Effect of GA based PID controller in bidirectional converter

In this paper, we analyze the design, realization and optimization of a photovoltaic (PV) system equipped with a maximum power point tracking (MPPT) command and detection circuit of dysfunction and convergence the system (CDCS) to the new maximum power point (MPP) following a perturbation due to the meteorological variation (solar radiation, temperature, ..) and the load. We have analyzed in depth manner the structure and optimization the operation of the MPPT command realized during this work. The results obtained show very good agreement between simulation and experiment. The experimental system realized during cloudy days or by varying the load, shows that it operates under optimum conditions independently of the meteorological variations. At each perturbation, the CDCS circuit detects it and convergences the system without restart it. The good efficiency of the direct current to direct current (DC-DC) converter used and the low loss (less than 7%) shows that the system realized can be used in a PV system to minimize losses due to connecting the panels to the load and in particularly to minimize the cost of installations. Key words: Panels and photovoltaic system, photovoltaic conversion, maximum power point tracking (MPPT) command, the detection circuit of dysfunction and convergence (CDCS), direct current to direct current (DC-DC) converter, simulator Pspice, optimal functioning, energy losses.

A fractional-order fuzzy proportional integral derivative (PID) controller is a controller that combines the benefits of fractional calculus and fuzzy logic with the conventional PID controller. In this paper, a four-stage optimization algorithm is proposed for the design of a Type-2 Fuzzy fractional-order PID controller based on the Fourier Series Method (FSM). Three distinct control structures are introduced: Type-2 fuzzy fractional PD + fractional PI controller, Type-2 fuzzy fractional PID, and Type-2 fuzzy fractional PD + Type-2 fuzzy fractional PI controller. In addition to a modified multi-performance criterion cost function, four integral performance criteria are employed as cost functions for each stage. The suggested algorithm avoids the utilization of the approximation equivalent for the fractional-order system and instead employs FSM. Furthermore, the approach optimizes the nonlinearity within the upper membership function (UMF) and the uncertainty range through the lower membership function, as opposed to arbitrary selection. By considering variations in the membership functions, the outcomes exhibit a superior response compared to previous investigations. The results of the three control structures are compared with the traditional PID controller, and simulation results demonstrate the feasibility of this technique. The findings suggest that by optimizing different integral performance criteria using this design technique, controllers for both integer and fractional-order plants can yield favorable step responses. The proposed algorithm is validated by comparing its step response performance with that of previous research, followed by a discussion on sensitivity analysis and computational requirements.

Electric load simulator (ELS) plays a vital role in the ground test and simulation of the electromechanical actuator (EMA) of an unmanned aerial vehicle (UAV), which is preferred by researchers for its dynamic response and high precision. However, clearance is an inevitable problem of the ELS system which can introduce backlash influencing the stability of the system. The backlash nonlinear can induce mechanical impact and impose limitations on the quality of loading torque control. In order to increase the accuracy of loading torque, a switching control method is presented to compensate for the backlash nonlinear. A time-optimal sliding mode control (TOSMC) method is used when the ELS system entering backlash, and a nonlinear proportional integral derivative (PID) controller is presented for the different system dynamic. A switching controller is also introduced to acquire suitable moment switching from TOSMC to nonlinear PID controller, ensuring the stability of the system. Experimental tests show that compared with the traditional PID control, this method can effectively reduce the torque overshoot when ELS crossing backlash and guarantee torque tracking speed and precision of ELS.

This paper investigates the integration of Fuzzy Logic with PID (Proportional, Integral, Derivative) controllers to enhance the performance of temperature control systems. Traditional PID controllers, widely used in various applications, often encounter difficulties with nonlinear, time-varying, and uncertain systems, resulting in performance issues like oscillations and overshoots. To mitigate these challenges, the adoption of Fuzzy PID control, which improves adaptability, robustness, and responsiveness by incorporating fuzzy logic into the control mechanism. The study involves simulations using Simulink to compare three distinct control models: conventional PID, standalone Fuzzy Control, and the hybrid Fuzzy PID Control across standardized control scenarios. Our results show that Fuzzy PID controllers significantly outperform traditional methods, especially in complex environments lacking precise mathematical modeling. The enhanced system response times and stability demonstrated by Fuzzy PID controllers suggest substantial potential for broader application across diverse industries, promising considerable improvements in operational efficiency and reliability.

A novel adaptive fuzzy proportional-integral-derivative (PID) controller based on the proposed piecewise PID controller is put forward for the sandglass-type floating, drilling, production, storage and offloading unit (FDPSO). First, on the premise of appropriate positioning accuracy, a piecewise linear PID controller considering the directional characteristic between the motions and environmental loads is proposed to conserve energy of traditional linear PID controller. Then, to improve the stability during the transient process and alleviate oscillation induced by piecewise PID controller, the robust adaptive fuzzy PID controller based on the piecewise PID controller is put forward, whose stability is verified by fuzzy Lyapunov method. The proposed fuzzy output variables are designed including motions in both the horizontal and vertical planes to solve the surge–pitch coupling problem and optimize energy consumption. The time-domain simulations with a complete model of all DP components and external forces are carried out. Therein, the thruster model is improved by introducing a virtual thrust command to decrease the obvious phase lag, response delay and amplitude reduction of the traditional thruster model. In addition, the application of wave filter is discussed, especially when the wave-frequency (WF) motions are more prominent and significant than low-frequency (LF) motions. Finally, comparisons between the proposed adaptive fuzzy PID controller and traditional linear PID controller are made and analyzed. Good control effects of the proposed controllers are demonstrated with the assistance of numerical simulations with a series of control parameters.

This paper demonstrates the design and analysis of automatic generation control using intelligent genetic algorithm tuned fuzzy based controller. A two area thermal power system simulated for four different scenarios considers a reheat steam turbine in each area with Generator rate constraints. The Integral Time Squared Error (ITSE) employed to get an objective function for the optimization of controller gains. The simulation results compared with the conventional Proportional Integral Derivative (PID) controller, Genetic Algorithm (GA) tuned PID controller and GA tuned Fuzzy PID controller. The proposed GA tuned Fuzzy based PID Controller can generate the best performance for peak overshoot, undershoot and settling time with step load disturbances. Robustness of the performance of the proposed controller provided with system parametric uncertainties.

Remote areas need to prioritize the pumping of water for agricultural and drinking purposes. Water pumping is a promising and advantageous application of renewable energy sources, such as hybrid energy sources that combine photovoltaic and wind power. A novel approach to effective flow control in hybrid PV and wind-powered water pumping systems is presented in this research. To optimize a Proportional Integral Derivative (PID) controller that governs motor speed, the proposed approach combines an optimized control algorithm, Unified Power Flow Controller (UPFC), and Fuzzy Q-learning. The main goal is to use UPFC to identify and mitigate Power Quality (PQ) issues. To achieve optimal UPFC operation under a range of solar and wind energy conditions, the Kookaburra Optimization Algorithm (KOA) is utilized. Fuzzy Q-learning provides adaptive parameter tuning based on real-time feedback and system dynamics, enabling the PID controller to be tuned online. Moreover, the detailed models of wind and photovoltaic systems highlight their influence on water pumping technology. A PID controller controlled by the KOA algorithm powers the UPFC, a crucial part of the system. The KOA is applied to optimize PID parameters for the UPFC, resulting in a unique contribution that emulates the hunting behavior of kookaburras for both local and global exploration. The PID controller can be easily adjusted online by the Fuzzy Q-Learning algorithm, which improves system stability when reacting to changing energy and environmental conditions. The combination of the Kookaburra Optimization Algorithm, PID control, and UPFC offers a sophisticated and flexible approach to the best possible operation of water pumping systems. Total Harmonic Distortion (THD) is successfully decreased by the proposed method, indicating better waveform quality and fewer harmonic components in the electrical system. The system’s ability to maintain a high-quality power supply is reflected in the THD value of 3.92% at 50Hz.

The tracking accuracy of speed plays a significant role in the autonomous vehicle's control and safety management. In this study, we presented a novel method called self‐adaptive proportional integral derivative (PID) of radial basis function neural network (RBFNN‐PID) which is shown with improved longitudinal speed tracking accuracy for autonomous vehicles. A forward simulation model of longitudinal speed control for autonomous vehicles is established based on the driver model of self‐adaptive RBFNN‐PID and the vehicle dynamics model. Based on that, we used the traditional PID and fuzzy control methods as benchmarks to demonstrate the edge of the self‐adaptive RBFNN‐PID control under the new European driving cycle. Simulation results show the RBFNN‐PID method is significantly more precise than the comparing groups, with a reduced error in the range of [−0.369, 0.203] m/s. The vehicle performance gives better ride comfort as well. In all, self‐adaptive RBFNN‐PID is proven to be effective in longitudinal speed control of autonomous vehicles and significantly outperforms the other two methods.

Traditional proportional–integral–derivative (PID) control performance optimization is an essential method to improve a loom’s warp tension control performance. This work proposes an improved genetic algorithm optimized PID control scheme to overcome the decline in control performance of the traditional PID control algorithm in a loom’s warp tension control system. Through the decoupling analysis of loom motion mechanism, the establishment of warp tension model and the optimization of fitness evaluation mechanism of genetic algorithm can effectively overcome the problems of local optimal solution and algorithm degradation of genetic algorithm. Simulation experiments were carried out with the traditional PID, the integral separation PID, and the genetic PID in warp tension control. The results show the advantage of the genetic-PID algorithm to control warp tension stability. Ultimately, according to the functional characteristics of the loom mechanism, a tension control platform for experimental studies was established. The test results show that the maximum fluctuation range of warp tension is within [−2, +6] at the test speed of 850 rpm, which meets the requirements of long-term stable and reliable control of warp tension under different weaving conditions.

In guided missiles, the major function of a seeker is to track a target continuously. In target tracking process most seekers employ a gimbal system, usually a two-axis, on which a tracking device and a rate gyroscope are mounted. For such guided missiles, the stabilization of gimbal is essential for maintaining the tracking device directing towards the target. Various control methods have been developed for the gimbal stabilization to make the gimbal to respond faster for a rate command input so that the gimbal follows the rate input in the presence of disturbances. Here, a fuzzy self-tuning proportional, integral, derivative (PID) type controller based on parameters adaptive method is proposed for two-axis gimbal stabilization which can be applicable for most guided missiles. The controller gains in the proposed control technique are made of input and output scaling factors used in fuzzification and defuzzification processes. The scaling factors are tuned online based on the missile rotation rate and the gimbal rate response. The response of the gimbal in the stabilization loop with the proposed controller has been analyzed in MATLAB for various missile rotation rates and the simulation results are compared with a traditional PID controller. The results prove the efficiency of the proposed control method which gives a better and faster response with zero steady-state error than the traditional control method.

The three-phase separator has a fundamental role in oil production, due to its multivariable and nonlinear characteristics, controlling it represents a major challenge. In the engineering training academy, having a real industrial process represents a significant cost, however, thanks to technological advances, providing a virtual industrial process is possible. In this research work, a hardware in the loop simulation (HIL) environment is proposed, which includes a three-phase horizontal separator, and its controllers implemented in a physical control device. The proposed system is flexible enough to perform different control algorithms and implement them in any control device. The design methodology of the proposed system includes three sections. In the first section, the nonlinear multivariable mathematical models of the industrial process are obtained. The second section corresponds to the development of the virtual environment, in which the 3D modeling of the industrial process is performed. In the third section, the control strategies are proposed, designed and implemented in a physical control device. To validate the applicability and performance of the variables to be controlled, two control strategies are implemented: a traditional proportional integral derivative (PID) control, and a multivariable model predictive control (MPC). Finally, a comparison of the implemented controllers performance is made for the controlled variables: water level $$h(t)_w$$ , oil level $$h(t)_l$$ and separator pressure P(t).

Superheat steam temperature control is critical to the normal and optimal operation of a power plant. Usually, cascade proportional integral derivative (PID) control system is introduced to regulate the superheat temperature with the PID parameters fixed constant. However, Due to the gains and time constants of the controlled plant change significantly with disturbances, the control effects of the conventional PID are not ideal. In this paper, a new adaptive control strategy is proposed to improve conventional superheat steam temperature control. Based on cascade PID control system, recursive least squares (RLS) method is used to identify the plant parameters and then genetic algorithm (GA) is applied to adjust the parameters of the PID controllers. MATLAB simulation investigation indicates that, compared to the parameter fixed PID system, GA-self-tuned adaptive control system is able to reject endogenous and exogenous disturbances more effectively and rapidly, thus has better self-adaptation capability and robustness.

The increasing use of battery-powered electronic devices necessitates efficient and fast charging solutions. This research aims to optimize battery charging at a charging station using the Maximum Power Point Tracking (MPPT) method integrated with Proportional Integral Derivative (PID) control. This method is chosen to address overshoot issues and speed up the stabilization process. The study begins with a literature review to understand the basic theory of MPPT optimization and PID control. Subsequently, hardware and software for the battery charging station were designed and developed. An Arduino ATMega328 microcontroller was used as the main controller, with an LCD to display charging information. Tests were conducted on 12V batteries with capacities of 5Ah and 9Ah. The results showed that using PID control in MPPT optimization successfully increased battery charging efficiency. The fastest charging duration was achieved for a 12V 5Ah battery with parameters Kp=6, Ki=9, and Kd=4, where the battery was fully charged in 19 minutes, and for a 12V 9Ah battery with parameters Kp=5, Ki=3, Kd=1, where the battery was fully charged in 27 minutes. Conversely, the longest charging duration occurred for a 12V 5Ah battery with parameters Kp=5, Ki=3, Kd=1, where the battery was fully charged in 34 minutes, and for a 12V 9Ah battery with parameters Kp=2, Ki=5, Kd=1, with a charging time of 38 minutes. The designed system was able to reduce input current and increase input voltage to reach the 14.5V set point, ensuring that there was no excessive voltage and current that could prolong battery life. This research demonstrates that the PID method in MPPT optimization is effective in accelerating and optimizing battery charging. Future research is expected to further develop this method to improve battery charging efficiency and duration. Keywords: MPPT, PID, Optimization, Battery Charger, Optimal, PWM ABSTRAK Penggunaan perangkat elektronik yang bertenaga baterai semakin meningkat memerlukan solusi pengisian yang efisien dan cepat. Penelitian ini bertujuan untuk mengoptimalkan pengisian baterai pada stasiun pengisian dengan menggunakan metode Maximum Power Point Tracking (MPPT) yang diintegrasikan dengan kontrol Proportional Integral Derivative (PID). Metode ini dipilih untuk mengatasi masalah overshoot dan mempercepat waktu menuju keadaan stabil. Penelitian dimulai dengan studi literatur untuk memahami teori dasar optimasi MPPT dan kontrol PID. Selanjutnya dirancang dan dibuat perangkat keras serta perangkat lunak untuk stasiun pengisian baterai. Mikrokontroler Arduino ATMega328 digunakan sebagai pengendali utama dengan LCD untuk menampilkan informasi pengisian. Pengujian dilakukan pada baterai aki 12V dengan kapasitas 5Ah dan 9Ah. Hasil pengujian menunjukkan bahwa penggunaan kontrol PID pada optimasi MPPT berhasil meningkatkan efisiensi pengisian baterai. Durasi pengisian yang paling cepat dicapai baterai 12V 5ah dengan parameter Kp=6 Ki=9 dan Kd=4 dimana baterai terisi penuh dalam waktu 19 menit dan baterai 12V 9ah dengan parameter Kp=5, Ki=3, Kd=1 dimana baterai terisi penuh dalam waktu 27 menit. Sebaliknya durasi pengisian terlama terjadi pada baterai 12V 5ah dengan parameter Kp=5, Ki=3, Kd=1 baterai terisi penuh dalam waktu 34 menit dan baterai 12V 9ah dengan parameter Kp=2 Ki=5, Kd=1 dengan waktu pengisian 38 menit. Sistem yang dirancang mampu mengurangi arus input dan meningkatkan tegangan input untuk mencapai set point 14.5V menjaga agar tidak terjadi tegangan dan arus berlebih yang dapat memperpanjang umur baterai. Penelitian ini menunjukkan bahwa metode PID pada optimasi MPPT efektif dalam mempercepat dan mengoptimalkan pengisian baterai. Penelitian selanjutnya diharapkan dapat mengembangkan lebih lanjut metode ini untuk meningkatkan efisiensi dan durasi pengisian baterai. Kata kunci: MPPT, PID, Optimasi, Charger Baterai, Optimal, PWM

Large Scale Multiple Input Multiple Output (MIMO) technology is a promising technology in wireless communications, and it is already at the heart of many wireless standards. MIMO technologies provide significant performance improvements in terms of data transfer rate and reduction the interference. However, MIMO techniques face large-scale linear dynamic problems such as system stability and it will be possible to overcome this problem by tuning the proportional integral derivative (PID) in continuous systems. The aim of this paper is to design an efficient model for MIMO based on Adaptive Neural Inference System (ANFIS) controller and compare it with a traditional PID controller. and evaluated by objective function as integral time absolute error (ITAE). ANFIS is used to train fuzzy logic systems according to the hybrid learning algorithm. The training involves the fuzzy logic parameters through simulating the validation data to represent a model to know the correctness and effectiveness of the system. It is optimizes the system performance in real time, however, to avoid potential problems such as easy local optimality. In the proposed approach stability is guaranteed as the initial steady-state scheme. ITAE is combined with ANFIS to minimize the steady-state transient time responses between the high-order initial pattern and unit amplitude response. The proposed ANFIS self-tuning controller is evaluated by comparing with the conventional PID. MATLAB simulink is used to illustrate the results and demonstrate the possibility of adopting ANFIS controller. The simulation results showed that the performance of ANFIS controller is better than the PID controller in terms of settling time, undershoot and overshoot time.

This paper aims to develop a Proportional Integral Derivative (PID) controller for the Automatic Voltage Regulator (AVR) of the Synchronous Generator, Automatic Voltage Regulator is responsible for keeping the output voltage of the Generator constant and stable. The Automatic Voltage Regulator system can be represented by a fourth order Transfer Function. The parameters of the controller are evaluated by using the Genetic Algorithm tuning method, for this method the Genetic Algorithm Optimization toolbox in MATLAB is used. A PID controller and a PID Filter controller are developed in order to come up with the optimum controller design for the system. The performance of the Genetic Algorithm based controller has been compared with the classical Ziegler-Nichols based controller. The results show that the Genetic Algorithm based controller outperforms the Ziegler-Nichols based controller, the optimum controller design is achieved by the GA based PID filter controller.