Tuning Of PID Controller Parameters Research Articles

Artificial rabbits optimization algorithm based tuning of PID controller parameters for improving voltage profile in AVR system using IoT

The power system is mainly affected by transient situations caused by switching heavy loads. The system may become unstable when transient situations occur. The power system should be able to perform continuous operation to maintain its voltage within acceptable limits. To achieve more stability and increase its speed of response, an Automatic Voltage Regulator (AVR) system requires the inclusion of a controller. The AVR system in the generating station uses the PID controller to adjust the abnormal voltage caused by transient conditions. To maintain the nominal voltage level under all the load conditions in the system, a bio-inspired meta-heuristic algorithm called Artificial Rabbit Optimization (ARO) algorithm is proposed to tune the PID controller gain parameters and obtain the optimal gain, thereby the AVR system adjusts the generator terminal voltage to nominal levels. The ARO algorithm inspires natural survival techniques to improve the AVR performance by reducing errors. To maintain a stable voltage profile in a power system, this research mathematically models survival techniques using the Internet of Things (IoT) to obtain an optimal solution. As a result, all devices connected to the power network receive a stable voltage that ensures their voltage reliability. The effectiveness of the proposed algorithm for the AVR system is verified with the MATLAB R2022a model, and the statistics functions are implemented in the module of Pandas, Scipy and mathematical investigations done in Numpy. The proposed ARO algorithm achieves a better voltage profile with less than 12.63% maximum peak overshoot during the system's transient response. The proposed algorithm provides the fastest response and highest stability comparable to other optimisation algorithms.

A Parameter Tuning Method of Adaptive Dynamic PID Controller for Time Delay Integral System

PID controllers are widely used in the control of integral systems. In the practical application of integral systems, the control effect is usually affected by the delay time, and at the same time, the large variation of the controlled integral parameters will also affect the control effect of the controller. This paper proposes an adaptive dynamic PID controller parameter tuning method suitable for time delay integral systems, considering the influence of parameters such as the voltage of the integral system, the variation range of the controlled object, and delay time, making the relationship between the parameters of the PID controller and the control object of the integral system more explicit. At the same time, this paper considers the tuning of the differential filter parameters in practical applications. A non-linear system with a 10-fold variation of the controlled integral parameters is simulated in Simulink, and the dynamic control of the non-linear system is realized using the method proposed in this paper, achieving a better control effect compared to the traditional control with constant PID parameters.

Design of Fitness Function for Intelligent Parameter Tuning of PID Controller on Belt Conveyor with PSO

Design of Fitness Function for Intelligent Parameter Tuning of PID Controller on Belt Conveyor with PSO

Robust PID optimal tuning of a Delta parallel robot based on a hybrid optimization algorithm of particle swarm optimization and differential evolution

AbstractIn this paper, we propose an approach to tune optimal parameters of a robust PID controller by means of computed torque control (CTC) strategy for trajectory tracking of a Delta parallel robot, using a hybrid optimization algorithm of Particle Swarm Optimization (PSO) and differential evolution (DE). It differs from previous works that they propose robust PID controller parameters tuning based on conventional gradient-based optimization algorithms and apply them to process control. First, we reduce the tuning problem of a robust PID controller with CTC strategy satisfying requirements including robustness and disturbance attenuation to an optimization problem with nonlinear constraints by considering the nonlinear dynamic model of a Delta parallel robot. Second, we set up the design characteristics for the trajectory tracking of a Delta parallel robot. Then, we propose a robust PID controller in a way of obtaining the global optimization solution of the nonlinear optimization problem by running a PSO-DE hybrid optimization algorithm of finding the global optimal solution by maintaining the diversity of swarm during evolution based on the evolution of cognitive experience. Simulation and experimental results demonstrate that the proposed controller outperforms previous works with respect to robust performance and active disturbance attenuation when it is applied to tracking control of a Delta parallel robot.

Single loop PID controller design based on optimization algorithms for parallelly connected dc-dc converters

This paper addresses a viable single loop PID controller on the bases of optimization algorithms for parallelly connected DC-DC converters to improve current sharing, improve the systems dynamics and guarantee good steady-state performance simultaneously. Because of inconvenience and lack of accuracy of Ziegler-Nichols rule in tuning PID controller parameters, an optimized controller design strategy with the purpose of enhancing the system performance is introduced in this paper. The PID is tuned by the traditional Ziegler -Nichols technique along with three other different algorithms: Genetic algorithm, whale algorithm and grey wolf algorithm. A comparison has been established between these algorithms based on the objective function value, execution time, overshoot, settling time and current sharing. The simulation results were collected to authenticate effectiveness of the proposed techniques and to evaluate the advantages of these optimization algorithms over the traditional tuning method.

NOVEL APPROACH BASED ON ARTIFICIAL NEURAL NETWORKS (ANN) FOR CONTROLLING THE THREE-PHASE INVERTER OF A PHOTOVOLTAIC SYSTEM

This paper is devoted to the study of a photovoltaic system connected to the grid. The objective is to provide a novel approach based on artificial neural networks for Controlling the three-phase invelter in order to ensure a flexible injection to unity power factor and low total harmonic dist01tion. A modeling system established mathematical models of the invener and the DC link. A control in the synchronous reference direct and quadratic frame by a neural proportional integral derivative based on back propagation of gradient descent is offered to regulate respectively the dc link voltage and the injected cunents in order to overcome the limitations ofthe experimental tuning (Ziegler-Nichols) of the classical PID controller parameters. Simulation results from the system under the Matlab / Simulink environment prove the efficiency of the proposed method and show that the currents injected have the best sinusoidal fonn with a cunent Total Harmonic Distortion of 0.96 % and a net followed of the dc link voltage.

Optimization of PID parameter tuning for gravity stabilized platform based on improved differential evolutionary algorithm

To address the difficulty of the PID controller parameters tuning of gravity stabilized platform, an improved differential evolutionary (IDE) algorithm containing two currently better variational strategies is proposed. In the process of algorithm iteration, the mutation factor and crossover probability factor of the algorithm are gradually changed to improve the optimization performance of the algorithm. On this basis, the improved differential evolution algorithm is applied to the PID controller parameter tuning problem of gravity stabilized platform. Simulation results show that the algorithm is effective and can be used to optimize the parameters of PID controller of gravity stabilization platform.

Parameter Tuning of PID Controller for Beer Filling Machine Liquid Level Control Based on Improved Genetic Algorithm.

Parameter tuning of PID controller for liquid level control of beer filling machine was studied in this paper, which can meet the demand of accurate controlling in beer production and improve the rapidity under the same conditions. Firstly, an improved genetic algorithm was proposed which has been verified by eight kinds of test functions. Simulation results revealed that, in comparation with other modified particle swarm optimization algorithm and modified genetic algorithm, the algorithm proposed in this work is not only capable to improve the convergence speed and precision under the same experimental conditions but also to improve the probability to converge to the optimal value. Finally, the proposed algorithm was applied to the parameter tuning of the PID controller of beer filling machine for liquid level control. Superior property had been obtained, which implied an effective improvement in the rapidity with the premise of steady-state error exclusion.

Oransal İntegral Türevsel Denetleyici Parametrelerinin Sezgisel Optimizasyon Yöntemleri ile Ayarlanması

In this article, modified radial movement optimization (MRMO) is proposed as a new variant of radial movement optimization (RMO). Also, for the three selected test systems, the gain factors of the PID controller were optimized by the proposed method (MRMO), particle swarm optimization (PSO), radial movement optimization (RMO), differential evolution (DE), and genetic algorithm (GA). With heuristic optimization methods, four different error area-based performance criteria were used in parameter tuning: Integral of square error, integral of absolute value of error, time-weighted integral of square error, and time-weighted integral of absolute value of error. First, the performance of the PID controller tuned by these five heuristic optimization methods (MRMO, PSO, RMO, DE, and GA) was compared according to the selected error area criteria, and then the performances of the optimization methods were compared with each other and the results obtained were analyzed. There are many studies in the literature regarding the tuning of PID controller parameters using PSO, DE, GA, and RMO. However, there is no study on the optimization of the gain constants of the PID controller with the proposed MRMO. This is the most important contribution of this study presented to the current literature.

6-DOF Modeling and 3D Trajectory Tracking Control of a Powered Parafoil System

The powered parafoil system is obtained by adding the propeller thrust to the unpowered parafoil system, and has coupling and nonlinear characteristics, which make its precise control more difficult than that of the unpowered parafoil system. To achieve the trajectory tracking control of the powered parafoil system in the field of precision airdrop, a mathematical model of the 6-DOF of the powered parafoil system is established first. Then, a new trajectory tracking strategy is proposed, which can overcome the limitations of the traditional guidance-based trajectory tracking strategy. We design lateral, longitudinal, and velocity controllers on the basis of the motion characteristics of the powered parafoil system. Then, we adopt the widely used PID control strategy in engineering. In response to the difficult of the PID controller parameter tuning of the powered parafoil system, we achieve the PID controller parameter tuning with the ecosystem particle swarm optimization (ESPSO) of the swarm intelligence optimization algorithm. The effectiveness of the algorithm is verified by simulation experiments. Results show that the proposed algorithm can obtain high trajectory tracking accuracy even when a deviation in the initial state and a random gust wind disturbance in the outside world occur regardless of the method of the multiphase homing or the optimal control homing adopted. The proposed trajectory tracking strategy also has strong robustness and adaptability.

An Adaptive Identification Method Based on the Modulating Functions Technique and Exact State Observers for Modeling and Simulation of a Nonlinear Miso Glass Melting Process

Abstract The paper presents new concepts of the identification method based on modulating functions and exact state observers with its application for identification of a real continuous-time industrial process. The method enables transformation of a system of differential equations into an algebraic one with the same parameters. Then, these parameters can be estimated using the least-squares approach. The main problem is the nonlinearity of the MISO process and its noticeable transport delays. It requires specific modifications to be introduced into the basic identification algorithm. The main goal of the method is to obtain on-line a temporary linear model of the process around the selected operating point, because fast methods for tuning PID controller parameters for such a model are well known. Hence, a special adaptive identification approach with a moving window is proposed, which involves using on-line registered input and output process data. An optimal identification method for a MISO model assuming decomposition to many inner SISO systems is presented. Additionally, a special version of the modulating functions method, in which both model parameters and unknown delays are identified, is tested on real data sets collected from a glass melting installation.

Implementation of Decentralized Control Scheme to Control a MIMO System using Response Optimization Toolbox in MATLAB

In this paper, Model based approach is implemented to control a heat exchanger prototype using optimization technique for parameter tuning of PID controller. Mathematical modelling of heat exchanger is done using mechanical specifications and differential equations of “Shell and Tube heat exchanger”. Optimally tuned PID controller is used to control the process model. Output response of optimal PID controlled model is compared with ordinary PID controlled model’s output response in results. Response optimization technique is implemented in this process to tune the parameters of the PID controller. It makes the response as a settled one on the desired value and also gives the stable response.

Tuning PID Controller Parameters to avoid the Effect of Integrator Wind-up by using The Experimental-based Heuristic Approach

Dc motor is widely used as actuator in industry and robotics. In this experiment a dc motor is used as an Omni wheel actuator on Omni-directional 3WD robot. In order for the movement of the robot to follow the specified vector quantity, then the speed of the wheels connected in each motor shaft must persist at each set of points that have been determined. For these needs then, every dc motor must be controlled the number of revolutions. One of the problems with implementing a PID controller is the possibility of a wind-up integrator effect that causes the system to be unable to follow the command behavior. The purpose of this study was to obtain PID controller parameters in such a way as to avoid the effects of wind-up integrators and achieve the best system response performance using an experimental based heuristic approach. The results of this study show that the wheel spin is able to follow various reference speeds with settling time and steady state error at about 1.1s and 0.648%

Tuning of PID parameters based on particle swarm optimization

PID controller has the advantages of simple algorithm, easy realization, easy adjustment and good robustness, and is widely used in various industries. Engineers have accumulated a wealth of experience in the long-term practice of PID controller parameter tuning, but the traditional experience-based PID parameter tuning method has been unable to meet the control needs of modern nonlinear complex objects. In this paper, the PID parameter tuning algorithm of basic particle swarm optimization (PSO) is designed. Establish the second-order system, select the experience trial and error method to tune the PID parameters, this method cannot quickly calculate the PID parameters, the staff need to rely on experience to constantly try and error, inefficient. However, the optimal solution of PID parameters can be obtained quickly by using PSO, and the control effect is good.

PID Controller Design for Controlling Integrating Processes with Dead Time using Generalized Stability Boundary Locus

This paper proposes a method so that all PID controller tuning parameters, which are satisfying stability of any integrating time delay processes, can be calculated by forming the stability boundary loci. Processes having a higher order transfer function must first be modeled by an integrating plus first order plus dead time (IFOPDT) transfer function in order to apply the method. Later, IFOPDT process transfer function and the controller transfer function are converted to normalized forms to obtain the stability boundary locus in (KKcT, KKc(T2 / Ti)), (KKcT, KKcTd) and (KKc(T2/Ti), KKcTd) planes for PID controller design. PID controller parameter values achieving stability of the control system can be determined by the obtained stability boundary loci. The advantage of the method given in this study compared with previous studies in this subject is to remove the need of re-plotting the stability boundary locus as the process transfer function changes. That is, the approach results in somehow generalized stability boundary loci for integrating plus time delay processes under a PID controller. Application of the method has been clarified with examples.

Tracking Control of a Continuous Stirred Tank Reactor Using Direct and Tuned Reinforcement Learning Based Controllers

AbstractThe need for linear model, of the nonlinear system, while tuning controllers limits the use of classic controllers. Also, the tuning procedure involves complex computations. This is further complicated when it is necessary to operate the nonlinear system under different operating constraints. Continues Stirred Tank Reactor (CSTR) is one of those non-linear systems which is studied extensively in control and chemical engineering due to its highly non-linear characteristics and its diverse operating range. This paper proposes two different control schemes based on reinforcement learning algorithm to achieve both servo as well as regulatory control. One approach is the direct application of Reinforcement Learning (RL) with ANN approximation and another is tuning of PID controller parameters using reinforcement learning. The main objective of this paper is to handle multiple set point control for the CSTR system using RL. The temperature of the CSTR system is controlled here for multiple setpoint changes. A comparative study is also done between the two proposed algorithm and from the test result, it is seen that direct RL approach with approximation performs better than tuning a PID using RL as oscillations and overshoot are less for direct RL approach. Also, the learning time for the direct RL based controller is lesser than the later.

PID Control Algorithm Based on Genetic Algorithm and its Application in Electric Cylinder Control

The traditional PID controller is simple in principle, easy to use, stable and reliable, and it is still widely used in the control field. However, for many nonlinear and lagging objects, the parameter tuning of PID controller is very important. Genetic algorithm provides a new way to optimize the parameters of PID. It uses simple coding techniques and propagation mechanisms to express complex phenomena, which is not restricted by the restriction of the search space. In this paper, the global optimization of genetic algorithm is used to optimize the parameters of PID, which can improve the performance and adaptive capability of PID controller. The mathematical model of the electric cylinder system is established, and the PID controller based on genetic algorithm is used to control the system. The simulation results verify the effectiveness of the proposed control algorithm.

Modelling and Simulation for Used Genetic Algorithm Method to Speed Control of Three-phase Induction Motor

Optimization techniques are very well-known to improve the performance of Three-phase induction motor (TIM). Inverter and SVPWM method can be used for setting voltage and frequency appropriating with loads requirements. This study deals with the tuning of PID controller parameters to be used TIM. Genetic algorithm is used to tune each parameters of PID speed controller to improve the speed response performance of the TIM. PID-GA aims to minimize the error signal speed of TIM, reduce overshoot, and tune parameters of PID controller. The error signal speed of TIM has objective function from integrated error signal (ISE). Parameters of PID controller from optimization are used to the model of the closed-loop speed control of three-phase induction motor using PID. PID-GA has the best speed response. On the model of the closed-loop speed control of three-phase induction motor using PID, various load torque can result in decreasing flux stator and rotor, increasing current stator and rotor, and decreasing speed of the induction motor.

Parameter tuning of PID controller with reactive nature-inspired algorithms

A PID controller is an electrical element for reducing the error value between a desired setpoint and an actual measured process variable. The PID controller operates according to its input parameters, which need to be set before its run. The optimal values of these parameters must be found during the so-called tuning process. Today, this process can be automatized using stochastic, nature-inspired, population-based algorithms, such as evolutionary and swarm intelligence-based algorithms. Unfortunately, these algorithms are too time consuming, and so the reactive, nature-inspired algorithms using a limited number of fitness function evaluations are proposed in this paper. Two reactive evolutionary algorithms (differential evolution and genetic algorithm), and four reactive, swarm intelligence-based algorithms (bat, hybrid bat, particle swarm optimization and cuckoo search), were used to tune the PID controller in our comparative study. Only ten individuals and ten iterations (generations) were used in order to select the most appropriate optimization algorithm for fast tuning of controller parameters. The results were compared using statistical analysis and showed that particle swarm optimization is the best option for such a task.

Adaptive Particle Swarm Algorithm for Parameters Tuning of Fractional Order PID Controller

In order to optimize the parameters of fractional order PID controller of complex system, an adaptive particle swarm optimization (PSO) method is proposed to realize the parameters adjustment. In this algorithm, the tuning particle population is divided into three subgroups firstly, and through introducing the swarm-aggregation degree factor and the evolution speed factor of particle, dynamically adjusting the inertia weight and size of subgroups respectively, setting to find optimal objective according to the time-domain performance index of the system, and then the controller parameter tuning is realized by iterative calculation. Finally, adaptive particle swarm optimization method of fractional order PID controller is applied to integer order and fractional order of the controlled system for performance simulation in time domain analysis. The experimental results show that the proposed method could improve the performance of the control system and has strong anti-interference ability.