Algorithm Based PID Controller Research Articles

A Study on Hybrid-GWO Algorithm-based PID Controller for Quadcopters

A Study on Hybrid-GWO Algorithm-based PID Controller for Quadcopters

Grey wolf optimization algorithm-based PID controller for frequency stabilization of interconnected power generating system

Grey wolf optimization algorithm-based PID controller for frequency stabilization of interconnected power generating system

Stabilization of wheeled mobile robot by social spider algorithm based PID controller

Stabilization of wheeled mobile robot by social spider algorithm based PID controller

Fractional order controller design via gazelle optimizer for efficient speed regulation of micromotors

In this paper, the design of an efficient fractional-order proportional-integral-derivative (FOPID) controller, tailored specifically for the regulation of micro direct current (DC) motors, is explored. A fresh approach is introduced using the gazelle optimization algorithm (GOA), a cutting-edge method set to manage the speed control of these small but vital motors. With the adoption of GOA, fine-tuning the parameters of the FOPID controller is aimed. This is achieved by employing a time-based performance metrics-based cost function as the guiding compass. The GOA is implemented to discover the optimal controller settings for the attainment of peak performance. Through thorough simulations and careful statistical analysis, the worth of GOA-based FOPID controller is demonstrated. Not only are top-notch cost function values achieved, but excellence across various time domain-based performance metrics is also excelled in. The results suggest that the GOA proves to be efficient in fine-tuning of FOPID controller parameters. A comprehensive analysis in the time domain further solidifies the superiority of GOA-based FOPID-controlled micro-DC motor system. Outperformance is observed when compared to alternative algorithms employing different controllers, such as an advanced hybrid stochastic fractal search-based FOPID controller, a grey wolf optimizer-based FOPID controller, a slime mold algorithm-based PID controller, an enhanced arithmetic Harris hawks optimization-based PID controller, and a hybrid atom search optimization and simulated annealing-based PID controller. In essence, the results highlight the potential of the GOA as a powerful tool poised to reshape the optimization of FOPID controllers for micro-DC motor speed regulation.

AGC of Multi Area Multi Source Restructured Electric Power System with Differential Evolution Algorithm based PID Controller

AGC of Multi Area Multi Source Restructured Electric Power System with Differential Evolution Algorithm based PID Controller

A novel optimization algorithm based PID controller design for real-time optimization of cutting depth and surface roughness in finish hard turning processes

This paper proposes a novel method to improve surface finish in turning processes by effectively controlling the cutting depth. A metaheuristic algorithm based PID control system, in combination with a piezoelectric vibration sensor for feedback, is introduced to regulate the position of the servo motor that controls the cutting tool. The PID controller is optimized using Q-learning based Sand Cat Optimization algorithm to achieve the best performance in terms of cutting depth accuracy and surface finish quality. The piezoelectric sensor provides real-time feedback information about the cutting process and allows for precise adjustments to the cutting depth. The results demonstrate the proposed system's ability to handle variations in cutting conditions and tool's wear and tear. Compared to highly optimized standard PID control, improved robustness and stability has been achieved in experimental results by proposed framework. Experimental results demonstrate improved robustness and stability compared to standard PID control. Several materials with high hardness of 20–65 HRC including Phenolic Bakelite, Copper, Thermoplastic, and Stainless Steel (AISI-420, AA6061-T6, and AISI-316) are tested. Minimum value of Vibration amplitude achieved is 0.598 μm for cutting depth of 0.25 mm. The sustained minimum amplitude of vibration and Ra value of the surface finish is found comparable to standard models with less than 10% error.

Adaptive backstepping controller design on Buck converter with a novel improved identification method

An adaptive backstepping method is presented by this paper for a DC–DC Buck converter utilising a strategy for system identification with pulse width modulation in the presence of parametric uncertainties, load variations, and high variance noises. In this control structure, the system is assumed as a black-box block that can decrease the computational burden providing faster dynamics. An adaptive mechanism is adopted for the BSM using the Lyapunov definition, providing robust dynamics for the controller against various disturbances. In addition, a novel improved exponential recursive least-squares identification algorithm is proposed, which shows higher robustness in parametric estimations and can decrease the negative impact of disrupting factors on the estimator. Moreover, a particle swarm optimisation algorithm-based PID controller is designed to be compared with the proposed controller. Finally, the merits of the presented controller are validated for various working conditions through simulations and experiments. It can be seen that the adaptive backstepping method with the improved identification technique provides much better results with faster dynamics.

Artificial bee colony algorithm based PID controller for steel stripe deviation control system.

To improve the performance of the PID controller for a steel strip deviation control system (SSDCS), an enhanced artificial bee colony algorithm (EABC) is proposed to optimize PID controller gains (EABC-PID). The proposed EABC changes the candidate solution equation to balance its explorative and exploitative capabilities. The experiment presents a detailed comparison of EABC-PID and four bio-inspired algorithms based PID controllers considering four types of objective functions. Simulation results show that EABC-PID proves to be superior for SSDCS compared to four bio-inspired algorithms based PID controller in terms of convergence, dynamic adjustment, and robustness.

Advanced genetic algorithm based PID controller for air levitation system

Advanced genetic algorithm based PID controller for air levitation system

Reformative artificial bee colony algorithm based PID controller for radar servo system

<abstract> <p>This paper proposes a PID controller optimized by a reformative artificial bee colony algorithm (RABC-PID) for the radar servo system (RSS). The RABC algorithm is an enhancement of the artificial bee colony (ABC) algorithm by introducing the best-positioned food source and modifying the food source probability. The RABC algorithm is validated by simulation with six benchmark functions, and the results show that the RABC algorithm is superior to the other variants of the ABC algorithm in terms of convergence speed and accuracy. The RABC-PID controller is then used for the RSS. The RSS is presented to illustrate the application of the RABC-PID controller. The simulation results, which are also compared to PID optimized by particle swarm optimization, differential evolution, and genetic algorithm (PSO-PID, DE-PID, and GA-PID) respectively, are shown to illustrate the effectiveness and robustness of the RABC-PID controller.</p> </abstract>

Advance genetic algorithm-based PID controller for air levitation system

In industrial control systems, PID controllers are being widely used owing to their simple working principles. Many control and instruments engineers and operators use PID controllers in daily life. PID controllers allows for many variations which can cope with a wide range of systems and conditions. For increasing performances of PID controller, fine tuning of its parameters are required. Many authors have used different optimisation algorithms to tune parameters of PID controllers. These optimisation algorithms offer less performance. In this paper, the fine-tuned PID controller have proposed for the air levitation system. Advanced genetic algorithm is used for tuning parameters of PID controllers. For demonstration of efficiency and applicability of the proposed PID controller, simulation-based experimentations have been conducted. The proposed PID design method has been linked with other three optimisation techniques. Ant colony optimisation, particle swarm optimisation and fuzzy logic have been used for performances comparison of advanced genetic algorithm-based PID controllers. In experimental results, we have got very smallest value of IAE, ISE and ITAE using proposed method. It indicates that the proposed PID design method offers better performances than other three optimisation-based PID design methods and other existing methods.

Microwave heating control system using genetic algorithm-based PID controller

The superiorities of microwave heating technology have attracted many researchers for further development. Designing a satisfying temperature control system for the microwave system is one of them. To confirm that the controller can work with good system performance, the adjustment of controller parameters is important. Most previous research about microwave control systems did not focus on the tuning problem which is presented in this work. This paper provides the use of a genetic algorithm to adjust the best parameters for a PID controller. A microwave heating system with a single microwave power input and a single output temperature probe which is identified using the ARX model is examined. Evaluation of the proposed controller was conducted in the simulation environment using MATLAB. The result shows that the genetic algorithm can obtain the most optimized parameter for PID controller which can follow a certain heating pattern. The parameters for Kp, Ki, and Kd are 998.94, 79.42, and 79.78, respectively. It can minimize the value of integral absolute error for the given setpoint to 446.11.

Disk margin based robust stability analysis of a DC motor drive

This research paper investigates optimal tuning of PID co-efficients using the Tree-Seed Algorithm. A DC motor drive with a PID controller is considered a simulation example. The step response of the DC drive with Tree-Seed algorithm-based PID controller is compared with Ziegler Nichols (closed-loop) method and Skogestad Internal Model Control. In this research, the Disk-based margins are proposed to investigate the feedback loop for robust stability against gain and phase uncertainty (i.e the gain of open-loop is increased or decreased by 50%, and the phase is by ± 270) for various values of skew, σ. The motivation for using the disk margins is the limitations of the classical margins. For the specified uncertainty, the safe uncertainty of DC motor drive with Tree-Seed Algorithm based controller is identified as 3.47 times the specified uncertainty, and the disk margins are computed as the gain variation between 9.1% and 1100% of the normal value, and the phase variation of ± 800. The analysis identifies that the optimal Disk margins are obtained for the DC motor drive (with Tree-Seed Algorithm-based PID controller) for zero skew value. Finally, it is proved from the MATLAB simulation results that the DC motor drive with Tree-Seed Algorithm-based PID controller possesses good robustness and a wide range of stability margins for the uncertainty in the feedback loop. For the validation of the proposed methodology, the experimental setup is prepared and made the comparison of simulation and experimental results for rotor speed variations for the proposed tuning method.

Simulation based of DC-DC Converter for AVR System and PID Controller with Tree Seed Algorithm based AVR System for Synchronous Generator

The voltage is supervised by AVR an automatic voltage regulator. It transforms the varying voltage into a constant voltage. The most prevalent cause of voltage fluctuation is changes in load on the supply system. An excitation system is a system that provides the necessary field current to the synchronous machine's rotor winding. The most important characteristics of an excitation system are dependability under all operating circumstances, ease of control, and ease of maintenance, stability, and quick transient response. In the literature, several control systems, such as in the literature, PID controllers, adaptive control methods, and intelligent control methods have all been suggested. On the one hand to get an accurate and quick generator terminal voltage control, the usage of a step down chopper in the exciter circuit is suggested in this study. DC-DC converters are also called as Choppers. The Step down chopper, It changes a given DC input voltage into a determined DC output voltage The input voltage source is tie up with governable solid state device that acts as a switch. Switches can be made using metal oxide semiconductor field effect transistors (MOSFETs) or insulated gate bipolar transistors (IGBT). The field circuit of the generator is associated to the chopper and a Praportional-Intigral controller deviates the converter duty cycle to vary the generator's terminal voltage. to regulate the generator field voltage. On the other hand, Tree-Seed Algorithm (TSA) algorithm based PID controller is put forward for automatic voltage regulator system. The suggested approach calculates PID coefficients to the best of its ability. The execution of this TSA-based optimum PID controller is compared to that of various PID controllers produced in the literature utilizing different meta-hermetic optimization techniques. Comparative research in for the suggested schemes has a superior transient response and is more resistant to fluctuations in Generator load and DC input voltage.

Optimal Coronavirus Optimization Algorithm Based PID Controller for High Performance Brushless DC Motor

This paper presents an efficient coronavirus optimization algorithm (CVOA) to find the optimal values of the PID controller to track a preselected reference speed of a brushless DC (BLDC) motor under several types of disturbances. This work simulates how the coronavirus (COVID-19) spreads and infects healthy people. The initial values of PID controller parameters consider the zero patient, who infects new patients (other values of PID controller parameters). The model aims to simulate as accurately as possible the coronavirus activity. The CVOA has two major advantages compared to other similar strategies. First, the CVOA parameters are already adjusted according to disease statistics to prevent designers from initializing them with arbitrary values. Second, the approach has the ability to finish after several iterations where the infected population initially grows at an exponential rate. The proposed CVOA was investigated with well-known optimization techniques such as the genetic algorithm (GA) and Harmony Search (HS) optimization. A multi-objective function was used to allow the designer to select the desired rise time, the desired settling time, the desired overshoot, and the desired steady-state error. Several tests were performed to investigate the obtained proper values of PID controller parameters. In the first test, the BLDC motor was exposed to sudden load at a steady speed. In the second test, the continuous sinusoidal load was applied to the rotor of the BLDC motor. In the third test, different operating points of reference speed were selected to the rotor of the BLDC motor. The results proved that the CVOA-based PID controller has the best performance among the techniques. In the first test, the CVOA-based PID controller has a minimum rise time (0.0042 s), minimum settling time (0.0079 s), and acceptable overshoot (0.0511%). In the second test, the CVOA-based PID controller has the minimum deviation about the reference speed (±4 RPM). In the third test, the CVOA-based PID controller can accurately track the reference speed among other techniques.

Design of Evolutionary Algorithm Based PID Controller with filter for an Automatic Voltage Regulator

Otomatik gerilim regülatör (OGR) sistemi, generatör terminal gerilimini belirtilen seviyede tutmak için güç sistemlerinde yaygın olarak kullanılır. OGR sisteminde farklı denetleyiciler kullanılarak generatör terminal geriliminin denetimi gerçekleştirilmektedir. Araştırmacılar yaptıkları çalışmalarda OGR sisteminin dinamik performansını iyileştirmeyi ve sürekli durum hatasını sıfıra indirmeyi hedeflemektedir ve bu kapsamda evrimsel algoritmalar yardımıyla denetleyici tasarlamaktadır. Evrimsel algoritmalar, denetleyici parametrelerini belirlenen bir amaç fonksiyonunu göz önüne alarak optimal bir şekilde ayarlamak için yaygın olarak kullanılmaktadır. Bu çalışmada, bir OGR sisteminin denetimi için iki farklı filtreli oransal-integral-türevsel (PID-F) denetleyici tasarlanmıştır. Denetleyicilerin parametrelerini ayarlamak için atom arama optimizasyon (AAO) ve parçacık sürüsü optimizasyon (PSO) algoritmaları kullanılmıştır. Her bir denetleyici için OGR sisteminin geçici yanıt analizi, frekans analizi, dayanıklılık analizi Matlab/Simulink programında incelenmiş ve performans karşılaştırması yapılmıştır. Elde edilen sonuçlara göre, AAO algoritmasının PSO algoritmasından daha iyi sonuçlar verdiği görülmüştür. Ayrıca, AAO algoritması ile tasarlanmış PID-F denetleyicinin, AAO, PSO, biyocoğrafyaya dayalı optimizasyon (BDO) ve yapay arı koloni (YAK) algoritmaları ile ayarlanmış klasik PID denetleyicilere göre geçici yanıt karakteristiklerini iyileştirdiği ve sistemin kararlılığını ve dayanıklılığını arttırdığı sonucuna varılmıştır.

Voltage Regulation in Microcontroller fed Bidirectional Converter Based on Genetic Algorithm using PV

The grid connected Bi-directional Converter based on Photo-Voltaic (PV) uses a genetic algorithm (GA) to reduce the settling time of DC link voltage, and to increase voltage gain. The BDC meets load demand and regulates the energy flow from various sources. Problems in the existing system have Total Harmonic Distortion (THD), DC link voltage and its settling time is high. This paper presents a microcontroller based voltage regulation in bidirectional converter using solar power generation. The solar, battery and grid has used to generate the power, to improve output power quality, a Sliding Mode Controller (SMC) in the inverter is used. To improve the voltage regulation operation using genetic algorithm based PID controller. The bidirectional converter and inverter utilize solar, battery and grid power generation hybrid. The battery functions as a charge based on two-way converter operation and electricity generation modes. The power quality improvement in bidirectional converter has power factor correction, harmonics elimination and voltage regulation at AC mains. This paper proposes the analysis of genetic based PID control a process that effectively reduces the bidirectional converter harmonics; this aim is attained and shown in both MATLAB/Simulink and experimental results.

Evolutionary Algorithms based AGA and BPSO Computing for Hybrid Renewable Energy System

Solar and wind Renewable system have more advantages compared to Conventional energy sources and it is environment friendly and also from economic point of view...For overcoming the problems of Conventional Energy sources, this paper proposed an hybrid renewable energy system using Different evolutionary algorithm approaches. This proposed method hybrids the three systems namely, solar PV, wind and Fuel Cell which have the biggest potential to provide power. The battery and loads are also integrated with this hybrid system. Then, the charging and discharging of this system is controlled by an Optimized PID controller for balancing the generated power and load power. Here, the PID controller works based on the rules to provide an efficient system. Also, the tuning parameters of the PID controller are enhanced by Adaptive Genetic Algorithm and Particle Swarm Optimization. Finally, the performance of the Combined Solar and wind system is tunedwith the Evolutionary algorithm based PID controller and is compared with the existing hybrid renewable systems based on Generated Power, Load side Power, Speed Controller and Torque.

Design of genetic algorithm-based PID controller for a joule heated shape memory spring driven ball and beam system

Design of genetic algorithm-based PID controller for a joule heated shape memory spring driven ball and beam system

Design of genetic algorithm-based PID controller for a joule heated shape memory spring driven ball and beam system

The ball and beam system is a highly nonlinear laboratory model which helps to understand the modern and classical control theory. This paper presents the position control of a ball and beam system, which is actuated by shape memory alloy (SMA) springs in the antagonistic configuration. The system is designed such that the SMA actuator provides the bidirectional controlled force on the pulley to the beam through the shaft. This bidirectional motion of the beam needs to be controlled for the proper positioning of the ball with an appropriate controller design. Hence it is proposed to design/tune the controller optimally for the effective control and improved performance of the system which is not feasible using conventional Ziegler Nichols (ZN) design. The objectives of this paper are: 1) to use SMA spring as an actuator for ball and beam system; 2) to optimise the PID controller gain with genetic algorithm (GA); 3) to compare the performance of Ziegler Nichols (ZN) PID and GA-PID in simulation and experimentation to show the competence of GA-PID with ZN-PID through performance error indices.