Controller Design Parameters Research Articles

Research on carbonation percentage of carbonated recycled concrete fine aggregate: experimental investigation and machine learning prediction

The carbonation percentage (CP) is an essential carbonation degree evaluation index that determines the physicochemical properties of carbonated recycled concrete fine aggregate (CRFA), and its accurate prediction is necessary. Therefore, this paper first uses six machine learning algorithms to build predictive models, aiming to forecast the CP of CRFA under various carbonation control parameters and analyze their advantages and shortcomings. Six models including k-nearest neighbor regression, back propagation neural network, decision tree (DT), random forest, gradient boosting decision trees, and extreme gradient boosting (XGB) are trained, tested, and validated by employing a comprehensive dataset collected from our 100 mix design samples. As expected, the XGB model shows a fascinating prediction ability superior to other proposed models, owing to the largest coefficient of determination being 0.974 and the lowest root mean squared of 1.521 in the testing set. Moreover, the sensitive analysis of input feature parameters is conducted using Shapley additive explanations (SHAPs) according to the XGB-based prediction model, illustrating C-Con (average SHAP value = 0.72) shows the largest impact on the development of CP. This study promotes effective forecasting and modifies the suitable design of carbonation control parameters, providing a theoretical foundation for utilizing building waste following low-carbon and ecologically friendly development concepts.

A Controller Design for Evaders Considering Single Integrator Dynamics

This paper presents a novel controller for the evader in the pursuit–evasion game, when each player is modeled as a single integrator. The resulting control design does not require any information on the pursuer control, only requires information on the pursuer state. The resulting control ensures the escape of the evader from the pursuer under an adequate selection and design of the evader controller parameters. Additionally, the synthesis of this controller is constructive and can be tuned straightforwardly since it is obtained from the solution of an LMI. The stability analysis of the tracking error dynamics is carried out considering a Lyapunov–like function and the effectiveness of the proposed result is illustrated through some simulations.

Improving poultry system in close house cage through advanced HVAC design: A Review of evaporative cooling pads and energy efficiency in broiler cages

Improving the quality and quantity of livestock production can be achieved by creating a comfortable and safe environment for animals. The use of closed-house pens is one of the methods employed to control temperature, humidity, airflow, and the cleanliness of the living space for animals. Close-house pens are equipped with Heating, Ventilation, and Air Conditioning (HVAC), including the combination of an Evaporative Cooling Pad (ECP) and an exhaust fan. The characteristics of the shape and material composition of the ECP components influence pressure drop and the flow pattern entering the room. This research focuses on reviewing papers related to the development of numerical simulation studies of close-house pens and ECP. The design of numerical simulations and the selection of boundary conditions enhance the precision and error level of predicting fluid flow distribution in closed-house cages. In addition to numerical simulations, the application of energy management calculations provides recommendations regarding the combination of HVAC design and environmental control parameters that need to be considered.

Data-Driven Kinematic Model for the End-Effector Pose Control of a Manipulator Robot

This paper presents a data-driven kinematic model for the end-effector pose control applied to a variety of manipulator robots, focusing on the entire end-effector’s pose (position and orientation). The measured signals of the full pose and their computed derivatives, along with a linear combination of an estimated Jacobian matrix and a vector of joint velocities, generate a model estimation error. The Jacobian matrix is estimated using the Pseudo Jacobian Matrix (PJM) algorithm, which requires tuning only the step and weight parameters that scale the convergence of the model estimation error. The proposed control law is derived in two stages: the first one is part of an objective function minimization, and the second one is a constraint in a quasi-Lagrangian function. The control design parameters guarantee the control error convergence in a closed-loop configuration with adaptive behavior in terms of the dynamics of the estimated Jacobian matrix. The novelty of the approach lies in its ability to achieve superior tracking performance across different manipulator robots, validated through simulations. Quantitative results show that, compared to a classical inverse-kinematics approach, the proposed method achieves rapid convergence of performance indices (e.g., Root Mean Square Error (RMSE) reduced to near-zero in two cycles vs. a steady-state RMSE of 20 in the classical approach). Additionally, the proposed method minimizes joint drift, maintaining an RMSE of approximately 0.3 compared to 1.5 under the classical scheme. The control was validated by means of simulations featuring an UR5e manipulator with six Degrees of Freedom (DOF), a KUKA Youbot with eight DOF, and a KUKA Youbot Dual with thirteen DOF. The stability analysis of the closed-loop controller is demonstrated by means of the Lyapunov stability conditions.

Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm

The increasing complexity of modern plant systems and the limitations of precise mathematical modeling have led to a shift towards data-driven control methods. These methods present an effective alternative that treats plants as black-box systems without requiring explicit models. The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. Nonetheless, control performance can be limited by the fixed probability coefficient used in the original SEDA to disrupt design parameters, especially when balancing exploration and exploitation phases. This study proposes an enhanced version of the SEDA: the modified SEDA (MSEDA), to address this issue. The MSEDA introduces a dynamic probability coefficient that decreases with each iteration. The adjustment improves the balance between exploration and exploitation phases, which enhances control accuracy. The MSEDA was used to tune the brain emotional learning-based intelligent controller (BELBIC) together with a proportional-integral-derivative (PID) controller. The result was the BELBIC-PID controller inspired by the limbic system of the human brain, which has high precision. The effectiveness of the proposed MSEDA-BELBIC-PID was validated using simulations on multi-input multi-output (MIMO) systems, with a focus on tracking performance and computational efficiency. The statistical analysis of 30 independent trials demonstrated that the proposed MSEDA-BELBIC-PID was significantly improved over the original SEDA-BELBIC-PID. Wilcoxon's rank test yielded a fitness function p-value < 0.05, which confirmed the robustness and effect of the proposed enhancement. The comparative results demonstrated that the MSEDA-BELBIC-PID consistently performed better than the original approach and had improved fitness function values, reduced total integral square error, and lower total integral square input. These findings underscored the MSEDA suitability as a data-driven tool for controller design parameter optimization. Furthermore, the low computational burden of MSEDA rendered it a strong alternative to heuristic multi-agent algorithms, which frequently encounter high computational costs with large controller design parameters.

Design and optimization of additive manufactured Fischer-Koch-structured heat exchanger for enhanced heat transfer efficiency

The study explores using triply periodic minimal surfaces (TPMS) to enhance heat exchanger efficiency and reduce flow resistance. By leveraging TPMS's high surface area and minimal curvature, a range of compact heat exchangers with different unit cell sizes was developed for thermal-hydraulic analysis. The Fischer-Koch (FK) S structure, a TPMS model, was used to precisely control design parameters like porosity and surface area. Selective Laser Melting (SLM) facilitated the creation of complex geometries, which were analyzed through numerical simulations and experiments. Results showed a strong link between Nusselt (Nu) and Reynolds (Re) numbers for the FK structure. The smallest unit cell (6 mm) achieved the highest heat transfer rate and specific volume heat transfer of 140 W/cm3, a notable industry advancement. A “Light” configuration reduced the heat exchanger's volume by 28 % without affecting thermal performance, indicating a new direction for efficient, lightweight designs.

Improved fractional order control with virtual inertia provision methodology for electric vehicle batteries in modern multi-microgrid energy systems

Providing inertia in modern electrical power grids has become a highly demanding task to mitigate reduced power system inertia of renewable energy sources (RES) based power grids. To mitigate the reduced inertia-related problems and consequences, this paper presents improved FOCs for achieving load frequency control (LFC) and the virtual inertia control (VIC) provision of electric vehicles (EVs) lithium-ion batteries. The proposed method includes a twofold contribution using a new hybrid FOC method and an optimum control parameter design method. An innovative hybrid controller is proposed by merging the characteristics of FOPID and TID methods with a fractional filtering stage, leading to hybrid tilt plus FOC integrator-derivative terms. From the VIC side, the TID is proposed to provide a fast response to lithium-ion batteries of the already available EVs. Furthermore, a metaheuristic-based design method is proposed to simultaneously determine the best control parameters set and to facilitate the design procedures using the recently presented powerful growth optimization algorithm (GO). The two interconnected areas case study with high-RES penetration is suggested for testing and investigating the new proposed controller. The results include performance evaluations and comparisons at various expected model uncertainties, renewable energy intermittency, and parametric variations. For instance, the proposed VIC method has a minimized ISE after 28 iterations of 0.001 compared to 0.003 with conventional VIC, and 0.004 without VIC. Thence,the proposed controller achieves more accurate frequency tracking with reduced deviations and fluctuations and exhibits greater robustness compared to previous controllers used in all case studies.

Power controller design for electrolysis systems with DC/DC interface supporting fast dynamic operation: A model-based and experimental study

The ability of the electrolysis system, powered by fluctuating and intermittent renewable sources, to rapidly and accurately track power signals is crucial for energy management in renewable power-to-hydrogen (ReP2H) plants and for providing grid frequency regulation as a virtual power plant (VPP). However, there is a considerable lag (several seconds or even more than 20 s) between the changes of the stack power compared with the stack current, mainly due to the existence of the electric double-layer (EDL) effect. This characteristic hinders the further application of electrolysis systems as flexible loads in power grids. By designing a suitable power controller in the power-electronics interface directly connected to the stack to replace the traditional current controller, it is expected to improve the fast dynamic response of the stack power. This paper proposes a unified electrical equivalent circuit for alkaline water electrolysis (AWE) systems and proton exchange membrane (PEM) electrolysis system with detailed parallel Buck type DC/DC interface, which considers the EDL effect and nonlinear behaviors of electrolysis systems and is suitable for controller design. A power controller design and robust parameter tuning method without excessive current overshoot based on frequency-domain analysis is proposed. The accuracy and effectiveness of the proposed model and method are verified by the experimental test on the 2 N m3/h(10 kW) AWE system and the 1 N m3/h(5 kW) PEM electrolysis system. With the proposed power controller, the AWE and PEM electrolysis system can change the stack power within 0.266s and 0.21s respectively, meeting the requirements of energy management and frequency regulation. Additionally, the temperature stability and the sensitivity of the proposed method to parameter fluctuations in the stack and DC/DC interface are analyzed.

Novel approaches in developing sustainable and cost-effective semi-active suspension systems for smart vehicles—A review

The dynamic evolution of automotive technology necessitates integrating intelligent systems in suspension systems to enhance vehicle dynamic performance and environmental sustainability. The major challenge in the vehicle suspension design is to achieve an optimum tradeoff between riding comfort and ground holding with the available rattle space. Semi-active suspension systems (SASSs), which meet these requirements more effectively than passive and active systems, have gained prominence for their potential. Different SA control strategies and dampers have been developed to enhance dynamic performance. The optimal control strategies and design parameters improve the responsiveness of SASSs in the vehicle dynamic performance. The algorithms attempt to avail the full potential of various dampers controlled electronically. The accurate representation of nonlinearities in mathematical modeling with noise mitigation enhances the practical implementation of novel controllers in SASSs. This review investigates the forefront of research and development in SASSs, focusing on innovative strategies to make these systems more sustainable, adaptive, and cost-effective for smart vehicles. Semi-active magnetorheological dampers have more commercial applications than other dampers, but the high cost makes them unaffordable for economical cars. Several researchers have attempted to develop inexpensive, high-performance dampers that can vary damping coefficients precisely for different road conditions. Real-time energy harvesting-based dampers for sustainable electric mobility are under development. Commercially available SASSs are less expensive than active suspensions but they have limited applications than passive suspensions. The low market share of existing SASSs and their regenerative inability are the major drawbacks in making it a sustainable suspension system. The novel strategies attempted to develop an effective smart suspension system and its limitations are discussed in this review. The next generation of SASSs that aligns with the global drive towards smarter, greener, and economical vehicles is identified.

Research on parameter optimization method of thrust vector/aerodynamic rudder composite control law based on singular value method

Advanced aircraft with thrust vectoring/aerodynamic rudder layout have significant control coupling problems. The traditional single-loop control parameter design method cannot take into account the performance of multiple control loops in this scenario. Therefore, a control law parameter optimization method based on the singular value of the hysteresis matrix is proposed. First, the control parameter optimization interval is determined using the time domain control performance index. Then, the singular value method is used to measure the stability margin of the multiple-input multiple-output (MIMO) system on this basis, and the corresponding optimal objective function is established to optimize the controller parameters. The feasibility of this method is verified by numerical simulation. The results show that the designed control parameter optimization algorithm has better time domain control performance and larger system stability margin than the traditional single-loop control parameter design method.

Small-Signal Stability Analysis and Voltage Control Parameter Design for DC Microgrids

Small-signal instability issues will occur in the DC microgrid when the high-frequency oscillation peaks of the voltage closed-loop transfer function are not effectively suppressed. To ensure the small-signal stability of DC microgrids, the concept of a small-signal stability domain for voltage control parameters is proposed. Based on the voltage closed-loop transfer function, a small-signal-stability-domain-solving algorithm is proposed. With this stability domain, the impact of voltage-proportional coefficient and voltage-integral coefficient on oscillation frequency (or damping factor) is quantitatively analyzed. In addition, the influence of current control parameters on the small-signal stability boundary is also analyzed. With the introduction of this stability domain, a design method for voltage control parameters has been proposed. The voltage control parameters, which are quantitatively designed by this method, are able to maintain the small-signal stability of the system. Finally, based on the RT-BOX hardware-in-the-loop experimental platform, a switching model for a typical DC microgrid is established. Additionally, the effectiveness of the proposed algorithm and designed control parameters is verified by multiple sets of experimental results.

Multi-Objective Optimization of Building Design Parameters for Cost Reduction and CO2 Emission Control Using Four Different Algorithms

Multi-Objective Optimization of Building Design Parameters for Cost Reduction and CO2 Emission Control Using Four Different Algorithms

Active disturbance rejection control of a bearingless permanent magnet slice motor

Abstract Bearingless permanent magnet slice motor has the advantages of easy stator-rotor isolation, no contact, simple structure, and high integration, which is a significant feature in the ultra-pure field of biology, chemistry, medical treatment, semiconductor manufacturing, aerospace, and other applications, as well as high-speed drive field. To realize the control of a bearingless permanent magnet slice motor, the mechanism of torque control, as well as suspension control, is systematically analyzed. On this basis, a simplified control parameter design method for decoupling torque and suspension is proposed, and the parameter tuning rules are given. For the problem of insufficient anti-disturbance performance of the suspension control under the traditional linear active disturbances rejection controller (LADRC), an improved LADRC strategy based on known disturbances is proposed, and an expansion state observer (ESO) with partially known disturbances is designed. The suspension accuracy and robustness of the system are verified by simulating the motor control with a Matlab/Simulink module. Finally, the improved LADRC control is used in a BPMSM prototype. The simulation and experimental results show that the improved LADRC control with the addition of known perturbations effectively improves the suspension control stability and accuracy compared to the traditional LADRC control.

Optimizing vertical pump reliability: Investigating main shaft challenges through integrated design and testing strategies

ABSTRACT Vertical pump systems are vital in industries like water treatment, power generation, and oil and gas production. Their efficiency depends on well-designed components, especially the main shaft connecting the motor to the pump impeller. Changes in shaft length greatly impact vibration, structure, and overall performance. Therefore, thorough evaluation of shaft length modifications is crucial for ensuring equipment suitability and long-term reliability. This study examines how changing shaft length in a vertical pump system, from 70 cm to 170 cm due to construction constraints, affects its performance. Using Finite Element Analysis (FEA), the study evaluates both shaft designs’ natural frequencies, critical speeds, and maximum stresses. Results reveal that the 170 cm shaft design is unsuitable, leading to increased stresses and a higher risk of vibration-induced failures compared to the original design. These findings stress the importance of controlling design parameters and conducting thorough analyses before implementing modifications to ensure reliability and operational stability. Unplanned design changes like shaft length modifications can severely impact pumping equipment performance, emphasizing the need for careful consideration and analysis prior to implementation. This research emphasizes the importance of considering shaft length in the design of vertical pump systems to ensure reliability. The findings offer valuable insights for engineers and designers to maintain long-term operational stability and performance. Ultimately, this improves safety and efficiency in industrial and municipal processes reliant on these pump systems.

Improved control strategy and designed control parameters of pitch system for wind turbine considering blade load reduction

As wind turbines increase in the power, the longer the blade, the more severe the blade load. Traditional control strategies and control parameters do not consider to reduce blade load, which seriously threatens the safe operation of wind turbines. Therefore, this study proposes a pitch control strategy based on blade active damping control and a multistage control parameter design method. Firstly, this study proposes a pitch control strategy based on blade active damping control to reduce the blade load based on the establishment of a mathematical model of the pitch system considering the blade load and a dynamic model of blade active damping control. Secondly, in the control parameter initial values design of Stage Ⅰ, this study proposes a variable forgetting factor recursive least squares to identify a simplified mathematical model of the pitch system and a Low–overshoot Chien–Hrones–Reswisk to solve the proportional integral control parameter initial values. Thirdly, in the control parameter optimal values design of Stage Ⅱ, this study proposes an adaptive genetic algorithm with improved hyperbolic tangent function to optimize control parameter at the equilibrium points and proposes an adaptive control to calculate control parameter at the non–equilibrium points. Finally, based on the Bladed software for 2 MW and 5 MW wind turbines, the superiority of the proposed methods, effectiveness and applicability in improving the control accuracy of the output power and generator speed, and reducing the load situation at the blade root position are verified.

Distributed event‐triggered fixed‐time formation tracking control for multi‐spacecraft systems based on adaptive immersion and invariance technique

AbstractIn this study, the problem of fixed‐time formation tracking for multi‐spacecraft systems without internal collisions was investigated. Aiming to ensure that the formation members can accurately realize and maintain the required configuration within the user‐given time, we designed a novel adaptive immersion and invariance (I&amp;I)‐based control protocol. The novelty here lies in two aspects. First and foremost, this study was based on the adaptive I&amp;I technique, combined with a new artificial potential function, to achieve the desired formation tracking without internal collision. Second, unlike the asymptotic convergence of the traditional I&amp;I‐related works, to guarantee the fixed‐time stability, the proposed protocol introduced the prescribed performance control, which can also alleviate the possible system performance degradation caused by the non‐periodic control signal update of the event‐triggered mechanism. Further, the introduction of the event‐triggered mechanism can reduce the unnecessary information interaction. Lyapunov stability analysis shows that this proposed protocol can enable the defined implicit manifold to converge to the origin for the most initial conditions. Also, benefiting from the prescribed performance techniques, the convergence time can eliminate the dependence of the system on designed controller parameters or initial system conditions, relying only on the actual mission requirements. In addition, we adopted a linear extended state observer to deal with the parameter uncertainties and external disturbances, and used the I&amp;I adaption to estimate the observer errors, thus further improving the system performance. Moreover, a new exponential‐type artificial potential function was designed to avoid close proximity between formation members and prevent internal collisions. Finally, numerical simulations were conducted to verify the theoretical results.

Design and implementation of an improved adaptive embedded-factor current controller for three-phase grid-connected inverter

This paper presents an improved current controller based on a series proportional integral resonant structure in synchronous reference frame in order to address low-order harmonics issue of the injected-grid current for three-phase grid-connected inverter. Additionally, to improve dynamic performance, an adapted factor is embedded into the proposed controller structure. Moreover, to design the parameters of the proposed controller, different constraint conditions are investigated. Based on the proposed controller structure and parameters design method, satisfactory steady-state and faster dynamic performances are both obtained in comparison to those of the conventional parallel proportional integral resonant controller. What's more, the proposed controller is easy to implement in practice. Simulation and experimental results are finally given to verify the effectiveness and superiority of the proposed controller.

Exploring the role of process control and catalyst design in methane catalytic decomposition: A machine learning perspective

Methane catalytic pyrolysis presents a promising avenue for producing CO2-free hydrogen. However, optimizing catalyst composition and process parameters through experimental studies has been resource-intensive. Machine learning techniques have been increasingly utilized to deepen the understanding of process mechanisms. In this study, machine learning models were developed to deepen the understanding of the effects of process control and catalyst design on CH4 conversion performance. The optimal CH4 conversion model achieved a testing R2 of 0.9999. To further assess the model's real-world applicability, an initial verification experiment was conducted using a catalyst composition and process parameters distinct from those in the dataset. The results reaffirmed the model's good accuracy in real-world scenarios. Delving deeper into the intricacies of methane pyrolysis, the Shapley values and partial dependence plots generated by the model were scrutinized and discussed to provide a new perspective on the influence of process control and catalyst design parameters on methane catalytic decomposition.

Aerodynamic modeling and performance analysis of model predictive controller for fixed wing vertical takeoff and landing unmanned aerial vehicle

This research focuses mainly on the aerodynamic modelling and performance analysis of a model predictive controller for a hybrid fixed-wing vertical takeoff and landing of unmanned aerial vehicle. The aerodynamics, which comprises several aerodynamic characteristics including the lift, drag, and thrust coefficients, is modelled using Newton’s second law of motion. The force and moment equations were obtained, and they were then converted into matrix and state equation form, together with the transformation matrices. The essential equations with six degrees of freedom (6 DoF) and 12 state matrices including the control input and manipulating variables were obtained. The proposed model predictive controller (MPC) was designed using the optimal model predictive controller design parameters, such as a sampling time of 0.1, a prediction horizon of 15, a control horizon of 3 and additional controller settings. With a settling period of 3 s, an overshoot of 0.5865, and a steady state inaccuracy of 0.00785, the MATLAB simulation demonstrates that the system variables, including roll, pitch, and yaw, are stabilized. This MPC control is more effective in anticipating and optimizing the UAV than other control strategies. Eventually, the controller’s simulation on MATLAB Simulink demonstrates the controller’s ability to stabilize and control the system in a real-time application. Autonomous vertical takeoff and landing operations depend heavily on the mathematical model and architecture of the flight controller. Among the uses are inspection, monitoring, and rescue.

Innovative hierarchical control of multiple microgrids: Cheetah meets PSO

The expansion of microgrids (MGs) comes from the increasing integration of renewable energy resources (RES) and energy storage systems (ESs) into distribution networks. However, effective integration, coordination, and control of Multiple Microgrids (MMGs) during energy transition from microgrid to grid, and vice versa presents a formidable challenge. This arises from the necessity to ensure smooth operation, coordination, and control of MMGs for stability and efficiency during transitions. The dynamic operation of MMGs due to intermittent renewable energy in microgrids and load variations presents an additional challenge for hierarchical control techniques. This paper provides an Artificial Intelligent (AI)-based control technique and introduces an innovative hybrid optimization technique, denoted as HYCHOPSO, derived from the fusion of Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques, and the quantitative findings of extensive benchmark testing, constitute key aspects of novelty. The strategic choice of this novel optimization technique, coupled with a Proportional-Integral (PI) controller, provides key insights. HYCHOPSO is tested within benchmark functions and reaches its optimal score before 50 iterations as compared with optimization algorithms namely CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO which reaches after approximately 200 iterations. HYCHOPSO consistently exhibits the lowest mean values, indicating its robust convergence capabilities. The proposed control technique results in a significant reduction of the microgrid's Current Total Demand Distortion to 1.1%, meeting acceptable limits for a 600 V-bus According to IEEE519–2014 standard, even under nonlinear loads. Additionally, precise regulation of voltage and frequency is achieved with0.19%−+ deviations and a frequency overshoot of 1.9%. Furthermore, optimization contributes to seamless energy transition from microgrid to grid, achieving synchronization in less than 5 s. This enhances the real application's reliability, flexibility, scalability, and robustness. Furthermore, HYCHOPSO is introduced as an AI-based technique to facilitate control parameter design under dynamic conditions, with substantial simulation cases demonstrating control feasibility