Integral Time Absolute Error Research Articles

Frequency Loop-Shaping and IMC-Based Integer-Order Robust PID Controller Design for Fractional-Order Processes with Time-Delay

It is well accepted that fractional-order calculus yields a more accurate description of complex physical processes compared to integer-order one. So, fractional-order modelling is getting a good attention in the process industry. On the other hand, to meet design goals accurately, control designers prefer fractional-order controllers over integer-order ones due to the availability of more parameters. However, the design of such controllers are complex. More often, the design faces the challenge in real-time implementations, and the problem is circumvented by higher order approximations of fractional-order operators. In the presented work, three methods for integer-order Proportional–Integral–Derivative (PID) controller design are proposed based on Frequency Loop-Shaping (FLS) and Internal Model Control (IMC). To prove the efficacy of the presented control law, simulations are conducted and included along with a performance measure based on ISE (Integral of Square Error), ITAE (Integral of Time Absolute Error) and IAE (Integral of Absolute Error).

Design of novel UPFC based damping controller for solar PV integrated power system using arithmetic optimization algorithm

Abstract Integrating renewable energy sources like solar power into traditional power systems poses challenges. One such challenge is the effect of renewable power plants, which use power electronics, on the grid’s stability. Specifically, these plants can impact small-signal stability by either damping or exacerbating low-frequency oscillations. This paper introduces a novel Unified Power Flow Controller (UPFC) based damping controller specifically designed for Solar Photovoltaic (PV) integrated power systems. It employs an Arithmetic Optimization Algorithm (AOA) to optimize the UPFC damping controller parameters and mitigate low-frequency oscillations in the power system. The objective function minimizes the Integral Time Absolute Error (ITAE) of speed deviations under varying loading conditions. The proposed technique is utilized simultaneously to control the modulation index of series and phase angle of shunt converters of UPFC. The MATLAB/simulation results obtained effectively from the proposed technique which is actualized and identify both detrimental and beneficial impacts of increased PV penetration for small signal stability performance. The study reveals both the small-signal stability of the system and its response to large disturbances that alter the active power balance and frequency stability. The results of the analysis demonstrated with single and multimachine environment by comparing with the other optimizations like PSO, DE, DE-PSO and GWO, the proposed one is effective for damping out the oscillations. The effectiveness of the proposed damping controller is further confirmed through real-time validation using the OPAL-RT setup.

A novel modified Cheetah Optimizer for designing fractional-order PID-LFC placed in multi-interconnected system with renewable generation units

Establishing robust electrical interconnections between nations is a pivotal foundation for significant investments and addressing energy shortfalls in regions grappling with generational challenges. However, developing electrical load disruptions within interconnected systems can lead to substantial variations in frequencies and energy transmission. Load Frequency Control (LFC) is a crucial mechanism to mitigate these disruptions and ensure stable operations in interconnected regions. While meta-heuristics have been employed for LFC design, some techniques face challenges like early convergence and poor accuracy due to a lack of population diversity. In this study, a novel Modified Cheetah Optimizer (mCO) is proposed to optimize the parameters of LFC system, incorporating fractional-order proportional integral derivative (FOPID) controllers within multi-interconnected system with renewable energy integration. The mCO integrates learning-from-experience and random contraction strategies to enhance convergence accuracy and overcome local optima, demonstrating superior efficiency in solving optimization problems. The proposed mCO is evaluated by solving twelve functions from the CEC2022 test suite, showcasing its effectiveness. The optimization problem involves minimizing the Integral Time Absolute Error (ITAE) of the area control error, considering changes in frequencies and exchanged power, with controller parameters λd, kd, ki, kp, and μ to be identified. Two interconnected systems, photovoltaic (PV)-thermal and thermal-wind turbine (WT)-thermal-PV, are assessed under various load disturbances. The mCO is compared with other methods, including Modified Hunger Games Search Optimizer (MHGS), Driving Training-Based Optimizer (DTBO), Grey Wolf Optimizer (GWO), Aquila Optimal Search (AOS), and Cheetah Optimizer (CO). In the case of PV-thermal linked system, the proposed mCO succeeded in mitigating the ITAE by 19.21% compared to the reported MHGS and 8.63% compared to the conventional CO. In the four interconnected systems, the suggested approach reduced the ITAE by 89.21% and 15.26% compared to the reported MHGS and conventional CO, respectively. This confirmed the efficacy of FOPID-LFC, which was designed using the proposed mCO in all examined scenarios.

Improving PID Controller Performance in Nonlinear Oscillatory Automatic Generation Control Systems Using a Multi-objective Marine Predator Algorithm with Enhanced Diversity

Power systems are pivotal in providing sustainable energy across various sectors. However, optimizing their performance to meet modern demands remains a significant challenge. This paper introduces an innovative strategy to improve the optimization of PID controllers within nonlinear oscillatory Automatic Generation Control (AGC) systems, essential for the stability of power systems. Our approach aims to reduce the integrated time squared error, the integrated time absolute error, and the rate of change in deviation, facilitating faster convergence, diminished overshoot, and decreased oscillations. By incorporating the spiral model from the Whale Optimization Algorithm (WOA) into the Multi-Objective Marine Predator Algorithm (MOMPA), our method effectively broadens the diversity of solution sets and finely tunes the balance between exploration and exploitation strategies. Furthermore, the QQSMOMPA framework integrates quasi-oppositional learning and Q-learning to overcome local optima, thereby generating optimal Pareto solutions. When applied to nonlinear AGC systems featuring governor dead zones, the PID controllers optimized by QQSMOMPA not only achieve 14%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} reduction in the frequency settling time but also exhibit robustness against uncertainties in load disturbance inputs.

Maximum power optimization of a direct-drive wind turbine connected to PMSG using multi-objective genetic algorithm

This work aimed to develop and evaluate a maximum power point tracking (MPPT) control system for a wind energy conversion system (WECS) based on a permanent magnet synchronous generator (PMSG). PMSG is commonly used to generate direct-drive and variable-speed wind energy. Initially, the generator and converter on the DC load side are controlled to follow the wind speed reference set by the MPPT algorithm. The paper presents the optimization problem formulation, including the optimization space, constraints, and objectives. The genetic algorithm (GA) is used to extract the maximum power from the WECS in this design improvement. In this study, to control and stabilize the maximum power point (MPP) of the wind turbine, a proportional integral (PI) controller and a GA heuristic approach were utilized. The GA approach was employed to determine the best settings (Kp, Ki) using MATLAB/Simulink with a 12.3 kW PMSG to model and simulate the proposed system. Based on four performance indicators-integrated squared error (ISE), integrated absolute error (IAE), integrated time absolute error (ITAE), and integrated time squared error (ITSE), the GA approach was used to optimize the controller settings. The results of the simulation show that the wind turbine (WT) can effectively track the necessary MPP. The simulation's output also includes generated power, DC bus voltage, electromagnetic torque, and currents.

Simulation and modeling for controlling stepper motor with tuned PID by GWO: comparative study

The current work aims to simulate the operation of the electric motor in one of the most important industrial applications, which is printers, by adopting stepper motors (SM). The performance of the motor is also improved by adopting traditional control systems and adjusting them using the gray wolf optimization (GWO) advanced algorithm. It works to adjust the parameters of a conventional controller. Simulation to reach an appropriate design with high performance, which is obtained by adopting the integral time absolute error (ITAE) function to get rid of the error for transient cases. Transfer function was adopted to represent the engine and two methods of control were used, traditional and advanced optimization. Results demonstrated the possibility of improving performance by adopting both methods with a clear superiority of advanced optimization. Response of SM without controller for close loop shows the values of each rising time equal 130.440 ms, overshoot equal 0.505%, and undershoot equal 1.077%. Response of SM for close loop with proportional-integral-derivative controllers (PIDC) shows the parameters, performance, and robustness of PIDC also the values of overshoot=9.16%, settling time=0.406, and rise time=0.0628 s. Results were developed by using GWO-PID over the previous cases by reducing values of overshoot to zero, rise time, and settling time to 0.00145 and 0.0027 respectively.

Performance analysis of an optimized PID-P controller for the position control of a magnetic levitation system using recent optimization algorithms

As industrial technology advances, there is an increasing need for very precise position control mechanisms for highly integrated and accurate products. Therefore, high precision positioning systems play an essential part in today's manufacturing processes. Piezoelectric actuators offer the requisite stiffness and positioning precision, but they have a limited traveling range. The combination of a linear motor with air bearings is a typical method for achieving long stroke movement at high speeds. However, to accomplish extensive and precise motion in several degrees of freedom with a linear motor and non-contact bearing, a sophisticated system setup is required. In this paper, a proportional-integral-derivative-proportional (PID-P) controller is presented for magnetic levitation system position control. Particle swarm optimization (PSO) and Black window optimization (BWO) algorithms are proposed for tuning the PID-P controller parameters. The PSO and BWO algorithms are employed by considering Integral Time Absolute Error (ITAE) as an objective function with rise-time and percentage peak overshoot as constraints. Furthermore, the performance of PSO and BWOA tuned PID-P controllers is compared to conventional PID-P and PID controllers using time response specifications such as rise time, settling time, and percentage peak overshoot. The graphical and numerical simulation results show that the PSO and BWOA tuned PID-P controller outperforms the conventional PID-P and PID controllers. The BWOA tuned PID-P controller outperformed the PSO tuned PID controller and the classical PID-P controller by 8.5 % in rise time, 46.77 % in settling time, and 86.6 % in percentage peak overshoot.

On the Performance of the Model-Free Adaptive Control For A Novel Moving-Mass Controlled Flying Robot

In this paper, the performance of Model-Free Adaptive Control (MFAC) has been investigated on a novel and specific moving-mass controlled (MMC) flying robot system. The novel one-degree-of-freedom (1 DOF) MMC flying robot test bed presented in this paper has highly nonlinear and slow dynamics with a variable center of gravity (CoG) and moment of inertia. This makes the control of this system a challenging problem. One of the solutions to this challenge is the use of data-driven control methods, in particular, MFAC. This controller uses a data-driven model to control the system using only input and output (I/O) data. This paper compares this data-driven controller with proportional-integral-derivative (PID) control, and Linear Quadratic Regulator (LQR) as two model-free and model-based controllers which are widely used controllers in industry. The results of the comparison show that in the various scenarios applied, MFAC has a clear superiority over the PID and LQR, and its adaptive structure gives more freedom of action in the implementation of different scenarios and the presented noise. The results are obtained using the Integral Time Absolute Error (ITAE) criteria and the mean maximum error has also been compared in a Monte Carlo analysis. For a more detailed study, the amount of control energy consumption was also compared, which showed a clear superiority of the MFAC. Also, the robustness of the controller was demonstrated by introducing uncertainty in the plant parameters and by running 100 Monte Carlo simulations with random initial conditions. Finally, despite the PID controller, the MFAC followed the desired scenarios well and compared to LQR consumed less energy. The results demonstrate that the MFAC outperformed the PID and LQR controllers in the presence of random initial conditions and noise in terms of mean maximum error (70.4%)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(70.4\\%)$$\\end{document}, mean ITAE (91%)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(91\\%)$$\\end{document}, and energy consumption (46%)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(46\\%)$$\\end{document}.

Novel tuning rules for IMC-high-order PID load frequency controller of power systems

The problem of load frequency control (LFC) for power grids has received widespread attention in power industrial control. Ensuring the stability of the frequency is the primary concern that constrains the development of the electric power system. This paper presents a novel internal model control (IMC)- proportional-integral-derivative (PID) controller design method for LFC control systems, centered on pole-zero conversion. This method aims to simplify the system model's complexity and improve the system's performance. The IMC controller is approximated as a PID series compensator (high-order PID), with exact tuning formulas provided for various scenarios. These include single-area without or with reheat turbines, hydro turbines with transient droop compensators, wind turbines, and gas turbines. The tuning formulas are characterized by a single parameter, determined under robustness constraints and integrated time absolute error (ITAE). The proposed tuning method considers the generator rate constraint (GRC) and applies to multi-area single-source and multi-area multi-source power systems. The results of the simulation confirm that the controller tuning method presented in this paper has significant advantages over the existing design methods concerning robustness and anti-interference performance.

An adaptive fractional controller design for automatic voltage regulator system: sigmoid-based fractional-order PID controller

The primary objective of a power system is to provide safe and reliable electrical energy to consumers. This objective is achieved by maintaining the stability of the power system, a multifaceted concept that can be divided into three distinct classes. The focus of this study is on one of these classes, voltage stability. A critical component in maintaining voltage stability is the automatic voltage regulator (AVR) system of synchronous generators. In this paper, a novel control method, the sigmoid-based fractional-order PID (SFOPID), is introduced with the aim of improving the dynamic response and the robustness of the AVR system. The dandelion optimizer (DO), a successful optimization algorithm, is used to optimize the parameters of the proposed SFOPID control strategy. The optimization process for the DO-SFOPID control strategy includes a variety of objective functions, including error-based metrics such as integral of absolute error, integral of squared error, integral of time absolute error, and integral of time squared error, in addition to the user-defined Zwee Lee Gaing’s metric. The effectiveness of the DO-SFOPID control technique on the AVR system has been rigorously investigated through a series of tests and analyses, including aspects such as time domain, robustness, frequency domain, and evaluation of nonlinearity effects. The simulation results are compared between the proposed DO-SFOPID control technique and the fractional-order PID (FOPID) and sigmoid-based PID (SPID) control techniques, both of which have been tuned using different metaheuristic algorithms that have gained significant recognition in recent years. As a result of these comparative analyses, the superiority of the DO-SFOPID control technique is confirmed as it shows an improved performance with respect to the other control techniques. Furthermore, the performance of the proposed DO-SFOPID control technique is validated within an experimental setup for the AVR system. The simulation results show that the proposed DO-SFOPID control technique is highly successful in terms of stability and robustness. In summary, this study provides comprehensive evidence supporting the effectiveness and superiority of the DO-SFOPID control technique on the AVR system through both simulation and experimental results.

Optimizing load frequency control in microgrid with vehicle-to-grid integration in Australia: Based on an enhanced control approach

Microgrids are extensively integrated into electrical systems due to their many technical, economic, and environmental advantages. However, they encounter a challenge as they experience high-frequency fluctuations caused by the stochastic nature of renewable energy generation, electric loads, and the presence of Electric Vehicles (EVs). Therefore, various techniques, algorithms, and controllers have been introduced to ensure effective Load Frequency Control (LFC) and maintain a stable power system in microgrids. These methods aim to ensure that the system's frequency remains stable and within an acceptable range, especially when faced with changing load demands and other factors. This paper presents a novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), to optimize the load frequency model of a microgrid with vehicle-to-grid integration. The results are then compared under various scenarios, including renewable energy integration, EV charging and discharging dynamics, and varying load demands. The comparative analysis involves assessing the performance of the conventional Proportional–Integral–Derivative (PID) controller, the PSO-PID controller, and the newly proposed controlling technique. The suggested controller attains 99.904% efficiency with a negligible mean squared error of 1.1112 × 10−7, decreasing the integrated time absolute error to 1.0 × 10−4. It shows rapid response, precise targeting, and quick peak output ability, with marginal overshoot and undershoot, and a transient time of 28.5626 s, efficiently controlling microgrid frequency. Stability analysis validates the effectiveness of the proposed PSO-TANN controller in ensuring stability within the microgrid's LFC system during uncertainties and disturbances. This establishes resilience, diminishes settling time, and maintains reliable performance while controlling frequency.

Performance Analysis of Automatic Generation Control for a Multi-Area Interconnected System Using Genetic Algorithm and Particle Swarm Optimization Technique

The primary focus of this paper is to assess an interconnected power system using different optimization techniques. The main purpose is to employ different optimization techniques, including genetic algorithms (GA) and particle swarm optimization (PSO), to systematically enhance the performance of a multi-area or two-area automatic generation control (AGC) system, aiming to optimize the three PID controllers gain values and improve system performance under diverse loading conditions. Two case studies are conducted exploring different loading conditions in the megawatt (MW) range, including increasing load demand and decreasing load demand. The analysis involves four scenarios, covering without any kind of controller, another with solely a proportional integral derivative (PID) controller, a PID controller enhanced through a genetic algorithm (GA), and lastly, a PID controller improved through particle swarm optimization (PSO). The optimization process utilizes the integral time absolute error (ITAE) as the objective function to evaluate the system's performance. The simulation outcomes for ITAE, settling time, overshoot, and undershoot for frequency deviation of area one, area two, and power deviation in the tie-line are compared with previous similar studies to assess the novelty of this work. The article highlights the importance of the multi-area AGC system and the significance of different optimization techniques in improving its performance.

Research on the Control Method of a Brushless DC Motor Based on Second-Order Active Disturbance Rejection Control

This research addresses the issues of weak anti-disturbance ability, fast response, and incompatibility of overshoot in the control process of brushless DC motors (BLDCs). A six-step commutation control method based on second-order active disturbance rejection control (ADRC) is derived following the analysis of the BLDC model and the mathematical model of ADRC. A control model of the BLDC using both PI and ADRC is constructed. Detailed comparative and quantitative analyses of the simulation results using PI and ADRC are conducted, focusing on the anti-load disturbance capabilities using the integrated square error (ISE), integrated time square error (ITSE), integrated absolute error (IAE), and integrated time absolute error (ITAE). Experimental testing on the STM32F4 controller is also carried out, analyzing four error integral criteria in depth. The results indicate that both the ADRC and PI control modes can track the target signal without overshooting, demonstrating strong anti-load disturbance ability and robustness at varying working speeds. In the BLDC control system, using the ADRC control method can achieve fast and non-overshoot tracking of target signals compared to the PI control method, and ADRC has stronger resistance to load disturbances.

Optimal proportional-integral speed control for closed-loop engine timing system

In internal combustion engines, adjusting the air-fuel ratio is essential to control the speed and minimize the burnt fuel. The throttle opening is the actuator to control the air-fuel. A better design for the used/conventional controller can give a better response without additional cost. In this work, the proposed controller gains of the proportional-integral (PI) controller are tuned to enhance the speed in constant and variable drive cycle modes. The tuning process is conducted based on two of the most efficient performance indices used in this field. The performance indices are integral absolute error (IAE) and integral time absolute error (ITAE). The optimization problem is solved using three reliable stochastic optimization algorithms to ensure mature convergence of the solutions, to avoid local optima solutions, and to ensure effective shrinking of the search space. The optimization algorithms are teaching-learning-based optimization (TLBO), particle swarm optimization (PSO), and genetic algorithm (GA). Different simulations are conducted to validate the results. The results are compared with conventional tuning methods regarding the system's time response.

Multi-loop active disturbance rejection control and PID control strategy for poultry house based on GA, PSO and GWO algorithms

Maintaining the health and welfare of broilers, besides obtaining and optimizing good performance, are the main objectives of poultry production. In response, climate control remains the most guaranteed strategy for managing livestock successfully. Separate controlling temperature and humidity on the one hand; and contaminant gases on the other was a focus of several investigations. Thus, the particularity of this work which involves the study, analysis, and control of broiler livestock building while taking into account, at the same time, all the system's constituent variables (i.e., temperature, humidity, NH3 and CO2 concentration, air velocity, and differential pressure). In this paper, an Active Disturbance Rejection Control (ADRC) and Proportional Integral Derivative (PID) controllers were designed and combined with a multi-loop approach for a multi-inputs multi-outputs (MIMO) system. Then, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) were used to obtain the optimal controllers' parameters employing the reward function, the Integrated Time Absolute Error (ITAE), according to the poultry system requirements. Simulation experiments were carried out using the Matlab Simulink toolbox to verify the effectiveness of all the proposed control methods with the two optimization algorithms regarding stabilization and tracking setpoints. Despite the introduction of several disturbances in the plant model, the PSO-ADRC controller still exhibits notable benefits in terms of rise time, overshoot, settling time, and good disturbance rejection, proving the robustness of the suggested control method.

Data-Driven Controller for Drivers’ Steering-Wheel Operating Behaviour in Haptic Assistive Driving System

An advanced driver-assistance system (ADAS) is critical to driver–vehicle-interaction systems. Driving behaviour modelling and control significantly improves the global performance of ADASs. A haptic assistive system assists the driver by providing a specific torque on the steering wheel according to the driving–vehicle–road profile to improve the steering control. However, the main problem is designing a compensator dealing with the high-level uncertainties in different driving scenarios with haptic driver assistance, where different personalities and diverse perceptions of drivers are considered. These differences can lead to poor driving performance if not properly accounted for. This paper focuses on designing a data-driven model-free compensator considering various driving behaviours with a haptic feedback system. A backpropagation neural network (BPNN) models driving behaviour based on real driving data (speed, acceleration, vehicle orientation, and current steering angle). Then, the genetic algorithm (GA) optimises the integral time absolute error (ITEA) function to produce the best multiple PID compensation parameters for various driving behaviours (such as speeding/braking, lane-keeping and turning), which are then utilised by the fuzzy logic to provide different driving commands. An experiment was conducted with five participants in a driving simulator. During the second experiment, seven participants drove in the simulator to evaluate the robustness of the proposed combined GA proportional-integral-derivative (PID) offline, and the fuzzy-PID controller applied online. The third experiment was conducted to validate the proposed data-driven controller. The experiment and simulation results evaluated the ITAE of the lateral displacement and yaw angle during various driving behaviours. The results validated the proposed method by significantly enhancing the driving performance.

Influence of Using ACO Algorithm in STATCOM Performance

Flexible alternating current transmission systems (FACTS) are technologies that rely on the use of high-power electronic equipment and advanced control methods to increase the ability of electrical transmission lines to transmit the largest amount of electrical power.Static_ synchronous _compensator (STATCOM) is one of FACTS_ family It has a rapid ability to supply or absorb reactive power from the system, traditional controller in STATCOM is (PID) or (PI). The controller is used to control the currents and voltages. In (STATCOM) there are two control loop, the inner loop which is represented by controlling the source current, and the outer loop which is used to control the voltage of the DC-link energy –storage- capacitor (vdc). Many algorithms are used in optimization to predict a specific behavior and to find the best solution for it. Two optimization methods were used Ant Colony Algorithm (ACO) and Genetic Algorithm (GA) to adjust the parameters of the outer PI controller. This unit is responsible for adjusting (vdc). The capacitor voltage is converted by the STATCOM inverter into a three-phase voltage, which is used to supply the load with required reactive power. Through the simulation and control process, the results demonstrated the effectiveness of the compensator in supplying the reactive power to the linear or non-linear loads, The results also showed that using the ACO technique that the integrated time absolute error ( ITAE) was reduced which gives more precise in adjusting the PI control parameters Which is achieved best Vd.c response ,compared with the GA and ziklur-nichlos method , by measuring the response of the lowest settling time (TS) and minimal peak overshoot (P-ov) .

Enhancement of Field Oriented Control for Permanent Magnetic Synchronous Motor using Ant Colony Optimization

Because of its frequent use in diverse systems, the PMSM drive must be controlled. Field-oriented control (FOC) based PMSM drive is modeled in the present work to optimize the torque and speed performance of the PMSM. The FOC is based on a dissociated speed and flux control approach, which controls the speed and flux of the PMSM independently. The standard Proportional Integrator Derivative (PID) controller regulates the speed in FOC, which is noted for its increased resilience in linear systems, however in nonlinear ones, the PID controller responds poorly to changes in the system’s variables. In this case, the best solutions are frequently based on optimization techniques that produce the controller’s gains in every period. Optimizing the PID’s behavior in response to the system’s nonlinear behavior. The novel proposed strategy for enhancing the gains of the PID controller by employing a cost function such as Integral Time Absolute Error (ITAE) is based on PID speed regulation and is optimized using the Ant Colony Optimization algorithm (ACO) for FOC. To confirm the strategy’s aims, the suggested method is implemented on Matlab/Simulink. The simulation results demonstrated the efficiency of the intelligent ACO-FOC control, which delivers good performance in terms of stability, rapidity, and torque fluctuations.

Load frequency control of a two-area using virtual inertia and a PSO-PID controller with integrated electric vehicles

The increasing penetration level of converter-based renewable energy sources in the grid causes low inertia, which leads to grid frequency variation. To deal with this problem, virtual inertia (VI) control with particle swarm optimisation (PSO)-based PID controller has been applied to a two-area power system with multi-generating units. This improves the frequency variation and response time. In this paper, a derivative control technique has been used to emulate the VI, which controls the active power of the energy storage device which provides the inertia support to the power system. The integral time absolute error (ITAE) has been used as the performance index for PSO. Furthermore, the primary frequency control is augmented by integrating electric vehicles as vehicle-to-grid resources. Finally, various cases of disturbance and a comparative analysis have been performed between the VI with PSO-PID control and conventional control (PI). The simulation has been performed using MATLAB/Simulink, which demonstrates the superior performance of the VI with PSO-PID over a conventional controller by improving the settling by 45.84% in Area 1 and 39.77% in Area 2 for the almost same overshoot of frequency deviation. These simulation results have also been validated by performing hardware-in-loop (HIL) experiments utilising the OPAL-RT real-time simulator.

Modeling, design and control of a 4-arm delta parallel manipulator employing type-1 and interval type-2 fuzzy logic-based techniques for precision applications

This paper presents the virtual design of a four-arm Delta parallel robot for potential applications in the food industry, specifically for the automated preparation of fast foods. Kinematic and dynamic models were developed based on the morphology of this type of robot. Cutting-edge and previously unexplored control strategies for these types of manipulators are then designed and implemented based on Fuzzy PD and Fuzzy PID configurations. Several performance indicators, such as the Integral Square Error (ISE), Integral Time Absolute Error (ITAE) and Integral Time Square Error (ITSE), were used to conduct a performance comparison of the control techniques, considering type-1 and interval type-2 fuzzy sets. In all the analyzed scenarios, the fuzzy controllers correspond to the Takagi–Sugeno–Kang model using linear functions of the inputs in the outputs. Among the main contributions of this work is the development of a detailed dynamic model of the robot in Simscape, incorporating realistic aspects that are often overlooked during analytical modeling. To ensure more accurate results, the rejection of external disturbances is thoroughly analyzed in simulations, considering elements such as time delays and control signal saturations. The results demonstrate the veracity of the proposed design for a four-arm Delta robot, both in models and in the Simscape/Simulink implementation. In the trajectory tracking task and external disturbance rejection, the superiority of the Fuzzy PID controller with interval type-2 fuzzy sets over fuzzy controllers with PD structure and over type-1 fuzzy sets is evidenced.