Design and implementation of PSO/ABC tunned PID controller for Buck converters

Massive Multiple Input Multiple Output (MIMO) systems can significantly improve the system performance and capacity by using a large number of antenna elements at the base station (BS). To reduce the system complexity and hardware cost, low complexity antenna selection techniques can be used to choose the best antenna subset while keeping the system performance at a certain required level. In this paper, Tabu Search (TS) and three bio-inspired optimization algorithms were used for antenna selection in Massive MIMO systems. The three bio-inspired algorithms were: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Artificial Bee Colony (ABC). Simulations showed promising results for the TS by achieving higher capacity with GA than PSO and ABC, and much shorter CPU time than any of the bio-inspired techniques.

This paper proposes the design of a novel Modified Counter based Digital Pulse Width Modulation generator to analyze the performance of the closed-loop DC-DC buck converter. The closed-loop DC-DC buck Converter uses the Proportional Integral controller to regularize the output voltage and the bio-inspired algorithms namely Particle Swarm Optimization and Ant Colony Optimization are considered for the generation of the optimal values for the PI gains namely KP and KI. Conventionally, the switching of the buck converter is controlled by the PWM signals that exhibit complexity in design. The updated Digital Pulse Width Modulation techniques were suitable for voltage regulation at the cost of high clock frequency requirement, increase in the design area for real implementation, and the trade-off between the switching frequency and the component size of the buck converter. To overcome these, the modified Counter based Digital Pulse width modulation that generates the high switching frequency DPWM is developed in HDL. Then the proposed technique is validated in the closed-loop PI-based DC-DC buck converter using the System Generator MATLAB SIMULINK. To regularize the voltage output, the PI controller is included along with the optimization algorithms such as Particle Swarm Optimization and Ant Colony Optimization to optimize the PI gains. The time transient analysis of the proposed method exhibits improvement in the ACO based design compared to the PSO based method. The power and area are manipulated by using the Cadence and Xilinx tools

This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.

This paper is proposed to show the interaction between the LFC and the AVR loops in the Power Plant. The combinational effects of these two LFC and AVR loops are studied by extending the linear zed AGC system. A complete system model for LFC with Speed Governor, Turbine, Integral controller and Power System and AVR with Excitation System and PID controllers are described. LFC is for regulation of system frequency. It is also called a power factor control loop and influence the active power balances in the power system network. The LFC is achieved by the speed-governor mechanism. The basic principle of the speed-governor mechanism is to adjust itself as per the load variations. The voltage of the generator is proportional to the speed and excitation of the generator. If the speed is kept at constant, then the excitation system is used to control the voltage. The voltage control is also called as an excitation control system. This combined model is tested with conventional PID Controller, PID with Power System Stabilizer and Fuzzy-PID with Power System Stabilizer. The results are shown by using simulation; this will be reachable in dynamic and steady state responses.

Procedural Content Generation (PCG) has emerged as a powerful approach for automating game content creation, offering significant benefits in terms of cost reduction and time efficiency compared to traditional game design and development processes (Zhang, Zhang, & Huang, 2022). While Genetic Algorithms (GAs) have been widely used in PCG, alternative metaheuristic algorithms such as Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) have demonstrated their effectiveness in delivering high-quality solutions and efficient optimization capabilities across different problem domains (Amato, 2017). However, their application in PCG remains limited. I aim to evaluate the performance of PSO and ABC in map layout generation, challenging the conventional use of GAs. By comparing three metaheuristic algorithms (GA, ABC, and PSO) I seek to assess the effectiveness of these approaches in generating game levels and identify any obvious differences in their performance characteristics. Comprehensive experiments are conducted, applying GA, ABC, and PSO to a map layout generation. Metrics like convergence speed and content quality are used to evaluate the generated game content. My findings reveal that both ABC and PSO demonstrate advantages over traditional GA implementations when generating game levels, indicating their potential for enhancing PCG. In this presentation, I will share the results of comparing three metaheuristic algorithms (GA, PSO, and ABC) in map layout generation for game levels, emphasizing the potential benefits of leveraging diverse algorithmic approaches to create more captivating and immersive game worlds. Also, I will conclude with a call for further research in this area to expose new possibilities in content generation. By considering varied metaheuristic approaches, game developers can improve content generation techniques and create more captivating and interactive player experiences.

The role of an intelligent control system with a certain stage of autonomy is prerequisite for effective operation. We designed a particle swarm optimization, fuzzy proportional integral derivative (PSOFPID) controller using MATLAB for a set point voltage and frequency. The projected controller intended to ease the frequency and the terminal potential difference constantly under any operating conditions and loads which can be attained in the wanted range via the rule of the generation system. PSOFPID used to carry out the AVR system auctions main voltage control. The existing algorithm was based on particle swarm optimization (PSO), and Sugeno fuzzy logic (SFL). It required optimal tuning for thematic factory operation of the generation system. The newly developed controller combined the PSO and fuzzy logic control (FLC) to determine the optimal PID controller of generator parameters in the AVR system. The PSOFPID controller was used as a hybrid full control system for the voltage and frequency. Optimal PID gains obtained by a combined PSO and SFL for various operating conditions of PSO (β and know about birds’ no.) were employed to develop the principle subject of the Sugeno fuzzy system. The hybrid controller arranged the control signal based on communication and thereby decreases the voltage error and the swaying in the terminal voltage and frequency control process. An outstanding potential and frequency control presentation was achieved when the projected hybrid controller was broken on the AVR system in synchronous generator to improve the transient response.

This paper presents an enhancement of the output performance of a linear buck converter system for the mobile (smartphone) devices using an adaptive digital Proportional–Integral–Derivative (PID) controller with off-line swarm optimization algorithm. The work focuses on improving the use of using single-input single-output (SISO) digital Field Programmable Gate Array (FPGA)-PID to control the linear buck converter system. The goal of the proposed adaptive SISO-FPGA-PID voltage-tracking controller is to rapidly and precisely identify the optimal voltage control action (optimal on-off duration time) that is used to control the buck converter output voltage level in order to avoid the troubleshooting hardware problem issues on mobile devices. The Particle Swarm Optimization (PSO) algorithms are used to find and tune the three weights of the SISO-FPGA-PID controller. The numerical simulation results and the experimental work using Spartan-3E xc3s500e-4fg320 board with Verilog hardware description language (HDL) show that the proposed controller is more accurate in terms of voltage error and the number of function evolutions are of high reduction. As well as to generate a smooth voltage control response without voltage oscillation in the output by investigating under mobile applications variations such as using Bluetooth, WI-FI, and CPU operating voltage when these results are compared with other controllers.

Unlike a traditional topology of a control system with feedback connected plant and controller systems, this paper discusses a new topology that uses an intermediate network system between the plant and controller systems. More specifically, this paper presents modeling frameworks as well as computational procedures for the design of the closed-loop control system when the nodes of the plant, controller, and network systems fail to operate and the communication links fail to transfer information between communicating nodes. It also discusses conditions required for the synthesis of the controller and network systems, and characterizes the maximum number of unavailable nodes and the maximum probability of unavailable communication links. The aim of the presented results is to provide a more practical approach to the design of the new topology. Also, a four-tank process system is used to demonstrate details of the presented modeling frameworks and computational procedures.

The most important criterion in the design of unmanned air vehicles is to successfully complete the given task and consume minimum energy in the meantime. This paper presents a comparison of the performances of metaheuristic methods such as Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to design controllers and DC/DC buck converters for optimizing the energy consumption and path following error of a PEM fuel cell-powered quadrotor system. Hence, the system consists of two PSO- and GWO-based optimizers. Optimizer I is used for determining the parameters of the PD controller, which is used for minimizing the route-tracking error. On the other hand, the I controller parameters and the values of the DC/DC buck converters’ components are determined by Optimizer II to minimize the voltage-tracking errors of the converters. Both optimizers work together in the system and try to minimize tracking errors while also minimizing power consumption by using suitable objective functions. Simulation results demonstrate the effectiveness of the PSO- and GWO-based design of the controllers and converters in enhancing energy efficiency and improving the quadrotor’s flight stability. For step inputs, the GWO-based optimized system shows better performance according to power consumption and the time domain criteria such as rise time and settling time. However, the PSO-based optimized system shows 24.707% better performance for overshoot. On the other hand, 10.8866% less power consumption is observed for the GWO-based optimized system. This power efficient performance of the GWO-based system increases to 18% for the complex route involving ramp and step inputs. Then, a 39 s route test was performed and the total power consumptions for the GWO-based optimized and PSO-based optimized systems were observed to be 168.0015 W/s and 179.9070 W/s, respectively. This means that GWO-based optimizers provide more energy-efficient performance for complex routes. On the other hand, it was determined that the tracking errors in the performance of the desired and actual values of both translational and rotational movement parameters and the forces and torques required for the quadrotor to follow this route were obtained at a maximum of 4% for systems optimized with both techniques. This shows that the full systems optimized with both GWO and PSO algorithms significantly increase their energy efficiency and provide maximum route-following performance.

This paper proposes a new method for designing a self-tuning PID controller. A PID controller is designed on the basis of generalised predictive control with a terminal matching condition (γGPC) to automatically tune its PID parameters. The proposed PID controller has a time-varying proportional gain to precisely achieve γGPC performance. Furthermore, a PID controller is designed on the basis of γGPC extended by using coprime factorisation to obtain stable proportional gain. Hence, γGPC can be well approximated by the proposed PID controller. A numerical example demonstrates its effectiveness.

The aim of this paper is to design a PID controller based on an on-line tuning bat optimization algorithm for the step-down DC/DC buck converter system which is used in the battery operation of the mobile applications. In this paper, the bat optimization algorithm has been utilized to obtain the optimal parameters of the PID controller as a simple and fast on-line tuning technique to get the best control action for the system. The simulation results using (Matlab Package) show the robustness and the effectiveness of the proposed control system in terms of obtaining a suitable voltage control action as a smooth and unsaturated state of the buck converter input voltage of ( ) volt that will stabilize the buck converter system performance. The simulation results show also that the proposed control system when compared with the other controllers results has the capability of minimizing the rising time to ( sec) and the settling time to ( sec) in the transient response and minimizing the voltage tracking error of the system output to ( ) volt at the steady state response. Furthermore, the number of fitness evaluations is decreased.

DC motor is the most common choice if the wide range of adjustable speed drive operation is desired. In many studies, the DC motor controllers have been implemented without considering the final control element (FCE), leading to a non-realistic performance. In this paper, the authors have suggested a new approach to obtain the adaptive and robust control of DC motor. Buck converter, driven by high frequency PWM signals obtained from PID controller has been chosen as FCE in this study. Ziegler-Nichols method is applied to the combined system of dc motor and buck converter, to get the initial settings of PID controller. Using fuzzy logic, these settings are online updated corresponding to the changes that may occur during system operating conditions. Also, this approach has a robust performance against disturbance/parameter variations. The buck converter has been simulated using sim-power system library of MATLAB. Comparative results have been shown for PID controller and adaptive fuzzy-PID controller for both tracking and regulatory type of control problems to obtain excellent performance with no steady state error.

In this work, a hybrid PID controller, combining Zeigler–Nichols (ZN) theory and the dominant pole placement, has been developed for the automatic voltage regulator (AVR) systems. Furthermore, the reduced order models (ROMs) of AVR systems, namely, AVR-1, AVR-2, and AVR-3, have been established using Particle Swarm Optimization (PSO), Big Bang Big Crunch (BBBC), and Pade’s approximation, respectively. Then, the proposed PID controller has been applied to both the original and developed lower order models of AVR to check the efficacy and robustness of the developed hybrid controller. It is shown that the proposed hybrid PID controller provides a very small value of the percentage peak overshoot (%M p ) and settling time (t s ). Also, the gain margin (GM) and phase margin (PM) are found to be in the desired range. Finally, the sensitivity analysis has also been performed to check the robustness of the proposed controller. The sensitivity function, complementary sensitivity function, and various performance indices have been evaluated for the proposed controller based on Bode’s Integral (BI), Tyreus–Lyuben (T–L), and Internal Model Control (IMC) approaches. The comparative studies of the results show that the proposed controller has smaller values of performance indices and sensitivity function.

This study addresses the modeling of high-order FIR (Finite Impulse Response) systems using lower-order FIR filters, which is a significant challenge in digital filter design and system identification. The primary objective is to approximate the amplitude frequency response of a fifth-order FIR system with a fourth-order FIR model while minimizing the modeling error. To achieve this, four metaheuristic optimization algorithms—Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Quick ABC (qABC), and Memetic Algorithm (MA)—are employed. Each algorithm is executed in 30 independent trials to assess its robustness and performance.The modeling task is formulated as an optimization problem where the mean squared error (MSE) between the reference system’s frequency response and the approximated model’s response is minimized. The performance of each algorithm is evaluated not only by their convergence behavior over iterations but also by detailed frequency-domain analyses, including both amplitude response and power spectrum comparisons. Furthermore, the statistical properties of each algorithm’s output—such as mean, standard deviation, minimum and maximum MSE values—are used to evaluate consistency and accuracy.The results show that the ABC algorithm achieves the best overall balance, providing the lowest average MSE and smallest variance, indicating strong stability and accuracy. The MA algorithm, while achieving the lowest minimum MSE in some runs, exhibits higher variability, suggesting less consistency. The qABC algorithm demonstrates fast convergence and high potential for accurate modeling, but its relatively high standard deviation points to a lack of solution stability. The PSO algorithm, although able to reach low error values in some runs, generally produces more dispersed and inconsistent solutions due to its broader error range.In conclusion, this work presents a comprehensive comparative evaluation of metaheuristic algorithms for low-order FIR filter modeling. The findings highlight the superiority of ABC and MA algorithms in terms of accuracy and stability, and point toward the potential of hybrid or adaptive versions of these algorithms for future filter design and system identification problems.Keywords: FIR filter, system modeling, PSO, ABC, Quick ABC, MA.

In this study, a nonlinear Archimedes wave swing (AWS) energy conversion system was employed to enable the use of irregular sea waves to provide useful electricity. Instead of the conventional PI controllers used in prior research, this study employed fractional-order PID (FOPID) controllers to control the back-to-back configuration of AWS. The aim was to maximize the energy yield from waves and maintain the grid voltage and the capacitor DC link voltage at predetermined values. In this study, six FOPID controllers were used to accomplish the control goals, leading to an array of thirty parameters required to be fine-tuned. In this regard, a hybrid jellyfish search optimizer and particle swarm optimization (HJSPSO) algorithm was adopted to select the optimal control gains. Verification of the performance of the proposed FOPID control system was achieved by comparing the system results to two conventional PID controllers and one FOPID controller. The conventional PID controllers were tuned using a recently presented metaheuristic algorithm called the Coot optimization algorithm (COOT) and the classical particle swarm optimization algorithm (PSO). Moreover, the FOPID was also tuned using the well-known genetic algorithm (GA). The system investigated in this study was subjected to various unsymmetrical and symmetrical fault disturbances. When compared with the standard COOT-PID, PSO-PID, and GA-FOPID controllers, the HJSPSO-FOPID results show a significant improvement in terms of performance and preserving control goals during system instability