Improved load frequency control with chess algorithm-driven optimization of 3DOF-PID controller

This paper compares the performance of a group of intelligent algorithms such as the genetic algorithm (GA), particle swarm optimization (PSO), and repulsive particle swarm optimization (RPSO) based on the optimization of thermo-economic indicators such as the payback period (PBP), the levelized energy cost (LEC), the specific investment cost (SIC), and also in the optimization of the thermodynamic process (net power output) of an energy recovery system in a 2 MW natural gas internal combustion engine based on an organic Rankine cycle. Four parameters were considered to analyze and compare the performance of these algorithms: integral of squared error (ISE), integral of absolute error (IAE), integral of time-weighted absolute error (ITAE), and the integral of time-weighted squared error (ITSE). Analyses of variances (ANOVA) were proposed for each of the parameters studied. The PSO and RPSO algorithms presented the best performance in terms of the mean and the standard deviation of the ISE, IAE, ITAE, and ITSE parameters. Significant differences were not found between the three algorithms in terms of the parameters considered. However, significant differences did exist when comparing groups (pairs) of algorithms considering a significance level of 5%. The ANOVA analysis showed that ITAE was the most affected parameter by population size, while the IAE and ITSE parameters were the less affected. In the optimization, the PSO algorithm obtained the best performance in terms of convergence with values of 0.1110 USD/kWh (LCOE), 4.6971 years (PBP), 1114 USD/kWh (SIC), and 173.64 kW (Wnet). PSO-based algorithms obtained better performance in computational terms compared with the genetic algorithms.

This research project investigates the regulation of autonomous power generation in two interconnected regions using two hydroelectric power plants. It specifically addresses the challenges posed by significant electrical system issues. The hippopotamus optimization algorithm (HOA) has demonstrated enhanced gain value in research and designs of 2 degree of freedom (2DOF)-proportional integral derivative (PID) controllers. The objective is to provide efficient and uninterrupted functioning of the electrical network in both areas. Contemporary technology and methods enable the electrical system to efficiently and accurately fulfill user requirements, resolving any problems related to system balance and stability. This experiment evaluates the efficacy of several algorithms in accurately selecting optimal values. We evaluate performance using the integral of absolute error (IAE) and integral of time-weighted absolute error (ITAE) functions. This experiment evaluates and contrasts different algorithms. Summarizing the analysis using verifiable evidence. Optimization when evaluated using the ITAE measurement, the HOA earned the lowest result of 0.08744 for ITAE. Empirical research has demonstrated that this strategy is the most effective in reducing the ITAE. The sine-cosine algorithm (SCA) and whale optimization algorithm (WOA) have similar ITAE values, with SCA having an error of 0.08967 and WOA having an error of 0.08967. The numerical number is 0.08970.

This paper presents a comparative analysis of a fractional-order proportional–integral–derivative (FO-PID) controller against the standard proportional–integral–derivative (PID) controller, applied to a nonlinear robotic arm manipulator systems. The genetic algorithm (GA) optimization method was implemented to tune the gain parameters of the FO-PID and PID controllers. The performance of the FO-PID and PID controllers were evaluated though different cost functions, including integral of squared error (ISE), integral of absolute error (IAE), integral of time-weighted absolute error (ITAE), and integral of time-weighted squared error (ITSE). The performance of these controllers was examined via extensive simulations by using MATLAB/SIMULINK for different operating scenarios of the robotic arm manipulator system. Based on the obtained results, a comparative performance matrix is proposed, wherein cost functions ISE, IAE, ITAE, and ITSE are represented as columns while characteristic parameters (overshoot, rising time, and settling time) are represented as rows. The proposed performance matrix facilitates the selection between the PID and FO-PID controllers.

Implementation of tilt integral derivative (TID) controller for controlling the speed of a D.C. Motor using meta heuristic nature inspired algorithm named by firefly algorithm (FA) is proposed in this paper. By using FA based optimization technique, we have tuned TID controller parameters. Further, comparative analysis has been done with FA based conventional PID and fractional order PID (FOPID) controllers. The performance of TID is investigated in terms of various performance indices like integral of square error (ISE), integral of time-weighted absolute error (ITAE), integral of absolute error (IAE) and integral of time-weighted square error (ITSE). Investigation carried out reveals the advantage of TID over conventional PID and FOPID in terms of reduced settling time and performance indices.

Mineral processing facilities concern an enormous amount of dynamically complex unit operations (due to nonlinearities), for instance ball mill system. Normally, these processes need multivariable controllers to smooth actions by designing for plant constraints such as deadtimes and dynamics interactions. The present work presents a comparison between a classical PI and nonlinear moving average autoregressive-linearization level 2 (NARMA-L2) controllers based on artificial neural network (ANN) for a ball mill system. The manipulated variables of this plant are the rotation velocity (Vr) and the feeding weight (Wf), while the controlled parameters are the hold up (HU) and the mass fraction under 45 μm (P45). The simulation was built in the MATLAB software (Simulink), comparing the actions of PI and NARMA-L2 controllers in the face of operational changes in specific regions (constraints). The performance of proposed controllers was verified by the integral of absolute error (IAE), integral of squared error (ISE), or the integral of time-weighted absolute error (ITAE). The results of simulation showed the validity of the model obtained and the control technique proposed in this paper, which contributes to studies of multivariate controller designs for ball mills with significant applications. Additionally, this paper brings a first hybrid approach (PI/NARMA-L2) with successful implementation described in the literature.

Regulation capability of an automatic voltage regulator (AVR) system still needs to be improved to keep the output voltage of the generator within the AVR system at the desired level. Researchers have been developing new control structures and designing controllers to improve the performance of the AVR system. Designing of PID controller, which is commonly preferred controller due to its simple structure and robustness against to system parameter changes, has an important place among these studies. Especially with the development of metaheuristic algorithms, more successful PID controller designs are emerging by using these algorithms than traditional design methods. Undoubtedly, the objective function utilized also has a significant effect on this success. Therefore, effects of the objective function in PID controller design process for an AVR system are examined in this study. Two different PID controllers are designed using two different metaheuristic algorithms, namely, crow search algorithm (CSA) and ant colony optimization (ACO) algorithm. The parameters of the PID controllers are optimally tuned by using five different objective functions in both algorithms. These objective functions are: Integral of absolute error (IAE), integral of squared error (ISE), integral of time-weighted absolute error (ITAE), integral of time-weighted squared error (ITSE), and a commonly used user-defined objective function. The performance of the designed PID controllers are compared in terms of transient response characteristics and performance metrics. In addition, in order to evaluate the stability of the AVR system with the designed controllers, bode analysis, pole-zero map analysis and robustness analysis are performed.

Design of optimal input–output scaling factors based fuzzy PSS using bat algorithm

Control of electric machines using flower pollination algorithm based fractional order PID controller

The effectiveness of the model structure for designing control system is highly depending on the right selection of tuning parameters belonging of the algorithms. No doubt, preferable parameter estimation leads to better results for the modelling process. This study consists of two main parts. In the first part, fractional and integer model structures with time delay are proposed for integer-order systems using artificial bee colony (ABC) algorithm and differential evolution (DE) algorithm. The second part of the study is the fractional order PID controller design based on the use of new models obtained with the help of Matlab/Simulink software package. While the integrated square error (ISE) is preferred in the modelling process as the performance criterion, four different performance indices are chosen as ISE, integral time-square error (ITSE), integral absolute error (IAE) and integral time-weighted absolute error (ITAE) during the controller design phase. It is shown that, the results obtained with fractional modelling have achieved better results than the integer order modelling. Furthermore, the controller designs for the algorithm based models proposed in the first stage of the study present a satisfactory performance.DOI: http://dx.doi.org/10.5755/j01.eie.24.5.21836

Stewart platform or other parallel manipulators with a Stewart structure are commonly used in flight simulators, surgical operations, medical rehabilitation processes, machine tools, industrial applications, etc. Therefore, researchers have paid attention to position control of these manipulators in addition to their design and development process. In this study, a developed Stewart platform and its inverse kinematic analysis are presented first. Then, a model-free control scheme called a high order differential feedback controller scheme is designed for the Stewart platform in order to improve its trajectory tracking performance and robustness against to different reference trajectories. Real-time trajectory tracking experiments with varied reference trajectories are carried out to show the robustness and effectiveness of the high order differential feedback controller scheme compared to the traditional proportional–integral–derivative controller of which the parameters are optimally tuned. The obtained visual trajectory tracking results and numerical performance results based on error-based performance measurement metrics such as integral of absolute error, integral of squared error, and integral of time-weighted absolute error are provided for both the proposed high order differential feedback controller scheme and the optimal tuned proportional–integral–derivative controller. Experimental results show that the proposed high order differential feedback controller scheme is more robust than the proportional–integral–derivative controller. Furthermore, the high order differential feedback controller scheme has superiority in both transient and steady-state responses and even the parameters of the proportional–integral–derivative controller are optimally tuned.

This study investigates the optimization of a 2-Degree-of-Freedom Proportional-Integral-Derivative (2DOF-PID) controller for an air pressure monitoring sensor system using a Multi-Objective Genetic Algorithm (MOGA). The research addresses the common challenge of time delays in real-world control systems, which often stem from sensor latency, actuator dynamics, and signal transmission lags which are factors that compromise system stability and performance. To address this, the system was mathematically modeled using a transfer function to represent the dynamic behavior of the air pressure monitoring sensor, a key component in regulating pneumatic systems. The 2DOF-PID controller was implemented to independently manage reference tracking and disturbance rejection, providing greater control flexibility. The MOGA was employed to fine-tune the controller parameters based on three standard performance indices: Integral of Absolute Error (IAE), Integral of Squared Error (ISE), and Integral of Time-weighted Absolute Error (ITAE). For comparison, other optimization algorithms such as ChASO, GA, MOPSO, and ISCA were also applied. Simulation results demonstrated that the MOGA-optimized controller outperformed all other approaches, achieving superior performance metrics: -82.9% flow disturbance rejection, -76.8% temperature disturbance rejection, 1.24% overshoot, no undershoot, a fast-settling time of 44.25 seconds, and a rise time of 53.2 seconds. These results highlight the MOGA’s effectiveness in enhancing the robustness and responsiveness of pneumatic control systems.

Vapor-compression refrigeration systems (VCRS) are applied extensively in domestic, commercial and industrial refrigeration and are responsible for a high percentage of worldwide energy consumption. To achieve high energy efficiency, the application of advanced control methods in VCRS has increasingly attracted the attention of academia and industry. The model-free adaptive control (MFAC) strategy, as an important branch of advanced control research, encounters the problem of parameter tuning when applied to VCRS with strong nonlinearities. In this work, a parameter self-tuning methodology based on the self-learning and self-adapting properties of back propagation neural networks with the System Error set and/or Gradient Vector set as inputs is proposed to adjust the parameters utilized in SISO Partial-Form Model-Free Adaptive Control (SISO-PFMFAC). To test the performance of this novel methodology named SISO-PFMFAC-NNSEGV, qualitative and quantitative comparisons are carried out between the proposed method and the decentralized single PIDs given in the simulation platform provided by the benchmark PID 2018. The integral absolute error ( IAE ), the integral time-weighted absolute error ( ITAE ), the integral absolute variation of control signal ( IAVU ) and a combined index $J_{c}$ are used to evaluate the performance. As a result, the proposed method shows the best performance with a higher tracking accuracy and less variation of the control signal with a combined index $J_{c} = 0.7088$ , which represents a 29.1% improvement over the benchmark PID controller and a 9.6% improvement over the SISO-PFMFAC controller, making it a promising control method for vapor-compression refrigeration systems.

Recent methodology based Harris Hawks optimizer for designing load frequency control incorporated in multi-interconnected renewable energy plants

Optimal fractional order PID controller design using Ant Lion Optimizer

Voltage control of Proton Exchange Membrane Fuel Cell (PEMFC) is necessary for any practical application. This paper considers a state space model for controller design and a Neural Network (NN) feed forward controller with an optimization technique called Harmony Search algorithm is considered to control the output voltage. This paper compares the results of the proposed controller with the NN feed forward controller. The comparison shows the proposed controller follows the reference voltage more closely than NN feed forward controller. Finally the performance of the controller is studied by evaluating Integral Squared Error (ISE), Integral Absolute Error (IAE) and Integral Time-weighted Absolute Error (ITAE) and the results are compared. The system error of the proposed controller is reduced to a least minimum value compared with the other.