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This paper presents a comprehensive study on the implementation and analysis of PID controllers in an automated voltage regulator (AVR) system. A novel tuning technique, Virtual Time response-based iterative gain evaluation and re-design (V-Tiger), is introduced to iteratively adjust PID gains for optimal control performance. The study begins with the development of a mathematical model for the AVR system and initialization of PID gains using the Pessen Integral Rule. Virtual time-response analysis is then conducted to evaluate system performance, followed by iterative gain adjustments using Particle Swarm Optimization (PSO) within the V-Tiger framework. MATLAB simulations are employed to implement various controllers, including the V-Tiger PID controller, and their performance is compared in terms of transient response, stability, and control signal generation. Robustness analysis is conducted to assess the system's stability under uncertainties, and worst-case gain analysis is performed to quantify robustness. The transient response of the AVR with the proposed PID controller is compared with other heuristic controllers such as the Flower Pollination Algorithm, Teaching–Learning-based Optimization, Pessen Integral Rule, and Zeigler-Nichols methods. By measuring the peak closed-loop gain of the AVR with the controller and adding uncertainty to the AVR's field exciter and amplifier, the robustness of proposed controller is determined. Plotting the performance degradation curves yields robust stability margins and the accompanying maximum uncertainty that the AVR can withstand without compromising its stability or performance. Based on the degradation curves, robust stability margin of the V-Tiger PID controller is estimated at 3.5. The worst-case peak gains are also estimated using the performance degradation curves. Future research directions include exploring novel optimization techniques for further enhancing control performance in various industrial applications. [ABSTRACT FROM AUTHOR]

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Linear–quadratic regulator (LQR) is a control technique successfully implemented for several complex systems. However, LQR technique in certain cases fails due to disturbances mainly caused by the improper choice of weighting matrices Q and R. Methods using particle swarm optimisation (PSO) are used to find the weighting matrices of LQR and have been proven to be superior. The paper proposes an output feedback LQR proportional–integral–derivative (PID) controller, which is also compared with LQR and PSO-based LQR. The output feedback LQR–PID provides a better response, and the effectiveness of the proposed method is demonstrated through simulations, and experimental validations are conducted on various cases. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are computational techniques based on the collective behavior of swarms and the study of organisms acting in communities. These algorithms involve different types of organisms. Finding controller values for nonlinear systems is a challenging task using classical approaches. Hence, using metaheuristics to find the controller values of a twin rotor multi-input multi-output system (TRMS), one of the nonlinear systems studied in the literature, seems to be more appropriate than using classical methods. In this study, different types of metaheuristic algorithms were used to find the PID controller values for a TRMS, including a genetic algorithm (GA), a dragonfly algorithm, a cuckoo algorithm, a particle swarm optimization (PSO) algorithm, and a coronavirus optimization algorithm (COVIDOA). The obtained graphs were analyzed based on certain criteria for the main rotor and tail rotor angles to reach the reference value in the TRMS. The experimental results show that when the rise and settlement times of the TRMS are compared in terms of performance, the GA took 1.5040 s (seconds) and the COVIDOA took 9.59 s to increase the pitch angle to the reference value, with the GA taking 0.7845 s and the COVIDOA taking 2.4950 s to increase the yaw angle to the reference value. For the settling time, the GA took 11.67 s and the COVIDOA took 28.01 s for the pitch angle, while the GA took 14.97 s and the COVIDOA took 26.69 s for the yaw angle. With these values, the GA and COVIDOA emerge as the foremost algorithms in this context. [ABSTRACT FROM AUTHOR]

To improve the stability of underactuated robotic arms in the sorting process of fragile parts, a fuzzy PID control algorithm is proposed to optimize their grasping performance by improving the particle swarm algorithm. Firstly, the characteristics of the underactuated robotic arm grasping force control system were analyzed and a specific strategy was proposed that combines particle swarm algorithm optimization algorithm with fuzzy PID grasping force control system. Secondly, methods such as dynamic inertia weights were introduced into particle swarm algorithm to improve its iteration speed and avoid falling into local optima. On this basis, the improved particle swarm optimization algorithm was used to optimize the relevant parameters of the fuzzy PID controller, achieving online self-tuning of fuzzy rule weights and quantization factors, and solving the problem of PID parameters unable to be dynamically adjusted. Finally, the method was simulated and analyzed. The results show that the control algorithm designed in this paper can achieve stable grasping force within 0.8 s. with a steady-state error of less than 0.2%, and a disturbance tuning time of 0. 262 s. The transient response speed, control accuracy, and stability of the system are significantly improved. [ABSTRACT FROM AUTHOR]

Boost converters often face challenges such as sluggish dynamic behavior, inadequate voltage regulation, and variations in input voltage and load current. These issues necessitate the need for closed-loop operation. Nature-inspired optimization algorithms (NIOA) have demonstrated their effectiveness in delivering enhanced solutions for various engineering problems. Several studies have been conducted on the use of proportional-integral-derivative (PID) controllers for controlling boost converters, as documented in the literature. Some studies have shown that using fractional order PID (FO-PID) controllers can lead to better performance than traditional PID controllers. Nevertheless, implementing FO-PID can be quite complex. Considering the widespread use of commercial PID controllers in industrial settings, this study focuses on finding the best tuning for these controllers in DC-DC boost converters. The approach used is particle swarm optimization (PSO) based on integral performance criteria. Simulation results indicate that the proposed controller achieves superior performance, evidenced by the lowest settling time, overshoot, integral absolute error (IAE), and integral squared error (ISE) values under varying input voltage and load current conditions, compared to both PID and FO-PID controllers. These findings have been confirmed through hardware implementation, which demonstrates the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

The primary goal of enhancing automatic generation control (AGC) in interconnected multi-area power systems is to ensure high-quality power generation and reliable distribution during emergencies. These systems still struggle with consistent stability and effective response under dynamic load conditions despite technological advancements. This research introduces a secondary controller designed for load frequency control (LFC) to maintain stability during unexpected load changes by optimally tuning the parameters of a Proportional–Integral–Derivative (PID) controller using pelican optimization algorithm (POA). An interconnected power system for ith multi-area is modeled in this study; meanwhile, for determining the optimal PID gain settings, a four-area interconnected power system is developed consisting of thermal, reheat thermal, hydroelectric, and gas turbine units based on the ith area model. A sensitivity analysis was conducted to validate the proposed controller's robustness under different load conditions (1%, 2%, and 10% step load perturbation) and adjusting nominal parameters (R, Tp, and Tij) within a range of ±25% and ±50%. The performance response indicates that the POA-optimized PID controller achieves superior performance in frequency stabilization and oscillation reduction, with the lowest integral time absolute error (ITAE) value showing improvements of 7.01%, 7.31%, 45.97%, and 50.57% over gray wolf optimization (GWO), Moth Flame Optimization Algorithm (MFOA), Particle Swarm Optimization (PSO), and Harris Hawks Optimization (HHO), respectively. [ABSTRACT FROM AUTHOR]

This study introduces an advanced dual-mode compound attitude control framework for reusable launch vehicles (RLVs), underpinned by an enhanced particle swarm optimization (PSO) algorithm. This innovative strategy is tailored to meet the stringent demands for precision and robust anti-interference capabilities across the entire flight envelope of RLVs. The research commences with the formulation of a comprehensive attitude dynamics model and diverse heterogeneous actuator representations, meticulously crafted to reflect the distinct phases of RLV flight. Building upon this foundation, a synergistic control paradigm is engineered, integrating PID and fuzzy PID controllers and dynamically adjusting the inertia weights and learning factors of the PSO algorithm to achieve the balance between global and local optimization performance, complemented by a refined fitness evaluation function. The crux of the study is the application of an upgraded PSO algorithm to fine-tune the controllers' weighting coefficients, culminating in an optimized dual-mode compound attitude control system. A series of comparative simulation analyses are thoroughly executed to appraise the system's responsiveness, stability, precision, and resilience to interference. The simulation outcomes demonstrate an average reduction of 42.21% in step response overshoot, an 18.52% decrease in settling time, a 53.18% decline in steady-state error, a 56.80% drop in the maximum deviation value, a 55.82% improvement in recovery speed, and a 75.61% enhancement in tracking precision for the proposed controller. The findings clearly verify the superior performance of the proposed control system, affirming its contribution to the advancement of RLV attitude control. The proposed controller holds promising potential for real application in attitude control systems and is poised to augment the reliability and mission success rate of RLVs under intricate flight scenarios. [ABSTRACT FROM AUTHOR]

To address the control performance and harmonic suppression issues in maritime vessel rudder permanent magnet servo systems, a fractional-order PID controller was introduced into the existing improved repetitive control strategy. We used the Oustaloup approximation algorithm and particle swarm optimization for tuning the fractional-order PID controller. The optimized parameters substantially improved the control performance. By integrating the fractional-order PID controller with the improved repetitive controller, a composite fractional-order PID repetitive control strategy was formed. Finally, MATLAB/Simulink simulations were conducted to compare and verify the disturbance rejection and harmonic suppression capabilities of the improved control strategy. The results demonstrate its superior control performance, thereby increasing the practicality of the control system in dealing with various situations. [ABSTRACT FROM AUTHOR]

The study investigates the efficacy of a bioinspired Particle Swarm Optimization (PSO) approach for PID controller tuning in Radiofrequency Ablation (RFA) for liver tumors. Ex-vivo experiments were conducted, yielding a 9th order continuous-time transfer function. PSO was applied to optimize PID parameters, achieving outstanding simulation results: 0.605% overshoot, 0.314 seconds rise time, and 2.87 seconds settling time for a unit step input. Statistical analysis of 19 simulations revealed PID gains: Kp (mean: 5.86, variance: 4.22, standard deviation: 2.05), Ki (mean: 9.89, variance: 0.048, standard deviation: 0.22), Kd (mean: 0.57, variance: 0.021, standard deviation: 0.14) and ANOVA analysis for the 19 experiments yielded a p-value ≪ 0.05. The bioinspired PSO-based PID controller demonstrated remarkable potential in mitigating roll-off effects during RFA, reducing the risk of incomplete tumor ablation. These findings have significant implications for improving clinical outcomes in hepatocellular carcinoma management, including reduced recurrence rates and minimized collateral damage. The PSO-based PID tuning strategy offers a practical solution to enhance RFA effectiveness, contributing to the advancement of radiofrequency ablation techniques. [ABSTRACT FROM AUTHOR]

Due to the strong nonlinearity and large torque ripple of switched reluctance motor, a cascaded fractional order PID sliding mode control system is designed. Considering the difficulty of parameter adjustment caused by the complexity of the system, the optimization technology using dung beetle optimizer is introduced to optimize the designed cascaded controller and control the speed and torque ripple. To prove the effectiveness of the proposed cascaded controller, a control model is built in MATLAB/Simulink, and compared with whale optimization algorithm, particle swarm optimization, sparrow search algorithm to verify the response effect of the cascaded controller. A cascaded double PID controller is also built for comparison to verify the effectiveness of fractional order PID sliding mode controller. The results show that the cascaded fractional order PID sliding mode controller optimized by dung beetle optimizer is better than another cascaded double PID controller, produces the minimum torque ripple, and has better speed response and stability. The torque ripple of motor is suppressed. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

A two-axis stabilizing gimbal is a device that ensures a sensor is working properly on a moving platform. When classical mechanics (Newton–Euler and Lagrange) is employed to model a two-axis stable gimbal, its limitations can complicate the modeling process. To address this issue, a method for establishing a dynamic model for a two-axis stabilizing platform based on the Kane method is proposed in this paper. The Kane method offers the advantage of a simple model structure and computational efficiency. Initially, utilizing a generalized coordinate system, expressions of the generalized velocities, deflection velocities and angular velocities are derived. Subsequently, the generalized active forces and inertial forces acting on the two-axis stabilized gimbal are analyzed. Finally, by combining force and velocity with the Kane equation, the dynamic model of the two-axis stable platform is obtained, demonstrating the validity of the Kane method for establishing the two-axis stable platform model. To ensure the pointing accuracy stability of the two-axis stabilizing platform, a Novel Particle Swarm Optimization Proportion Integration Differentiation (NPSO-PID) controller is designed using the PSO algorithm. It is then simulated in MATLAB/Simulink and compared with a classical PID controller. Simulation results demonstrate that NPSO-PID exhibits superior object tracking performance compared to classical PID controllers and better optimization of control parameters compared to traditional PSO-PID controllers. [ABSTRACT FROM AUTHOR]

The transverse leaf spring (TLS) suspensions are a promising option for van vehicles due to their high load-carrying capacity and excellent handling stability. However, its ride comfort remains a major challenge. This paper investigates and compares the effects of semi-active and active control strategies to enhance the ride comfort of TLS suspensions. Firstly, a four-degree-of-freedom (4-DOF) half-car model and a multi-body dynamics (MBD) model of the TLS suspensions are established. The MBD model has higher accuracy and can describe the medium and high frequency characteristics of the TLS suspensions, such as the suspension offset frequency and the frequency response function of the body vertical acceleration (BVA). Therefore, based on the MBD half-car model with TLS suspensions, this paper proposes an optimal fuzzy PID active control strategy considering the left and right suspension coupling. The optimization objectives are the BVA, the left and right suspensions dynamic deflection, and the left and right wheels dynamic displacement. The integral absolute error is used as the evaluation criterion. The left and right fuzzy PID controllers' parameters are obtained through particle swarm optimization. Simulation results demonstrate that the particle swarm optimization fuzzy PID active control strategy effectively controls the low-frequency vibration of the TLS suspensions and suppresses the medium- and high-frequency vibration characteristics compared with the traditional skyhook semi-active control strategy. This technology provides a reference for improving the ride comfort of the TLS suspensions. [ABSTRACT FROM AUTHOR]

This paper explores the advantages of the Two Degree of Freedom Proportional-Integral-Derivative (2DOF_PID) controller in tracking the reference signal and rejecting the disturbance signal at the same time. Three types of metaheuristic optimization algorithms are employed for tuning the controller's parameters which are Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA). These three algorithms have in common that they combine the exploratory concept (global search) and the exploitative concept (local search) in order to reach the optimal global solution. The effectiveness of these algorithms was taken advantage of to improve the performance of the control system that contains the controller. Second and third order plants were adopted for the purpose of testing, evaluating, and comparing the performance of the control system. This aim was accomplished by using each of the optimization algorithms for each plant. The simulation results showed the superiority of the DA in terms of obtaining the lowest value of the Integral Absolute Error (IAE) as the cost function. [ABSTRACT FROM AUTHOR]

Drilling operation has a direct impact on the quality of the production. Insufficiently controlling the cutting force during the drilling process leads to the risk of early drill failure. Typically, the selection of the drilling parameters is determined based on machining-data handbook where the experience and skill of the operator are required. This paper presents an optimal framework to control the cutting force of the drilling process. A mathematical model that captures complex drilling dynamics between cutting force and feed rate based on system identification is used. Then, a Proportional-Integral-Derivative (PID) controller is proposed to control the cutting force. Taking advantage of up-to-date swam-based optimization technique, an Enhance Bat Algorithm (EBA) approach is used to tune the design variables of the PID controller based on the Integral Absolute Error (IAE) criterion. The results are compared with another two swam optimization, the Particle Swarm Optimization (PSO) and the Whale Optimization Algorithm (WOA). The comparison reveals that EBA can give better results in terms of improving time domain specifications and reducing error performance indices. [ABSTRACT FROM AUTHOR]

This paper presents Load Frequency Control (LFC) strategy for Microgrid (MG) systems, proposing a strategy based on a PID optimized controller using computational intelligence (CI) technique. The MG system, relying solely on Renewable Energy Sources(RES) is modeled in SIMULINK. A novel multi-objective function comprising of Weighted Integral Square Error, WISE and Weighted Integral Absolute Square Error WIASE (WISE+WIASE) is proposed to enhance system performance. Using Particle Swarm Optimization (PSO) and Accelerated Particle Swarm Optimization (APSO), the Proportional, Integral and Derivative controller parameters are tuned. Simulation results revealed the superiority of the PSO-PID with the least steady state error of 4.1560e-07 and overshoot 0.0611 respectively over the APSO-PID with steady state error value of 4.8144e-07 and overshoot of 0.1334 respectively. PSO-PID also outperformed APSO-PID across other indices (ITAE, IAE, ITSE and ISE) making the PSO-PID to excel over the APSO-PID in terms of steady state error and overshoot. The controllers proved to be robust in various conditions with the PSO-PID controller exhibiting more robustness and stability than the APSO-PID controller. Comparing PSO and APSO algorithms, PSO outperformed APSO in terms of robustness because for five number of runs in both algorithms, the PSO algorithm presents a more quality and consistent results and also display a better convergence than the APSO algorithm. Conversely, APSO surpasses PSO in fast computational time and convergence speed. These findings underscore the effectiveness of the proposed LFC strategy for RES based Microgrid systems, providing valuable insights into comparative performance of PSO and APSO algorithms. [ABSTRACT FROM AUTHOR]

Featured Application: This study is mainly applied to the MRTD tester and improves the four-bar target to enhance the detection accuracy of the MRTD tester. Additionally, MRTD is an important parameter for infrared thermal imaging cameras, contributing to improving their accuracy. Furthermore, the dung beetle optimized fuzzy PID parameters can be applied to most control systems, enabling stable tracking of abrupt commands and compensating for the deficiencies of traditional PID control. With the widespread application of infrared thermal imagers in various fields, the demand for thermal imagers and their performance parameter testing equipment has increased significantly. There are particularly high demands on the detection accuracy of minimum resolvable temperature difference (MRTD) testers. Traditional MRTD testers have an issue with the four-bar target temperatures being easily affected by the external environment, resulting in non-uniform temperatures and imprecise detection results. This paper proposes an improvement to the four-bar targets by making them temperature-controllable. Temperature is controlled by installing thermoelectric coolers (TECs) and thin-film platinum resistors at the center and periphery of the four-bar targets with different spatial frequencies. The dung beetle algorithm is used to optimize fuzzy PID parameters to regulate the TEC's heating and cooling, improving the overall temperature uniformity of the four-bar targets. Temperature simulations of the four-bar targets were conducted on the COMSOL platform, with the control part simulated on the Simulink platform. The simulation results show that, compared to traditional PID, the fuzzy PID controller reduces overshoot by approximately 3.6%, although the system still exhibits mild oscillations. The fuzzy PID controller optimized by the dung beetle optimization (DBO) algorithm, in comparison to standard fuzzy PID, reduces the settling time by about 40 s and lowers overshoot by around 7%, with oscillations in the system nearly disappearing. Comparing the fuzzy PID optimized by the particle swarm optimization (PSO) algorithm with the fuzzy PID optimized by the DBO algorithm, the DBO-based controller shows shorter rise and settling times, further illustrating the superiority of the fuzzy PID control optimized by the dung beetle algorithm. This provides a theoretical foundation for improving the accuracy of MRTD detector measurements. Finally, experimental verification was carried out. The experimental results indicate that DBO (drosophila-based optimization) has significant advantages, and its optimized results are closer to the actual values. [ABSTRACT FROM AUTHOR]

In this study, the balance control of a Brushless Direct Current Motor (BLDCM) driven Two-Rotor UAV (2R-UAV) was carried out. First, a MATLAB/Simulink model of the balance system of the 2R-UAV was built. Afterwards, classical and 2-DOF PID, and proposed Adaptive Fuzzy (AF) 2-DOF PID control structures were created on the STM32F4 microprocessor for both balance angle of the system and speed control of the BLDCMs. Classical and 2-DOF PID controller parameters were determined via Particle Swarm Optimization (PSO), a technique that is commonly used in control applications. For the balance control of the 2R-UAV, a Co-Simulation structure was created using the STM32F4 microprocessor and MATLAB/Simulink, and the performances of classical and 2-DOF PID, and AF 2-DOF PID controllers were examined comparatively. Upon examining the comparison results, it was found that the classical and 2-DOF PID, and AF 2-DOF PID stably controlled the balance of the 2R-UAV. The AF 2-DOF PID controller, proposed in this research, performed better than the classical and 2-DOF PID, especially under variable operating conditions. [ABSTRACT FROM AUTHOR]

Hysteresis nonlinearity widely exists in the piezoelectric actuator (PEA), and the hysteresis nonlinearity has strong dynamic characteristics that lead to deterioration of tracking performance. To decrease the positioning error caused by hysteresis nonlinearity, a generalized Bouc-Wen (GBW) hysteresis model and its compensation method are proposed in this paper. First, based on the Bouc-Wen hysteresis model, two asymmetric terms and a second-order IIR filter are applied to describe the asymmetric hysteresis and high-frequency phase lag characteristics of PEA. Then, the model parameters with strong relevance to frequency variation are modified as frequency-dependent parameters. Meanwhile, based on the particle swarm optimization (PSO) algorithm, a novel parameter identification algorithm is designed for identifying the parameters of GBW hysteresis model. Then, an inverse feedforward controller is constructed based on the GBW hysteresis model, and a composite compensation control algorithm combining PID controller and repetitive controller is developed to reduce the unmodeled dynamics errors and unknown external disturbances. Finally, the comparison experiment results show that the accuracy and performance of the GBW model proposed in this paper are much better than the classical Bouc-Wen (CBW) model and the enhanced Bouc-Wen (EBW) model, and the developed compensation controller has excellent control performance and robustness. [ABSTRACT FROM AUTHOR]

Tokamak reactors’ performance is inherently tied to the precise control of plasma shape, position, and current while staying within the operational constraints, specifically managing the control signals and power supply voltages. Furthermore, many tokamak models exhibit strong coupling between control variables, necessitating the use of Multiple-Input Multiple-Output (MIMO) decoupling controllers. The primary control objectives include achieving high tracking and decoupling performance within these operational constraints, along with a focus on robustness. Fractional Order Proportional-Integral-Derivative (FOPID) controllers offer an advantage due to their additional degrees of freedom, which contribute to improved performance, robustness, and flexibility compared to conventional control methods. In light of these advantages, we have designed Optimal FOPID, and also two Integer Order optimal PID (IOPID), controllers for plasma current and horizontal position control in the IR-T1 tokamak, which were optimized using Particle Swarm Optimization to minimize objective functions. Our results have shown that the Integral of Time-weighted Absolute Error (ITAE) criterion exhibits the best tracking and robustness behavior in this context. Examining an actual experimental discharge in this tokamak as a test case, validate that the OFOPID control scheme is effective at maintaining stability when faced with disturbances and fast variations in the plasma parameters. [ABSTRACT FROM AUTHOR]

Tuning a PID controller is a crucial task in control engineering to achieve optimal performance of the system. However, manual tuning of PID parameters can be inaccurate and difficult without extensive experience. One approach to tuning the PID controller is by utilizing heuristic algorithms. These algorithms are based on natural phenomena and can efficiently search for the optimal set of PID parameters. Therefore, utilizing met heuristic algorithms for tuning PID controllers can significantly improve the system's performance and reduce costs associated with manual tuning. In this paper, a new method for tuning parameters of the PID controller of DC motors using a hybrid adaptive PID_GTO predictive model based on the artificial gorilla troop optimizer algorithm (GTO) is proposed. The empirical results are compared based on four types of error indicator functions: integral time squared error (ITSE), integral time absolute error (ITAE), integral absolute error (IAE), and integral squared error (ISE), as well as with other previously published techniques in the literature, such as the Ziegler-Nichols and PSO Optimizer algorithms. The empirical results show that this method outperforms other techniques in improving steady-state error, stability, overshoot, rising time, and settling time of the DC motor. [ABSTRACT FROM AUTHOR]

This article presents a method of designing an optimal Proportional-Integral-Derivative (PID) controller for Automatic Generation Control (AGC) of a two-area interconnected thermal system. In addition, an application of Salp Swarm Algirithm (SSA) in order to design PID controllers is proposed. A method inspired by salp foraging behavior. The journey of the Salp group in the sea, by random the population of the salp and separated into leader salp and follower salp. The leader moves towards the food source and the rest of the followers follow the salp positioned in front of themselves. The parameters of PID were achieved by trial and error methods by designers. As an interconneted thermal power system with a Governor Deadband (GDB) is nonlinear and dynamic system, the Integral Squared Error (ISE) criterion has been used as objective function to find optimum controller gain. In order to solve this problem, SSA is proposed to concurrently tune PID gains of the PID controllers to minimize frequency deviations and tie-line power deviations by simulation the interconnected thermal power system. Compare of tuning PID controller from each optimization technique SSA and PSO. Simulation results clearly show that the performance of the system after taking SLP into the interconnected thermal power system that tuning by SSA and PSO and the robustness of the optimal PID controllers are superior to the conventional PID controllers in terms of settling time (-10.46 % in area 1, +0.87 % in area 2 and +0.57 % in power tie-line), overshoot (-10.61 % in area 1 and -10.87 % in area 2) that obtain by (SSA) method less value than the value that obtain by (PSO) method and the power tie-line. Integral Absolute Error (IAE) (-0.38 %) that obtain by (SSA) few errors. Comparisons to the tuned with Particle Swarm Optimization (PSO) approaches is also included. [ABSTRACT FROM AUTHOR]

This article introduces hybrid Arithmetic Optimization Algorithm (AOA) and Local Unimodal Sampling (hAOA-LUS)-based fractional order (FO) proportional derivative (PD) cascaded with one plus proportional integral (1+PI) controller for automatic generation control of power system with renewable energy sources (RES) and electric vehicle (EV). The control-area 1 has thermal, hydro, gas, and wind power generators and the same true for control area 2, which uses thermal, hydro, gas, and solar energy sources. Initially, Proportional-Integral-Derivative (PID) controllers are taken into consideration and it has been demonstrated that hAOA-LUS outperforms as compared to the AOA, Particle Swarm Optimization, and Generic Algorithm (GA). The assessing of overshoots, undershoots, and different integral errors of frequency and tie-line power deviations after step load perturbations in each area allows for performance comparison of the proposed power system. In the next stage, EVs are considered in each area and the controller parameters are optimized by hAOA-LUS techniques in the presence of RES and EV. A comparative analysis is carried out by hAOA-LUS-tuned FO PD(1+PI) controller with PID as well integer ordered PD(1+PI) for various cases so as to validate the superiority of the anticipated controller. The results from MATLAB and OPAL-RT are compared in order to verify the authenticate feasibility of method. [ABSTRACT FROM AUTHOR]

The two different motion behaviors of seahorses in nature served as inspiration for the seahorse optimization (SHO) method, which is a new metaheuristic swarm intelligence-based approach to solving fundamental engineering problems. In this study, the proportional-integral-derivative (PID) parameters for the simplified speed control of the manipulator joint using squirrel-cage induction motors were calculated with the SHO algorithm. As a result of these calculations, Kp, Ki, and Kd values were obtained as 0.0430, 0.00474, and 0.03254, respectively. Then, the time for the squirrel-cage motor to reach 50 rpm (revolutions per minute) and 90 rpm was calculated with the help of SHO. In PID+SHO operation, the squirrel-cage electric motor reached 50 rpm in 3 seconds and 90 rpm in 8 seconds. In this study, in which the SHO optimization method was used, it was calculated that the acceleration of the squirrel-cage motor and reaching the desired value gave 50% better results compared to the particle swarm optimization algorithm. [ABSTRACT FROM AUTHOR]

This work proposes an intelligent method that uses an adaptive auto-tuned proportional-integral-derivative (PID) controller to track and regulate the blood glucose level of diabetic patients. The suggested controller seeks to provide the optimal insulin control action, which is in charge of swiftly, precisely, and accurately managing the blood glucose level. To train this controller, two meta-heuristic techniques are employed. The first technique is the particle swarm optimization (PSO), which has been widely used in both data estimation and training because of its quick computing speed, while the second one is an intrusion detection technique called grey wolf optimization (GWO), which was created to categorize data and effectively find multiple intrusions. The mean square error performance index is used in the two distinct meta-heuristic algorithm types to determine and optimize the optimal or nearly optimal gain parameters of the adaptive PID controller. The results of the MATLAB simulations for three different patients demonstrated the efficacy and resilience of the proposed control algorithm in tracking the dynamic behaviour of the diabetic patients' blood glucose levels by minimizing overshoot in the transient state to zero value, maintaining the steady-state blood glucose level in the normal physiological level of (60-120) mg/dl. These characteristics were particularly evident when we added a meal as a disturbance effect. Moreover, the comparison results showed that the proposed PID-GWO and PID-PSO algorithms enhanced the time (96 and 88) minutes to reach the blood glucose level at a normal physiological level by 4% and 12%, respectively, when compared to the fractional order PID and fuzzy logic control algorithms that the blood glucose level reached at a normal physiological level at 100 minutes and improved the time by 20% and 27%, respectively, when compared to the type-2 fuzzy control algorithm that the blood glucose level reached at a normal level at 120 minutes. In particular, the blood glucose level was kept at the desired normal physiological level without any oscillation. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks have an event-driven nature that includes a colossal count of sensors to generate unpredictable network traffic that causes network congestion Due to immoderate congestion packets remain in the queue for a longer period. This incites increased packet delay and packet losses. Existing congestion control strategies are either inelastic or have limited elasticity. Numerous elastic approaches, such as Cuckoo Fuzzy PID (CFPID), PSO (Particle Swarm Optimization)-neural PID, and PSO Fuzzy PID have been introduced using meta-heuristics and evolutionary algorithms; nevertheless, these are slow and prematurely converge, indicating an NP-hard problem. Moreover, Multi-objective Proportional Integral Derivative (MPID) is articulated as a multi-objective optimization problem, which is followed by many researchers but suffers from overshoot defects. To address these issues, the hybrid controller is proposed using the Non-dominated Sorting Genetic Algorithm III and MPID called N3-MPID to optimize the data transmission rate for the source node. The four indirect objectives that are Integral Square Error, Integral Absolute Error, Integral of Time multiplied Absolute Error, and Integral of Time multiplied Square Error are taken to design a novel optimal MPID fitness function. To merge these objectives into a single goal, an equally weighted procedure is adopted that is efficient and well-distributed. The proposed technique is simulated using a Network Simulator and is compared with CFPID using principal metrics. Simulation results depict that the N3-MPID outflanks existing algorithms by an 11.76% increase in packet delivery ratio while minimizing packet loss, average delay, and deviation of queue length by 43.16%, 20.55%, and 15.05%, respectively. [ABSTRACT FROM AUTHOR]

Cumene is a precursor for producing many organic chemicals and is thinner in paints and lacquers. Its production process involves one of the large-scale manufacturing processes with complex kinetics. Different classical control strategies have been implemented and compared in this process for the cumene reactor. As a system with large degrees of freedom, a novel approach for extracting the state space model from the COMSOL Multiphysics implementation of the system is adopted here. Internal Modern Control (IMC) based PI and PID controllers are derived for the system. The system is reduced to the FOPDT and SOPDT model structure to derive the controller setting using Skogestad half rules. The integral time is modified for excellent set point tracking and faster disturbance rejection. From the analysis, it can be stated that the PI controller suits more for this specific process. The particle swarm optimization (PSO) algorithm, an evolutionary computation technique, is also used to tune the PI settings. The PI controllers with IMC, Zeigler Nichols, and PSO tuning are compared, and it can be concluded that the PSO PI controller settles at 45 s without any oscillations and settles down faster for the disturbance of magnitude 0.5 applied at t = 800 s. However, it is computationally intensive compared to other controller strategies. [ABSTRACT FROM AUTHOR]

The PID controller is the most widely used controller, and the performance of each controller depends on its three gain parameters. The genetic algorithm (GA) is an optimization process that involves three types of operators: selection, crossover, and mutation. Inspired by natural evolution, a GA searches for the optimum solution automatically from a collection of potential solutions. The GA was employed for the optimization of PID controller parameters in this paper; this algorithm evolves from generation to generation until the stopping criterion is met or the optimal solution is found. The traditional GA has several shortcomings, such as the great influence of the best individual on the whole population, poor global search ability, and premature convergence; therefore, an improved cellular genetic algorithm (CGA) was proposed whose genetic operators occur between the individual and its neighbors. CGA avoids the adverse influence of the best individual and the possibility of falling into a local optimum. The simulation results show that CGA outperforms traditional GA. CGA is more efficient than GA in global search activities, and its optimum solution is better than that of GA. [ABSTRACT FROM AUTHOR]

This paper proposes an advanced Load Frequency Contol (LFC) strategy, using a hybrid adaptive particle swarm-spiral dynamic optimization algorithm (HAPSSDOA). The optimization algorithms iteratively adjust the PID gains based on the evaluation of the cost function and eventually find the optimal values that minimize the cost function and result in a satisfactory frequency value of 50Hz. The P-I-D parameters for the APSO, SDA, and HAPSSDOA algorithms were 3.5112, 2.9691 and 1.1972; 2.3712, 2.8479 and 0.9827; 2.3519, 1.7989 and 0.8864 respectively. The system’s performance showed the rise time, settling time, percentage overshoot and ITAE of 0.28s, 2.33s, 42.10%, and 0.1076 for APSO; 0.324s, 3.67s, 38.4%, and 0.3590 for SDA; 0.36S, 2.2S, 33.5%, and 0.2430 for HAPSSDOA respectively. The LFC strategy using the hybrid HAPSSDOA achieved better performance, improved robustness, and the best response. The optimized PID controllers’ values brought more stability to the designed microgrid network, by ensuring that the frequency remained at 50Hz. [ABSTRACT FROM AUTHOR]

In the proposed research article, the grey wolf optimization (GWO) technique is utilized to optimize the proportional (P) integral (I) derivative (D) (PID) controller/regulator gain parameters in three-area grid-connected power networks. The interconnected power plant covers thermal plants, hydro plants, and nuclear power plants. The proposed controller is used as a secondary controller in the power system to perform load frequency control (LFC). Under unforeseen load conditions, the system frequency deviates from the norm. To control and stabilize this oscillation, the LFC system is used. During the investigation, a step load perturbation of one percent (SLP 1%) is applied for the analysis of the thermal power plant. The response of the suggested optimization technique-designed regulator performance is equated with the genetic algorithm (GA)-tuned, particle swarm optimization (PSO)-tuned, and ant colony optimization (ACO) technique-tuned PID regulator response. The performance response is evidence that the GWO-based PID regulator provides a regulated response with minimal time-domain specification parameters (settling time, peak shoots) over other tuning methods. The effectiveness and robustness of the improved response of the suggested technique-optimized controller are verified with various load values (1%, 2%, and 10% SLP) and nominal parameter (R, Tp, and Tij) variations (± 25% & ± 50%) from its nominal value. [ABSTRACT FROM AUTHOR]

This paper proposes a particle swarm optimization (PSO) tuned novel proportional integral derivative (PID) like neural network (PSO-PID-NN), to control the temperature of a nonlinear jacketed continuous stirred tank reactor (CSTR). The nonlinear continuous stirred tank reactor (CSTR) plant is one of the most popular reactors in the chemical industry. The proposed structure is elegant in design, having only three neurons in the hidden layer and a single output neuron. The three weights in the neural network's output layer represent the PID controller's proportional, integral, and derivative gains. The suggested approach uses the PSO method to optimize the output layer weights, which corresponds to the PID gains. Mean square error is used as an objective function to optimize the weights. The performance of the proposed PSO-PID-NN controller is tested by comparing the time domain specifications of the output response, against the conventional Zeigler Nichols tuned PID controller and the back propagation-based NN-PID controller (BP-NN-PID). The overshoot in the proposed controller is 23.13%, while it is 26.33% in BP-NN-PID, and 44.13% in Zeigler Nichols tuned PID controller. In addition, the rise time is 0.1283 s, while it is 0.2727 s in the BP-NN-PID controller and 0.2813 s in Zeigler Nichols tuned PID controller. The proposed controller is also tested for disturbance rejection, it was found to be more efficient in rejecting disturbance signals as compared to BP-NN-PID and ZN-tuned PID controllers. [ABSTRACT FROM AUTHOR]

Designing effective PID controllers for gimbal payload systems (GPS) in naval applications poses inherent challenges due to the dynamic nature of these systems. The gimbal system requires precise control to stabilize its line of sight (LOS) for accurate target tracking in electro-optical systems. Conventional PID controllers often struggle to address the complex dynamics and nonlinearities inherent in gimbal systems, resulting in suboptimal performance. Therefore, in this study, we introduce a novel control design framework for the gimbal payload system (GPS) employed in naval applications. A more effective PID controller is developed by utilizing the hunting mechanism of the antlion optimization technique. This controller precisely manages and stabilizes the LOS of the GPS, ensuring seamless target tracking in electro-optical systems. Furthermore, we assess the robustness of the proposed controller by varying system parameters, including moment of inertia (± 20%) and motor resistance (+ 10%). The results demonstrate robust target tracking even in the presence of changes within the system. Additionally, the gimbal system exhibits zero overshoot, and the achieved high values of gain and phase margins outperform other techniques such as genetic algorithm, particle swarm optimization, and classical methods. [ABSTRACT FROM AUTHOR]

This paper investigates the frequency control strategy of the Virtual Power Plant (VPP), with the structure of an independent control area that includes diverse Distributed Energy Resources. The proposed VPP system comprises generating units such as Solar PV, Archimedes Wave Energy Conversion, Biodiesel driven Generator, and Plug-in Hybrid Electric Vehicle, which are interconnected to conventional thermal power plant. Centralized control strategy is adopted for frequency control studies, by considering the communication time delays. Fractional order Proportional Integral Derivative (FOPID), Proportional Integral Derivative (PID), and Proportional Integral (PI) controllers are employed to perform the control action on generating units to perform the frequency control. Tuning of the controllers is done using optimization algorithms such as Particle Swarm Optimization, Firefly Algorithm, Grasshopper Optimization Algorithm, Ant Lion Optimizer, Sine Cosine Algorithm, and Salp Swarm Algorithm (SSA). By comparative analysis, SSA algorithm is proved to outperform the other algorithms in terms of minimizing the objective function of Integral of Squared Error of the deviations in system frequencies and tie-line power. The system is also investigated under various scenarios of generation and load disturbances to study the comparative performance of the FOPID controller over PID and PI controllers. Results prove the superiority of SSA-tuned FOPID controller over PID and PI controllers in terms of providing better system responses. Sensitivity test has been conducted on the system without resetting nominal controller gain values and the proposed SSA-tuned FOPID controller is proved to be robust against the uncertainties in the load and parameters of our system. [ABSTRACT FROM AUTHOR]

The use of cable-driven parallel robots (CDPRs) has been steadily increasing across various sectors due to their expansive workspaces, impressive payload-to-mass ratios, and cost-effective designs. Controlling these robots, particularly those with substantial actuation redundancy, can present challenges. This research paper proposes the implementation of a fractionalorder proportional-integral-derivative (FOPID) controller to effectively regulate the end-effector of a planar CDPR with four actuation cables. The parameters of the controller are fine-tuned using the particle swarm optimization (PSO) algorithm to ensure optimal performance. The proposed controller's performance is evaluated through two numerical experiments: target tracking and trajectory tracking using a point-to-point approach. Furthermore, a comparative study is conducted to highlight the controller's performance, comparing the proposed FOPID controller with both the classical PID controller and an optimized PID controller. The achieved results demonstrate that the proposed controller exhibits superior performance in terms of tracking accuracy and smoothness of control signals when compared to the other controllers under investigation. As a result, the proposed controller design represents a substantial advancement in control performance and can be regarded as a promising control strategy for CDPRs. [ABSTRACT FROM AUTHOR]

The unpredictability of solar and wind energy sources affects contemporary power networks and adds frequency variations. An appropriate intelligent controller is necessary to balance electricity between generation and demand. As a result, in this research, we present a sine-cosine adaptive improved equilibrium optimization (SCaIEO) method tuned to Adaptive Type 2 Fuzzy PID Controller (AT2FPID) for frequency management of cutting-edge power systems. The efficiency of the SCaIEO approach is assessed by comparing it to the original equilibrium optimization (EO) and other comparable algorithms for the test function. Moreover, engineering applications of the SCaIEO technique are carried out by constructing an AT2FPID controller to manage the frequency of power systems that include renewable energy and dispersed sources. First, we show that SCaIEO outperforms EO, Particle Swarm Optimization, Genetic Algorithm, Moth Flame Optimization, and Gravitational Search Algorithm in the PID controller (Gravity Search Algorithm). The AT2FPID is next evaluated, and the SCaIEO AT2FPID controller's dominance is proven by equating the outcome to the original, PID Type 1 Fuzzy PID, Type 2 Fuzzy PID controller. [ABSTRACT FROM AUTHOR]

The vibration of piping systems is one of the most important causes of accelerated equipment wear and reduced work efficiency and safety. In this study, an active vibration control method based on a fractional-order proportional–integral–derivative (PID) controller was proposed to suppress pipeline vibration and reduce pipeline damage. First, a mathematical model of the distributed piping system was established using the finite element analysis method, and the characteristics of the distributed piping system were studied effectively. Further, the time-frequency domain parameter identification method was used to realise the system identification of the cross-point vibration transfer function between the brake and sensor, and the particle swarm optimisation algorithm was utilised to further optimise the transfer function parameters to improve the system identification accuracy. Therefore, a fractional-order PID controller was designed using the D-decomposition method, and the optimal controller parameters were obtained. The experimental and numerical simulation results show that the improved system identification algorithm can significantly improve modelling accuracy. In addition, the designed fractional-order PID controller can effectively reduce the system's overshoot, oscillation time, and adjustment time, thereby reducing the vibration response of piping systems. [ABSTRACT FROM AUTHOR]

Gantry-type dual-axis platforms can be used to move heavy loads or perform precision CNC work. Such gantry systems drive a single axis with two linear motors, and under heavy loads, a high driving force is required. This can generate a pulling force between the drive shafts in the coupling mechanism. In these situations, when a synchronization error becomes too large, mechanisms can become deformed or damaged, leading to damaged equipment, or in industrial settings, an additional power consumption. Effectively and accurately acquiring the synchronized movement of the platform is important to reduce energy consumption and optimize the system. In this study, a fractional-order fuzzy PID controller (FOFPID) using Oustaloup's recursive filter is used to control a synchronous X–Y gantry-type platform. The optimized controller parameters are obtained by the measurement of control errors in a simulated environment. Four optimization methods are tested and compared: particle swarm optimization, invasive weed optimization, a gray wolf optimizer, and biogeography-based optimization. The systems were tested and compared in order to optimize the control parameters. Each of the four algorithms is simulated on four contour shapes: a circle, bow, heart, and star. The simulations and control scheme of the experiments are implemented using MATLAB, and the reference paths were planned using non-uniform rational B-splines (NURBS). After running the simulations to determine the optimal control parameters, each set of acquired control parameters is also tested and compared in the experiments and the results are recorded. Both the simulations and experiments show good results, and the tracking of the X–Y platform showed improved performance. Two performance indices are used to determine and validate the relative performance of the models and results. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective optimization analysis to improve the controller tuning of three-element control loop for the best fit to both its servo and regulatory control objectives during the process operations. The existing Proportional-Integral-Derivative (PID) controller tuning for the three-element control loop is challenging because the best setting of each controller is obtained during the concurrent analysis, but all controller settings affect the control performance of other control loops and the output responses. Furthermore, this paper highlights the determination of Upper Limit (UL) and Lower Limit (LL) bounds by using the necessity criterion of Routh-Hurwitz stability analysis. The Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used as the optimization algorithms to improve the control performances. Both optimization analysis are operated by using a developed Graphical User Interface (GUI) via MATLAB software. At the same time, the optimized PID controller settings are applied to the steam boiler drum function of the LOOP-PRO simulator. Both GA and PSO outperform the manual tuning for the three-element loop. Among them, GA performs better than PSO even though both methods are capable of suggesting highly satisfactory performances. [ABSTRACT FROM AUTHOR]

In this paper, a method for the design of an optimal Sugeno model (FLC-Sugeno) fuzzy logic controller for the active suspension system of quarter-vehicle models is presented. The parameters of the FLC-Sugeno controller are optimally searched, using the Particle Swarm Optimization (PSO) algorithm. The 16 optimized parameters include 3 parameters for adjusting the domain of the input state variables and control variables at the controller's output, 4 fuzzy set adjustment numbers of the linguistic variables, and 9 parameters as the fuzzy rule weights of the rule system control. To compare and evaluate the effectiveness of the optimal FLC-Sugeno controller, an optimal PID controller using PSO is also implemented. Simulation results of the active damping system with the controllers when affected by the same type and standard road surface excitation show that the FLC-Sugeno controller is optimal for the quick ending of the oscillations of vehicle body displacement. The result shows that the proposed controlling scheme can be extended and applied to more complex active damping system models. [ABSTRACT FROM AUTHOR]

Ship course-keeping control is of great significance to both navigation efficiency and safety. Nevertheless, the complex navigational conditions, unknown time-varying environmental disturbances, and complex dynamic characteristics of ships pose great difficulties for ship course-keeping. Thus, a PSO-based predictive PID-backstepping (P-PB) controller is proposed in this paper to realize the efficient and rapid course-keeping of ships. The proposed controller takes the ship's target course, current course, yawing speed, as well as predictive motion parameters into consideration. In the design of the proposed controller, the PID controller is improved by introducing predictive control. Then, the improved controller is combined with a backstepping controller to balance the efficiency and stability of the control. Subsequently, the parameters in the proposed course-keeping controller are optimized by utilizing Particle Swarm Optimization (PSO), which can adaptively adjust the value of parameters in various scenarios, and thus further increase its efficiency. Finally, the improved controller is validated by carrying out simulation tests in various scenarios. The results show that it improves the course-keeping error and time-response specification by 4.19% and 9.71% on average, respectively, which can efficiently achieve the course-keeping of ships under various scenarios. [ABSTRACT FROM AUTHOR]

Electrical power networks consist of numerous energy control zones connected by tie-lines, with the addition of nonconventional sources resulting in considerable variations in tie-line power and frequency. Under these circumstances, a load frequency control (LFC) loop gives constancy and security to interconnected power systems (IPSs) by supplying all consumers with high-quality power at a nominal frequency and tie-line power change. This article proposes employing a proportional–integral–derivative (PID) controller to effectively control the frequency in a one-area multi-source power network comprising thermal, solar, wind, and fuel cells and in a thermal two-area tie-line IPS. The particle swarm optimization (PSO) technique was utilized to tune the PID controller parameters, with the integral time absolute error being utilized as an objective function. The efficacy and stability of the PSO-PID controller methodology were further tested in various scenarios for proposed networks. The frequency fluctuations associated with the one-area multi-source power source and with the two-area tie-line IPS's area 1 and area 2 frequency variations were 59.98 Hz, 59.81 Hz, and 60 Hz, respectively, and, in all other investigated scenarios, they were less than that of the traditional PID controller. The results clearly show that, in terms of frequency responses, the PSO-PID controller performs better than the conventional PID controller. [ABSTRACT FROM AUTHOR]

Continuum robots are complex structures that require sophisticated modeling and control methods to achieve accurate position and motion tracking along desired trajectories. They are highly coupled, nonlinear systems with multiple degrees of freedom that pose a significant challenge for conventional approaches. In this paper, we propose a system dynamic model based on the Euler–Lagrange formulation with the assumption of piecewise constant curvature (PCC), where we accounts for the elasticity and gravity effects of the continuum robot. We also develop and apply a particle swarm optimization (PSO) algorithm to optimize the parameters of our developed controllers: an inverse dynamic proportional integral derivative (PID) controller and an inverse dynamic fuzzy logic controller (FLC), where we use the integral time of absolute error (ITAE) as the objective function for the PSO algorithm. We validate our proposed model and optimized controllers through different designed trajectories, simulated using our developed unique animated MATLAB simulation. The results show that the PSO-PID controller improves the rise time, overshoot percentage, and settling time by 16.3%, 31.1%, and 64.9%, respectively, compared to the PID controller without PSO. The PSO-FLC controller shows the best performance among all controllers, with a settling time of 0.7 s and a rise time of 0.4 s, leading to the highest level of precision in trajectory tracking. The ITAE error for the PSO-FLC controller is 11.4% and 29.9% lower than that of the PSO-PID and FLC controllers, respectively. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

In this work, a Particle Swarm-Based optimization algorithm is proposed to optimize the PID controller used in the Positive Output Re-Lift Luo converter for the Electric Vehicle application. The novelty of the work lies in developing an improved PID controller to explore the reliability of the re-lift Luo converter. In addition, the optimization improves the effectiveness of the closed-loop system that possesses high voltage gain with esteemed power density and efficiency. With the Particle Swarm Optimization control algorithm, $ {K_p} $ K p , $ {K_i} $ K i , and $ {K_d} $ K d values are tuned in such a way to obtain the appropriate steady-state condition with no peak overshoot or undershoot, much lesser settling time, no steady-state error, and improved dynamic performances. To analyze the system stability and to obtain better results, reduced-order state-space modeling of the Re-lift Luo converter is adopted, which offers high computational speed and accuracy. Initially, a PID controller is designed using the Zeigler Nicholas method, and the results are compared with the genetic algorithm and Particle Swarm Optimization algorithm. The time-domain analysis has been carried out to analyze and compare the converter's performance. The simulation results were verified experimentally, and the results prove the robustness of the proposed converter with a PID controller optimized using the Particle Swarm Optimization algorithm. [ABSTRACT FROM AUTHOR]

Water quality is an issue of global importance in environmental research. The productivity, safety, and distribution of fresh and pure water to the population are priorities for every nation worldwide. Currently, reverse osmosis (RO) desalination plants are one of the best methods accepted worldwide due to their simplicity and high performance. This paper focuses on the design of a PID controller for the Doha plant RO using the class topper optimization (CTO) algorithm. In addition, a reduced-order model is created using the same algorithm for ease of analysis. The plant RO is decoupled, and then the PID controller is designed for two non-conducting loops. The effectiveness of the proposed method is shown by comparing its response characteristics with other state-of-the-art optimization techniques, namely artificial bee colony, differential evolution, and particle swarm optimization. The simulation comparison shows the better performance of the CTO algorithm in tuning the PID controllers. [ABSTRACT FROM AUTHOR]

This paper presents the implementation for the first time of a Multi-Particle Swarm Optimization (MPSO) algorithm in the tuning of a PID controller for Power Factor Correction (PFC), applied to a 100W AC-DC boost converter. MPSO algorithm navigates in a search space where each dimension of the space corresponds to the controller constants (Proportional, Integral, Derivative and the Derivative Filter), prioritizing communication over exploration in the algorithm. The controller parameters are randomly initialized in a reduced sector of the space [ K p , K i , K d , K n ], to optimize the search for a PID solution. In the first step, the algorithm is validated using a simulation model in Simulink and Matlab. Subsequently, a final implementation using a real converter is implemented with the PID tuned by MPSO, improving the PFC obtained in previous work. Although previous works have used evolutionary algorithms applied to heuristic optimization to tunning PID controllers, the MPSO algorithm is not usually used for this purpose, particularly to tunning a PID controller in a power electronics system. One advantage of MPSO over the PSO classical algorithm is the search at different points if the vectorial field looks for an optimal solution. PSO presents problems such as getting stuck in a locally optimal solution. The PID controller is trained offline, with the advantage of allowing the risk of damage in the Boost converter for transitory response, increasing the performance of the Power Factor Correction in the converter. This research opens the possibility to use the extended version of the PSO bioinspired algorithm to tune offline controllers to improve the power converter's performance, minimizing the risk presented in the real-time tuning process. [ABSTRACT FROM AUTHOR]

This paper introduces a novel neural network model predictive controller based on Archimedes optimization algorithm to control constrained nonlinear systems, namely robot manipulators. Neural network models of a simple structure are used to accurately predict the system's future outputs. The new Archimedes optimization algorithm is utilized to solve the optimization problem of model predictive control and compute the optimal control action; this algorithm was widely used in different engineering fields due to its good accuracy and convergence speed. The performances of the suggested control algorithm are investigated by simulating a two-degrees-of-freedom robot manipulator. The obtained results are compared with those of various techniques, namely the PID controller, the computed torque controller, and the neural network-based model predictive control using the teaching–learning-based optimization and the particle swarm optimization. To complete the study, the developed controller is implemented on a DSP board and used to control a three-degrees-of-freedom robot manipulator; its results are compared to the neural network-based model predictive control based on the teaching–learning-based optimization and the particle swarm optimization. The simulation and the experimental results demonstrate that the proposed controller provides satisfactory robustness and accuracy, can handle constraints, and can be used to control systems with fast dynamics. [ABSTRACT FROM AUTHOR]