Control Parameters Research Articles

Research on aerodynamic characteristics and control method of rigid-flexible variable camber wing.

In order to realize the continuous chord bending of the wing and consider the material deformation limitation, a trailing-edge curvature variable wing section combining rigid and flexible structures is proposed. In the wing configuration design, the optimal lift-to-drag ratio is used as the optimization objective, and the wing section mean line is parameterized to obtain the optimal rigid-flexible hybrid deformation configuration. The aerodynamic characteristics of hybrid rigid-flexible deflection wing and traditional rigid deflection wing were compared by flow field calculation. Under different angles of attack, the hybrid deflection airfoil has better aerodynamic performance, with the lift coefficient increasing by up to 1.66 times and the lift-to-drag ratio increasing by up to 2.86 times. Under various flight conditions, the rigid-flexible hybrid wing requires a smaller deflection angle and a better wing configuration than the traditional wing. Under high-angle deflection conditions such as landing, the lift-to-drag ratio of the rigid-flexible hybrid wing is increased by 78%, while delaying flow separation (x/c = 0.8) and reducing trailing-edge vortices, thereby improving aerodynamic efficiency. Additionally, for the flexible deformation part in the rigid-flexible hybrid wing, pneumatic muscles are used as flexible actuators, and a proportional integral sliding mode control method based on a nominal model is established. A test platform was built to control the deformation of the flexible part of the wing rib, and the ability of the flexible part to achieve the target curvature within the range of elastic deformation was verified. The actual deformation curve is very consistent with the target deformation curve. The dynamic performance test of the control method and parameters proves the rationality and effectiveness of the control system design.

Multidirectional coupling vibration characteristics and optimal damping control of superconducting electrodynamic levitated system for high-speed maglev transport

ABSTRACT Utilizing magnetic forces between the on-board superconducting magnets and the 8-shaped coils at ground, the superconducting electrodynamic suspended (EDS) train would achieve large levitation gap, good route adaptability, and ultra-high speed cruising potential. Meanwhile, the integrated levitation and guidance system brings the multidirectional motion coupling effects and has negative damping characteristics in high-speed operation scenarios, such that they pose severe challenges to running stability and vibration attenuation of the levitation bogie. In order to understand multidirectional coupling vibration characteristics and explore a concise and high-efficiency electromagnetic damping method for the levitation bogie, firstly, a magnetic-electric-mechanical interaction (MEMI) model is established considering six degrees of freedom of superconducting magnet, and then it is embedded into a three-dimensional dynamic model of single levitation bogie. Secondly, the free vibration responses of the single bogie under various initial excitations are analysed to illustrate the coupling relationship between motions in different directions, and then a grouping-control strategy of the active damping coils is proposed for suppressing the multidirectional vibration of the bogie. Thirdly, the damping characteristics of EDS system under different state feedback controls are compared from the perspective of motion power, and an optimal voltage control method of the active damping coils using vertical acceleration-velocity combined feedback is proposed. Finally, the variation laws of vibration attenuation of the bogie and power consumptions of the damping coils with the velocity and acceleration proportional coefficients under the step deviations of guideway are discussed, and then the optimal values of the control parameters under different power limits are given.

Physical Implementation and Experimental Validation of the Compensation Mechanism for a Ramp-Based AUV Recovery System

In complex marine environments, ramp-based recovery systems for autonomous underwater vehicles (AUVs) often encounter engineering challenges such as reduced docking accuracy and success rate due to disturbances in the capture window attitude. In this study, a desktop-scale physical experimental platform for recovery compensation was designed and constructed. The system integrates attitude feedback provided by an attitude sensor and dual-motor actuation to achieve active roll and pitch compensation of the capture window. Based on the structural and geometric characteristics of the platform, a dual-channel closed-loop control strategy was proposed utilizing midpoint tracking of the capture window, accompanied by multi-level software limit protection and automatic centering mechanisms. The control algorithm was implemented using a discrete-time PID structure, with gain parameters optimized through experimental tuning under repeatable disturbance conditions. A first-order system approximation was adopted to model the actuator dynamics. Experiments were conducted under various disturbance scenarios and multiple control parameter configurations to evaluate the attitude tracking performance, dynamic response, and repeatability of the system. The results show that, compared to the uncompensated case, the proposed compensation mechanism reduces the MSE by up to 76.4% and the MaxAE by 73.5%, significantly improving the tracking accuracy and dynamic stability of the recovery window. The study also discusses the platform’s limitations and future optimization directions, providing theoretical and engineering references for practical AUV recovery operations.

Investigation and optimization of machining peculiarities of heat treatment and non-heat treatment X40CrMoV51 alloy by powder-suspended electro-discharge machining in final-finishing operation

X40CrMoV51 alloy in the heat-treated (HT) and non-heat-treated (NHT) states processed by electro-discharge machining (EDM) with suspended tungsten compound powder has been rarely investigated. Hence, this study explored and compared the effect of control parameters, including peak-current (I p ), pulse-on time (T on ), and powder amount (A p ) on material removal rate (MRR), tool wear rate (TWR), and surface roughness (R a ) of the two material states, and finds an optimum combination of control parameters for each material state for improving the MRR, R a , and TWR. With this goal, the predictive models for MRR ht , MRR nht , TWR ht , TWR nht , R aht , and R anht were instituted, and also evaluated by the analysis of variance (ANOVA). The Grey Relational Analysis (GRA), Non-Dominated Sorting Genetic Algorithm II with Evaluated by an Area-based Method of Ranking (NSGA II-EAMR, and multi-objective particle swarm optimization with Evaluated by an Area-based Method of Ranking (MOPSO-EAMR) algorithms were executed for enhancing the MRR, R a , and TWR. As a results, the influence of control parameters on MRR, TWR, and R a for each material state is dissimilar. Regarding optimization results, the GRA produced the highest MRR value (MRR ht = 0.0236 g/min and MRR nht = 0.0223 g/min). The NSGA II-EAMR offered the lowest TWR ht and R aht , with a reduction in TWR ht of 41.37% and R aht of 14.86% compared to GRA, and with a decrease in TWR ht of 1.01% and R aht of 2.01% compared to MOPSO-EAMR. Meanwhile, the MOPSO-EAMR suggested the lowest TWR nht and R anht , with a reduction in TWR nht of 45.49% and R anht of 10.16% compared to GRA, and with a decrease in TWR nht of 4.23% and R anht of 0.44% compared to NSGA II-EAMR. Additionally, the surface metallurgical features of two material states obtained from the optimum control parameters of NSGA II-EAMR and MOPSO-EAMR were explored. The results showed that the surface defects, micro-crack acreage percentage (MCAP), composition, content, and distribution of chemical elements, distribution and density of W 2 C phase, surface topography, thickness of recast layer (TRL) in two material states are different.

Oscillations in capacitively coupled plasmas induced by nonlinear coupling between the plasma and its external circuit

Abstract In experiments, periodic oscillations in capacitively coupled plasmas (CCP) driven by a radio frequency (RF) generator can arise from various factors, one of which is the impedance mismatch due to specific system conditions. In this paper, through numerical simulations incorporating an external circuit, we demonstrate that even a highly simplified CCP circuit without any special configurations can generate oscillations caused by such mismatches, and we provide a more in-depth physical explanation of the oscillatory behavior. Our findings reveal that certain circuit component values can lead to significant oscillations in parameters such as electron density and electron temperature, with the oscillation frequency being adjustable by modifying external circuit components. The results also suggest that these oscillations stem from the nonlinear interaction between the plasma and its external circuits. Based on this, we divide the control parameters of the oscillations into two categories: (i) parameters that are part of the external circuit, e.g., the power supply frequency and the values of components in the impedance matching network (IMN). (ii) Parameters that are part of the CCP and affect its resistance and capacitance, such as the electrode spacing and electrode area of the CCP. Additionally, as the oscillations significantly undermine the system's matching performance, we also propose methods to eliminate them.

Towards a Digital Twin for Gas Turbines: Thermodynamic Modeling, Critical Parameter Estimation, and Performance Optimization Using PINN and PSO

Gas turbine (GT) modeling and optimization have been widely studied at the design level but still lacks focus on real-world operational cases. The concept of a digital twin (DT) allows for the interaction between operation data and the system dynamic performance. Among many DT studies, only a few focus on GT for thermal power plants. This study proposes a digital twin prototype framework including the following modules: process modeling, parameter estimation, and performance optimization. Provided with real-world power plant operational data, key performance parameters such as turbine inlet temperature (TIT) and specific fuel consumption (SFC) were initially unavailable, therefore necessitating further calculation using thermodynamic analysis. These parameters are then used as a target label for developing artificial neural networks (ANNs). Three ANN models with different structures are developed to predict TIT, SFC, and turbine power output (GTPO), achieving high R2 scores of 94.03%, 82.27%, and 97.59%, respectively. Physics-informed neural networks (PINNs) are then employed to estimate the values of the air–fuel ratio and combustion efficiency for each time index. The PINN-based estimation resulted in estimated values that align with the literature. Subsequently, an unconventional method of detecting alarms by using conformal prediction were also proposed, resulting in a significantly reduced number of alarms. The developed ANNs are then combined with particle swarm optimization (PSO) to carry out performance optimization in real time. GTPO and SFC are selected as the primary metrics for the optimization, with controllable parameters such as AFR and a fine-tuned inlet guide vane position. The results demonstrated that GTPO could be optimized with the application of conformal prediction when the true GTPO is detected to be higher than the upper range of GTPO obtained from the ANN model with a conformal prediction of a 95% confidence level. Multiple PSO variants were also compared and benchmarked to ensure an enhanced performance. The proposed PSO in this study has a lower mean loss compared to GEP. Furthermore, PSO has a lower computational cost compared to RS for hyperparameter tuning, as shown in this study. Ultimately, the proposed methods aim to enhance GT operations via a data-driven digital twin concept combination of deep learning and optimization algorithms.

A novel and high-efficiency dual-electron gun concurrent preheating additive manufacturing technology: suppressing thermal cracks in non-weldable IN738LC nickel-based superalloy by mitigating temperature fluctuations

ABSTRACT The process of electron beam powder bed fusion (PBF-EB) is recognised for its efficacy in preparing non-weldable nickel-based superalloys, leveraging high-temperature preheating to minimise stress-related issues. Traditionally, the PBF-EB process involves pausing the preheating scan during printing phases when only one electron gun is available, leading to significant temperature fluctuations and hot cracking during the solidification of these alloys. To address the challenge of fabricating complex parts from such materials, a novel concurrent preheating electron beam powder bed fusion (CPPBF-EB) process employing dual-electron guns has been introduced. An in-depth analysis was conducted on the crack mechanisms of PBF-EBed nickel-based superalloys. Thermal oscillations inherent to the conventional electron beam powder bed fusion (CVPBF-EB) process synergistically induce three pivotal mechanisms in IN738LC superalloys: non-equilibrium carbide precipitation, destabilisation of the γ′ strengthening phase, and stress-mediated dislocation accumulation. These coupled phenomena arise from dynamic thermal gradients during cyclic reheating, which amplify interfacial stress concentrations and enable dislocation penetration through γ′ precipitates, ultimately initiating microcrack formation at geometric heterogeneities. Crucially, we demonstrate that implementing secondary electron beam thermal stabilisation effectively suppresses deleterious phase evolution and defect generation. This breakthrough highlights thermal field modulation as a pivotal control parameter for defect-resistant additive manufacturing of precipitation-strengthened alloys.

The Influence of Personality Traits on Users’ Preference for eVTOL Control Parameters

The electric vertical takeoff and landing (eVTOL) is believed to be the future of transportation. Startup companies have proposed eVTOLs with simplified control systems. A typical example is the single-joystick control system, which controls the longitudinal, lateral and yaw movement of the eVTOL by a single joystick. However, little is known about what control parameters can meet the users’ needs of eVTOL dynamics. Thus, a flight simulator experiment with 46 participants was conducted on a six-degree-of-freedom (6-DOF) eVTOL flight simulator. We investigated how users’ personality traits can affect their preferred eVTOL control parameters, which were identified by an adaptive staircase procedure. Then, hierarchical clustering based on the preferred control parameters revealed two distinct user groups. Further, higher conscientiousness, emotional stability, and extraversion were associated with a higher preference for more sensitive control parameters. These findings highlight the importance of considering individual heterogeneity when designing the eVTOL control systems.

A New Version of the Golden Eagle Optimizer Algorithm And Its Application For Solving A Trio-Objective Skillful Team Formation Problem In A Social Network

Abstract Metaheuristic methods have demonstrated their utility in tackling global optimization problems with and without constraints. However, existing state-of-the-art (SOTA) algorithms often suffer from limitations such as premature convergence, inefficient exploration-exploitation balance, and poor adaptability to complex discrete optimization problems like Team Formation (TF). The Golden Eagle Optimizer (GEO) algorithm is a promising metaheuristic that addresses some of these challenges by effectively managing its hunting spiral motion using two control parameters: cruise (exploration) and attack (exploitation). Despite its strengths, the standard GEO algorithm requires modifications to handle the discrete and multi-objective nature of the TF problem effectively. This paper proposes an amended version of GEO, called AGEO, which integrates specialized operators to enhance its performance in TF scenarios. A skillful TF aims to form teams of experts with complementary skills in social networks (SN) while optimizing multiple objectives, including minimizing communication costs, maximizing the similarity score between team members, and achieving minimal team cardinality. AGEO preserves GEO’s powerful exploitation and exploration mechanisms while introducing tailored operator strategies to overcome the challenges inherent in TF. The AGEO undergoes testing on several well-established benchmark datasets, including Universiti Malaysia Pahang (UMP), Internet Movie Database (IMDB), Association for Computing Machinery (ACM), and Database Systems & Logic Programming (DBLP). Additionally, a comparative study against SOTA metaheuristic algorithms such as Particle Swarm Optimization (PSO), Butterfly Optimization Algorithm (BOA), Crow Search Algorithm (CSA), and Jaya Algorithm demonstrates AGEO’s superior performance in forming highly optimized teams with the least communication cost, lowest team cardinality, and highest similarity score.

Photometric Diagnostic of the Sheath Dynamics and Electron Density Trends in Single and Dual Frequency CCRF Argon Plasmas

ABSTRACTA methodological study is presented on a noninvasive photometric diagnostic for determining trends in electron density and sheath dynamics in single (1f) and a dual (2f) radio frequency capacitively coupled (CCRF) argon plasmas. The 1f discharges are operated at 13.56 and 27.12 MHz, respectively, while the 2f discharge is driven by both frequencies simultaneously with a variable phase angle between them. A camera is used to measure the sheath extension in front of the powered electrode at different gas pressures and RF‐amplitudes. It was found that sheath thicknesses decrease with rising pressure and increase with frequency. The 2f sheath behavior reflects the influence of both driving frequencies. By fitting the Child–Langmuir law to the sheath width data, electron densities are estimated and compared to Langmuir probe measurements. The results indicate that the photometric method offers only a rough approximation of electron density trends. However, the simple photometric method effectively indicates the general trends and provides valuable insight into the plasma behavior, especially regarding the influence of pressure and RF‐power. In the 2f discharge, the results show a strong dependence of the DC‐self‐bias on the phase variance, as well as pronounced asymmetries in the sheath around , which can be attributed to asymmetric ionization behavior. These findings demonstrate the potential of the simple photometric approach for investigating the interplay between plasma parameters in CCRF discharges and illustrate internal plasma characteristics and controllable process parameters such as pressure, power and phase.

Modeling land cover changes using an enhanced Markov-future land use simulation model with spatial distribution considerations: a case study in the Yellow River Basin

ABSTRACT The traditional Markov-future land use simulation (FLUS) model for land use prediction primarily emphasizes the quantity changes and spatial distribution of land use types, but it neglects the influence of their inherent spatial characteristics on the prediction precision. This study introduces an innovative approach by designing shape control parameters and developing an enhanced Markov-FLUS model. The model integrates artificial neural networks to capture the relationships between the land use type occurrence probability and driving factors. It incorporates common points, common edges, distance, and aggregation parameters alongside a cellular automata model. Taking the Yellow River Basin as an example for analysis, this study compares the model's simulation performance before and after enhancement, focusing on the land use types with the most and least improvement. The results indicate that the refined model achieves a superior fitness function value in simulating land use within the Yellow River Basin. HIGHLIGHTS The Enhanced Markov-FLUS model has been meticulously constructed through the strategic design of a comprehensive set of shape control parameters. The refined model demonstrates significantly elevated fitness values, particularly within a 3 × 3 Moore neighborhood framework. This model stands out in accurately forecasting land use types characterized by enhanced contiguity and more regularized shapes. The improved model significantly diminishes the miss rate, notably along the edges of land type boundaries.

Association of thyroid function within the normal range with glycemic control and cardiovascular risk factors in type 1 diabetes

ABSTRACT Background Thyroid hormone (TH) variations, even within the normal range, influence cardiometabolic health. In type 1 diabetes (T1D), optimizing glycemic control and cardiovascular risk is crucial to prevent complications. We aim to explore the association of TH within normal range with cardiometabolic profile in T1D. Research Design and Methods Cross-sectional analysis including adult patients with T1D followed at our Endocrinology Department between 2022-2024. We excluded patients with TSH or fT4 outside the reference range. Associations between TH (TSH, fT4, fT3 and fT3/fT4 ratio) and glycemic, anthropometric, and cardiometabolic parameters, were evaluated using linear regression models unadjusted and adjusted for relevant variables. Restricted cubic splines between TH and glycemic control parameters were performed to assess non-linear associations. Results We included 296 patients (median age 35 years, 42.6% female). Patients with mid-range TSH had better glycemic control, with higher time in range and a lower time above range. Waist circumference and body mass index were negatively associated with fT4, and positively with fT3 and fT3/fT4 ratio. Estimated glomerular filtration rate was negatively associated with TSH and fT4, and positively with fT3 and fT3/fT4. Conclusions Variations in TH within the normal range were associated with glycemic control and cardiovascular risk factors in T1D.

Mid-Term Results of Renal Function in Living Kidney Donors in a Single Center.

Mid-Term Results of Renal Function in Living Kidney Donors in a Single Center.

Transient Voltage Stability Analysis of the Dual-Source DC Power System

This paper analyzes the transient voltage stability of the dual-source DC power system. The system’s equivalent model is first established. Subsequently, the effect mechanisms of line parameters and voltage-source rectifiers’ current control inner loops on the system’s transient voltage instability are investigated. It indicates that these factors reduce the power supply capacity of the source, increasing the risk of transient instability in the system. Then, considering the influence of fault depths, the influence of different large disturbances on the transient voltage stability is investigated. Furthermore, the critical cutting voltage and critical cutting time for DC power systems are determined and then validated on the MATLAB R2023b/Simulink platform. Finally, based on the mixed potential function theory, the impact of system parameter variations on stability boundaries is analyzed quantitatively. Simulation verification is conducted on the MATLAB R2023b/Simulink platform, and experimental verification is conducted on the RT-LAB Hardware-in-the-Loop platform. The results of the quantitative analysis and experiments corroborate the conclusions drawn from the mechanistic analysis, underscoring the critical role of line parameters and converter control parameters in the system’s transient voltage stability.

PID Based Artificial Neural Network For Rotary Dryer In The Fertilizer Industry

Rotary dryers are often used to dry materials in fertilizer industry. The drying process using a rotary dryer may occasionally encounter issues, such as an uneven distribution of hot air and overheating, which can be caused by operator input errors or control parameter adjustments that are not optimal. The conventional proportional-integral-derivative (PID) controller is a common method for regulating temperature in rotary dryers. However, it is not particularly effective in dealing with changes that occur in real time, which can result in extending the system’s transient period before equilibrium and overshoot for temperature output in rotary dryers. One potential solution that can be offered is to integrate PID control with an Artificial Neural Network (ANN) which would enable the system to adapt to the dynamics of the operational environment without operator assistance. The methodology used is backpropagation neural networks, trained with empirical data gathered during the operation of a rotary dryer. The output from the ANN model is then used to adjust the PID controller within the system in order to prevent extending the system’s transient period before equilibrium and overshoot. Backpropagation is used because this algorithm can effectively reduce errors when recognizing data patterns. The control design aims to improve the efficiency of the drying process, optimizing it, and reducing costs associated with production

Optimizing control strategies for DC-DC boost converters: Real-time application of an adaptive gain scheduled ISA-PI controller with hybrid state-space and linear parameter-varying modelling

This paper introduces an innovative sophisticated control scheme for a DC-DC boost converter (DCBC), employing an adaptive gain scheduled ISA-PI controller. Addressing the inherent non-minimum phase behaviour arising from a right-half plane zero and the complexities associated with nonlinear dynamics during continuous conduction mode (CCM), the proposed adaptive gain scheduled ISA-PI controller incorporates the distinct adjustable parameter within the controller structure. This parameter is instrumental in enhancing the adaptability of the controller to varied operating conditions. The adaptive ISA-PI controller seamlessly integrates the real-time duty cycle value, replacing traditional tuning variables with precision. The dynamic adjustment of this sole controllable parameter is facilitated through a carefully designed look-up table, employing the loop-shaping method. Verification of the proposed control system’s effectiveness is conducted using MATLAB/Simulink, incorporating a comprehensive comparative analysis against single proportional integral (PI) controllers. The assessment centres on evaluating the system’s precision in tracking desired signals and regulating plant process variables with optimal efficiency, minimizing delays and overshoot. Experimental validation is further undertaken using MATLAB/Simulink/Stateflow on a dSPACE Real-time-interface (RTI) 1007 processor, DS2004 High-Speed A/D, and CP4002 Timing and Digital I/O boards. The experimental results confirm the superior performance of the proposed adaptive gain schedule ISA-PI controller, which has a unique configurable parameter. This controller demonstrated a twofold improvement in tracking speed and significantly improved disturbance rejection, confirming its effectiveness.

Classifying destructive quantum interference in molecular junctions: Toward molecular quantum rulers.

Destructive quantum interference in molecular junctions might be used to build molecular quantum rulers, allowing us to quantify changes in external control parameters electrically. For this reason, it is important to understand which patterns of destructive quantum interference can occur inside the electronic excitation gap of a molecule coupled to conducting electrodes. By considering a four-level model, we show that much more complex destructive quantum interference behavior can arise than expected for just two levels. We classify the destructive quantum interferences analytically and show that they may even occur in regions forbidden by the standard orbital rule for electron transport. Our results suggest that appropriate molecular design may indeed allow us to construct highly sensitive molecular quantum rulers.

Hybrid fuzzy logic–PI control with metaheuristic optimization for enhanced performance of high-penetration grid-connected PV systems

This paper introduces a hybrid fuzzy logic control-based proportional-integral (FLC-PI) control strategy designed to enhance voltage stability, power quality, and overall performance of central inverters in photovoltaic power plants (PVPPs). The study is based on a real-world PVPP with an installed capacity of 26.136 MWp, connected to the Egyptian national grid at Fares City, Kom Ombo Centre, Aswan Governorate. A user-friendly MATLAB/SIMULINK environment is developed, incorporating eleven distinct blocks along with a modelled national utility grid, utilizing actual operational data from the PVPP. To optimize the FLC-PI control scheme, several artificial intelligence (AI)-based metaheuristic optimization techniques (MOTs) are employed to simultaneously tune all control parameters—namely Grey Wolf Optimization (GWO), Harris Hawks Optimization (HHO), and the Arithmetic Optimization Algorithm (AOA)—are employed. These techniques are used to simultaneously fine-tune all the gain parameters of FLC-PI control, based on four standard error-based objective functions: Integral Absolute Error (IAE), Integral Square Error (ISE), Integral Time Absolute Error (ITAE), and Integral Time Square Error (ITSE). The optimized gains are applied to both voltage and current regulators of the central inverters, enabling the identification of optimal values. Among the tested methods, the HHO algorithm combined with the ISE objective function delivered the best performance, achieving a total harmonic distortion (THD) of 3.88%—well below the IEEE 519–2014 limit of 5.00%. The results confirm that the proposed FLC-PI controller significantly enhances the integration of high-penetration PVPPs into the utility grid by reducing power losses and inverter-induced harmonics, especially during maximum power point tracking (MPPT). Moreover, employing MOTs for controller tuning proves to be an effective solution for adapting to dynamic solar irradiance conditions. Ultimately, the optimized FLC-PI control approach enhances voltage stability, improves power quality, and boosts the overall efficiency of grid-connected PV systems.

Dual-loop optimization of digital accelerometers based on advanced dual-degree-of-freedom Smith predictor.

Dual-loop optimization of digital accelerometers based on advanced dual-degree-of-freedom Smith predictor.

M-Race: A Racing Algorithm for the Tuning of Meta-Heuristics Based on Multiple Performance Objectives

The performance of meta-heuristic algorithms on optimisation problems depend on the values of control parameters. These parameters greatly influence the behaviour of algorithms and affect the quality of the solutions. In order to optimise an algorithm for a specific problem set, a structured approach is followed to carefully select the appropriate control parameters. This approach is called control parameter tuning. Most existing tuning approaches focus on tuning an algorithm based on only one performance objective, such as accuracy or convergence speed. However, these objectives often work against each other, and improving the algorithm based on one objective can worsen the performance based on another objective. For example, obtaining a more accurate solution generally requires the algorithm to run for a longer time. The goal of this research is to develop a tuning approach that takes multiple performance objectives into account when tuning the control parameters of a meta-heuristic. The result of the tuning algorithm presents the experimenter with multiple values for control parameters, each representing different trade-offs between the various objectives. Experimental results demonstrate that M-race successfully discovered between 9 and 15 non-dominated parameter configurations across benchmark functions for both particle swarm optimisation (PSO) and differential evolution (DE) algorithms. These non-dominated parameter configurations represent balances among the tuning objectives used.