designed to tune the PID controller parameters, an optimization model based on the minimization of integrated absolute error and named EO-PID is formulated and subsequently implemented to optimize the tuned parameters obtained using the ZN-PID controller. Simulation results showed that there is a significant reduction in terms of rise time, settling time, overshoot and mean square error using the proposed EO-PID tuning model as compared to the ZN-PID technique. The result also showed that the proposed EO-PID model gives a better dynamic performance as it eliminates the oscillatory response common with most conventional control techniques. The results of this study have established EO as an effective optimization technique for optimal tuning of PID controller parameters, and can therefore be used by machine operators for various industrial applications.

An Improved Topology of Isolated Bidirectional Resonant DC-DC Converter Based on Wide Bandgap Transistors for Electric Vehicle Onboard Chargers

This article proposes an improved topology for an isolated bidirectional resonant DC-DC converter for electric vehicle (EV) onboard chargers. As opposed to the conventional capacitor-inductor-inductor-inductor-capacitor (CLLLC) resonant converter, the proposed converter’s resonant circuit is composed of a capacitor-inductor-inductor-inductor (CLLL) structure, whose inductances, except the capacitor, can be fully integrated with the leakage and mutual inductances of the high-frequency transformer (HF). Therefore, this offers a smaller size, lower costs, minimal power loss, and eventually higher efficiency. Again, the proposed converter design is based on wide bandgap (WBG) transistor switches that operate at MHz-level switching frequency to achieve high power density, high efficiency, and high compactness. A discrete-time proportional integral derivative (PID) controller has been designed using the phase-shifted pulse width modulation (PSPWM) technique to assure closed-loop control of the proposed CLLL converter. The PID controller parameters have been optimized using both the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm and a comparative analysis has been presented between the two algorithms. To achieve fast switching with very little switching loss, the converter is simulated with several wide bandgap (WBG) switching devices. A performance comparison with conventional Si-based switching devices is also provided. A precise power loss model of the semiconductor switches has been devised from the manufacturer’s datasheet to achieve a perfect thermal design for the converter. A 5 kW CLLL converter with an input range of 400–460 V direct current (DC) and an output range of 530–610 V DC, and a switching frequency of 1 MHz has been designed and investigated under various loading scenarios. Gallium nitride (GaN) switching device-based designs achieved the highest levels of efficiency among the switching devices. The efficiency of this device is 97.40 percent in charging mode and 96.67 percent in discharging mode.

Comparison of Machine Learning Algorithms for DC Motor PID Control with Genetic Algorithm

Proportional-Integral-Derivative (PID) is a closed loop control system based on dynamic system feedback commonly employed in industrial control systems that require continuous modulated control. The purpose of this project was to improve the efficacy of PID control, a branch of classical control theory, to allow for the application of PID controllers in more complex and non-linear environments. The research compares the results of different machine learning algorithms with genetic algorithms to optimize PID control parameters to achieve more precise control of 12V DC motors. Genetic Algorithm (GA) is a metaheuristic algorithm used to solve search and optimization problems in machine learning. GAs are developed based on the process of natural selection and genetics, relying on biologically inspired operators. After a series of selections and crossovers, the genetic algorithm selected the fittest generation of PID constants that would result in the optimal PID controller which minimizes system error value and linearizes the system behavior. Optimizing PID parameters to enhance the efficacy of classical control theory could increase machining precision in industrial production and improve the robustness of integrated PID controllers in complex environments such as the human body, enabling PID controllers to be an adaptive, simple, and viable option to be implemented in implantable artificial organs and motorized prosthetics. The results of the study suggest that GA optimized PID parameters are effective in regulating the system behavior of DC motors and the enhanced PID control system could be implemented in a wide range of rapidly changing and non-linear applications.

Exponential synchronization analysis for complex dynamical networks with hybrid delays and uncertainties under given control parameters

<abstract><p>This paper addresses the problem of exponential synchronization in continuous-time complex dynamical networks with both time-delayed and non-delayed interactions. We employ a proportional integral derivative (PID) control strategy and a dynamic event-triggered approach to investigate this synchronization problem. Our approach begins with constructing a general model for complex dynamical networks that incorporate delays. We then derive synchronization criteria based on the PID control parameters, utilizing linear matrix inequality techniques in conjunction with a dynamic event-trigger mechanism. The application of Lyapunov stability theory and inequality techniques allows us to establish these criteria, considering the presence of hybrid delays. To illustrate the effectiveness of our proposed model, we provide two numerical examples showcasing synchronization dynamics. These examples demonstrate the successful theoretical results of a novel PID controller and dynamic event-trigger mechanism.</p></abstract>

A Microcontroller Based Proportional Integral Derivative Controller for Yam Tuber Storage Chamber Temperature and Humidity Control

Adequate and suitable controlled atmosphere plays an important role in post-harvest yam tuber storage environment for sustaining the quality and quantity of yam tuber especially in a reputable country that produces largest quantities of yam in the world. The effect of post-harvest loss is caused due to lack of appropriate storage systems and these had been the most common problem faced by small-holder yam farmers. In order to avoid post-harvest losses and food wastages due to poor atmospheric condition in West Africa, this paper proposed a microcontroller based Proportional Integral Derivative (PID) controller for controlling the temperature and humidity of yam tuber post-harvest storage. The due technique would maintain a relatively constant temperature and humidity in a storage chamber so as to minimized high level of yam postharvest losses that has become a serious economic and food security threat in West Africa. Some mathematical equations were formulated based on temperature and humidity storage chamber models using heat and energy balance principles. The models were designed using Proteus tools and simulated in Simulink form. The parameters of PID controller were tuned with Ziegler Nichols to achieve a desirable steady state response. The system performances were evaluated in settling time, temperature and humidity set point tracking. The performance metrics were compared with existing technique of Fuzzy Logic Controller (FLC). The sensitivity of the results justified that proposed technique gave a better performance in terms of decreased in settling time, temperature and humidity.

Research on robotic end positioning based on fuzzy PID control

The existing traditional proportional-integral-derivative (PID) control robot has low-end positioning accuracy, which cannot meet the needs of accurately placing the molding die in the current automatic coal pen production process. To address this problem, this paper proposes a fuzzy strategy PID control method based on fuzzy strategy, aiming to improve the accuracy and robustness of the system when the manipulator grasps the mounding mold. Firstly, a manipulator motor control model is established to derive the parameters of the manipulator control system; secondly, the fuzzy control strategy is applied to adjust the PID control parameters in real time to improve the dynamic stability of the system. Finally, the control system is simulated, and field experiments are conducted to verify the accuracy of robotic end positioning. The experimental results show that the fuzzy PID control system has a shorter response time, better anti-interference ability, and better stability than the traditional PID control and achieves the accuracy requirements of coal pen automation production.

A Novel Jaya Based Automatic Generation Control of Two Area Interconnected Power System with nonlinearity

Background: This paper presents a novel approach to design and optimize Proportional-Integral-Derivative (PID) controller for Automatic Generation Control (AGC) of two-area interconnected power system with different energy sources. Methods: The two area interconnected power system consists of thermal, hydro and wind energy sources in the area-1 and area-2 comprises thermal, hydro and diesel energy sources. The recently developed Jaya Optimization technique is explored to optimize PID controller parameters for the interconnected power system by Matlab. Findings: The dynamic responses of power system is studied including the non-linearities like Governor Dead band (GDB), Generation Rate Constraint (GRC) and boiler dynamics under 1% step load perturbation. Novelty and applications The beauty of Jaya algorithm is that it works with only few control parameters as compared with other algorithms which help in improving dynamic responses and performance values of power system with non-linearities. Hence, the frequency stability is achieved at faster convergence at loads. The comparative analysis of proposed technique with other techniques like Differential Evolution (DE) algorithm and Teaching Learning Based Optimization (TLBO) algorithm for the same power system justify the effectiveness of proposed approach. Keywords: Automatic Generation Control; PID Controller; Nonlinearities; Renewable Sources; Jaya Algorithm