Multi-input Multi-output Research Articles

Mathematical Techniques in the Design of Robust Control Systems

Solid control frameworks are exceptionally imperative for making beyond any doubt that energetic frameworks remain steady and work well in a parcel of diverse circumstances and with a parcel of questions. This diagram tells you everything you wish to know about the numerical strategies utilized to form steady control systems. Strong control tries to bargain with issues like unknowns within the system model, stuns from the exterior, and changes within the system's parameters. It does this to form beyond any doubt that the framework keeps working well and remaining steady in spite of these issues. Lyapunov soundness hypothesis and the H-infinity (H∞) optimization strategy are two of the foremost critical arithmetic apparatuses. Lyapunov steadiness hypothesis gives us a way to see at how steady a framework is, and H∞ minimizes the worst-case pick up from unsettling influence to yield. State-space representations are moreover utilized to show and ponder multi-input multi-output (MIMO) frameworks, which makes control plan more organized. When making controls that guarantee steady execution, strategies like Direct Framework Disparities (LMIs) and Riccati conditions are utilized. The thought of unwavering quality limits is additionally displayed to degree how well the framework can handle factors. The solid control plan incorporates frequency-domain apparatuses, such as the Nyquist and Bode plots, to see at how the framework acts at distinctive frequencies. This makes it simpler to form controllers that can bargain with high-frequency occasions. We conversation around progressed strategies like α-synthesis and strong shaft situation that can offer assistance with certain control problems in dubious settings. Down to earth illustrations appear how these scientific strategies can be used, showing how vital they are in numerous building areas, such as the airplane, vehicle, and prepare control industries. The unique emphasizes how imperative these numerical strategies are for making beyond any doubt that solid control frameworks can handle the challenges of the genuine world, which improves system security, execution, and steadfastness in a wide run of circumstances. The abstract's objective with this outline is to provide you a basic idea of the math that's critical to the field of solid control framework plan.

Adaptive Two-Degree-of-Freedom Robust Gain-Scheduling Control Strategy

This study introduces a novel tracking control strategy tailored to aeroengines, which are highly nonlinear and characterized by significant uncertainty. The proposed method entails a robust extended Kalman filter (REKF) enhanced by a forgetting factor for improved performance. An accompanying augmented, mixed onboard adaptive model based on the REKF precisely estimates and manages engine performance degradation. This advanced model effectively counters the degradation term in the perturbation block of the engine’s uncertain model. Using this strategic approach, a robust gain-scheduling controller was constructed and was found to outperform its predecessors, marking a notable advancement in control system design. Controlling twin rotor multi-input, multi-output (MIMO) systems is a highly complex process due to model uncertainties and unpredictable external disturbances. To address these challenges, we constructed an adaptive two-degree-of-freedom robust gain-scheduling controller (ATDF-RGSC) using a mixed sensitivity approach. Rigorous performance analysis confirms that this controller offers enhanced robustness, faster tracking, and more precise disturbance attenuation compared to other methods. These advanced control strategies successfully manage uncertainties and disturbances, improving performance metrics in both simulated and experimental scenarios. The proposed method may significantly enhance the safety and reliability of aeroengines and MIMO systems in practical applications.

Spatial patterns of hydroecological health in the semi-arid yellow river basin: Revelations from machine learning models

The ecosystem of semi-arid watersheds is influenced by a combination of natural climate factors, rainfall, and habitat destruction, resulting in complex mechanisms of spatial differentiation and evolution of water ecological health. Indicator selection in mainstream water ecological health assessment methods, such as the Index of Biotic Integrity (IBI), often relies on subjective reference point choices. This approach tends to overlook the comprehensive impacts and interactions among various environmental stressors. For watersheds significantly influenced by natural climatic factors, considerable uncertainties arise, leading to a lack of scientific justification for establishing water ecological health protection goals. In this study, the nonlinear capabilities of the random forest (RF) model were applied to reduce subjectivity in traditional water ecological health assessments and to more accurately reveal the emerging spatial differentiation patterns and underlying causes of water ecological health in the Wei River Basin (WRB), the largest typical semi-arid watershed of the Yellow River in China. Our findings indicate: (1) Traditional evaluation indices indicate that the overall water ecological health of the WRB is classified as sub-healthy (60 %). The core indicators include dominant species, total algal density, and the percentage of diatom density, with no significant spatial differentiation observed. (2) An improved water ecological health assessment method for semi-arid watersheds, based on the RF model, has been developed to replace traditional subjective judgment steps. This method establishes a complex multi-input–output response relationship (R2 > 0.85) between environmental stress indicators and the biological integrity index for the WRB. (3) The model results identify key driving factors affecting changes in water ecological health in semi-arid watersheds, with the sensitivity of the new model increasing nearly 11-fold compared to traditional IBI methods. (4) Following improvements, the water ecological health characteristics of the WRB exhibit significant spatial heterogeneity, with a higher dispersion coefficient (1.21), and demonstrate enhanced nonlinear response trends to climatic factors. The application of machine learning models indicates that traditional methods may underestimate the extent of ecological health degradation in watersheds and tend to oversimplify spatial heterogeneity characteristics.

Reduced model of MIMO systems using Meixner-like functions

This article investigates the use of orthogonal basis functions, particularly Meixner-like functions, to streamline and optimize the parameter complexity in multi-input multi-output (MIMO) system modeling. We introduce a new optimal model for MIMO systems, developed by decomposing the auto-regressive MIMO model with external inputs (ARX) using orthonormal Meixner-like bases. This model, referred to as the MIMO ARX Meixner-like model, enables filtering of the system’s inputs and outputs with Meixner-like functions. Parameter reduction is achieved through the optimization of Meixner-like poles using an iterative technique based on the Newton-Raphson method. The proposed model’s efficacy is demonstrated through simulations on two experimental systems: a two-degrees-of-freedom (DOF) helicopter and a two-tank process. Results reveal that the model effectively reduces approximation errors with fewer parameters. Comparative analysis with the traditional MIMO ARX model confirms the superior performance of the MIMO ARX Meixner-like model.

Optimized beamforming vector and antenna selection using autoencoder with Bayesian RNN and hybrid heuristic algorithm for MIMO systems

SummaryWireless communication systems depend on channel state information (CSI). However, the CSI collection is a significant barrier to achieving high performance. Present wireless communication models mainly rely on many antennas and related signal dispensation for increasing effectiveness. User mobility, the usage of greater frequencies, and a better number of antennas contribute to beamforming and beam management in these networks. Therefore, an improved hybrid heuristic algorithm is proposed here for providing optimized Joint beam forming and antenna selection (JBAS) in a multi‐input multi‐output (MIMO) model. The major novelty of the research work is to jointly perform the beam forming and antenna selection in MIMO that effectively enhanced the performance of the wireless transmission system. The major intention of this paper is to optimize the beamforming vectors and antennae by using the designed algorithm, where the spectral efficiency gets maximized. Initially, the data is collected by varying the number of users, locations, and sampling points, which is optimally selected by developing the hybrid reptile search‐based lion optimization algorithm (HRS‐LOA). Once it is collected, the relevant CSI is considered. This state of information is fed as input to the Auto Encoder with Bayesian Recurrent Neural Network (AE‐BRNN). Finally, through the network, the optimized value of the beamforming vector and antenna is obtained, which is useful to increase the sum rate/spectral efficiency. From the numerical results, the recommended model attained a spectral efficiency of 30 bits/s/Hz, which is higher than the BWO‐AE‐BRNN with 13%, RSO‐AE‐BRNN with 16%, RSA‐AE‐BRNN with 17%, and LA‐AE‐BRNN with 40% respectively.

Decoupled Model-Free Adaptive Control with Prediction Features Experimentally Applied to a Three-Tank System Following Time-Varying Trajectories

In this paper, the performance of three model-free control approaches on a multi-input, multi-output (MIMO) nonlinear system with constant and time-varying references is compared. The first control algorithm is model-free adaptive control (MFAC). The second is a modified version of MFAC (MMFAC) designed to handle delays in the system by incorporating the output error difference (over two sample time steps) in the control input. The third approach, model-free adaptive predictive control (MFAPC) with a one-step-ahead forecast of the system input, is obtained by using predictions of the outputs based on the data-based linear model. The experimental device used is an MIMO three-tank system (3TS) assumed to be an interconnected system with multiple coupled single-input, single-output (SISO) subsystems with unmeasurable couplings. The novelty of this contribution is that each coupled SISO partition is assumed to be controlled independently using a decoupled control algorithm, leading to fewer control parameters compared to a centralized MIMO controller. Additionally, both parameter tuning for each controller and performance evaluation are conducted using an evaluation criterion considering energy consumption and accumulated tracking error. The results demonstrate that almost all the proposed model-free controllers effectively control an MIMO system by controlling its SISO subsystems individually. Moreover, the predictive features in the decoupled MFAPC contribute to more accurate tracking of time-varying references. The utilization of tracking error differences helps in reducing energy consumption.

Identification methodology for MIMO Hammerstein nonlinear model with process noise

In this paper, we present a methodology for identifying the multi-input–multi-output (MIMO) Hammerstein nonlinear model under colored noise. The Hammerstein model presented comprises neural fuzzy models (NFMs) as its static nonlinear block and rational transfer functions (RTF) model as its dynamic linear block. The hybrid signals consisting of separable signals and random signals are utilized to deal with the MIMO Hammerstein model identification issue, and the separable signals to implement separation identification of MIMO Hammerstein model is introduced, that is, the two blocks are separately identified. First, parameters of the linear block are estimated applying correlation function-based least squares method in the presence of measurable input–output of Gaussian signals, which can efficiently weaken the process noise interference. Second, estimate of noise parameters vector is introduced to solve the unknown noise vector in the information matrix, then a recursive extended least squares method is developed for identifying parameters of nonlinear block and colored noise model based on available input–output of random signals. The validity and precision of the presented methodology are demonstrated applying a numerical simulation and a nonlinear process, and it is known from the research results that compared with existing identification techniques, the methodology utilized achieved higher identification accuracy.

Inverse system based decoupling design and control strategy for crude oil heat exchanger networks

This work focuses on a decoupling control approach for the crude oil heat exchanger network to improve the energy efficiency and achieve continuous energy saving. The desired control variables are identified by the signed directed graph (SDG) model based on the complex network theory. Then the principal component analysis (PCA) method and the evaluation index are employed to construct the initial controller as well as the control variables pairing. The presented system is a multi-input multi-output (MIMO) control system where the coupling problems are considered, and a decoupling control strategy based on the inverse system theory of neural network is proposed. The existence of the inverse system is proved by the reversibility derivation, and the original system is decoupled into two second-order subsystems. Consequently, the proposed decoupling control system is tested on the example of heat exchanger network (HEN) belonging to Crude Distillation Unit. The fluctuating value of temperature is reduced averagely by 57.1% compared with the non-decoupling control strategy and the mean absolute percentage error is within 0.02 under different operating conditions. The results show that the proposed decoupling control method has excellent decoupling performance and can improve the control performance of the system.

A low-complexity solution for LMIs of hardly constrained nonlinear MIMO tracking control systems

This research is dedicated to the discrete-time tracking control of nonlinear multi-input multi-output (MIMO) Newtonian mechanics systems with an equal number of inputs and outputs, in the presence of input saturation and high-order dynamics of the actuation system. Firstly, the continuous-time model of the plant and actuation system has been transformed into the discrete-time domain using Adams–Bashforth and Adams–Moulton discretization techniques. In the next step, considering saturation boundaries, the modified position and velocity references are derived using the discrete-time reaching law-based control approach. These modified references are obtained through an optimization algorithm that calculates the closest achievable position and velocity trajectories with respect to the desired reference trajectory. Modifying desired trajectories in MIMO systems results in the formation of a system of linear matrix inequalities (LMIs). Solving such systems of inequalities requires the use of complex methods, and this difficulty increases with the increase in the system’s degrees of freedom (DOFs). The proposed strategy offers a more straightforward approach to solving these inequalities. Once the modified references are calculated, the corresponding position and velocity control signals can be obtained using the mentioned reaching law strategies. The final step of the proposed algorithm is to combine the resulting position and velocity control signals using a Gaussian-weighted averaging method to form the main control signal. A desirable performance for the control system can be achieved by appropriately adjusting the standard deviations of the Gaussian weighing functions. To evaluate the performance characteristics of the proposed method, it has been applied in the tracking control procedure of a nonlinear 3-DOF mechanical system. The numerical simulation results demonstrate the effectiveness of the method.

Robust Fuzzy Adaptive Fault‐Tolerant Control for a Class of Second‐Order Nonlinear Systems

ABSTRACTIn this brief, an optimal active fuzzy fault‐tolerant control (OAFFTC) scheme is proposed for a class of unknown perturbed multi‐input multi‐output (MIMO) nonlinear systems with nonaffine nonlinear actuator faults and time‐varying sensor faults. The control strategy can handle automatically (online updating) with three additives (bias, drift, and loss of accuracy) along with one multiplicative (loss of effectiveness) sensor faults and nonlinear state‐dependent actuator faults. In order to deal with uncertain system dynamics, sensor and actuator faults, and external disturbances, fuzzy systems (FSs) and backstepping techniques were combined to provide the adaptive control term as well as a robust control term. Butterworth filter is used to get rid the algebraic loop problem. The suggested robust term, can deal with approximation errors of the fuzzy systems (FSs). To automatically optimize the adaptive parameters and the starting conditions, particle swarm optimization (PSO) approach is introduced. The Lyapunov approach is employed to demonstrate the closed‐loop system's stability. To assess the efficacy and correctness of the suggested scheme, quadrotor dynamic model is performed on the simulation part.

Impedance modeling and quantitative stability analysis of grid-connected voltage source converters under complex unbalanced conditions

The stability issues caused by voltage source converters (VSCs), which are commonly used in renewable energy systems, are investigated in this paper. Much literature has investigated the impedance models and quantitative stability analysis for converters under the weak and unbalanced grid. However, on the one hand, harmonic-transfer-function (HTF)-based modeling methods are complex, and the established infinite-order models may suffer from unreasonable truncation. On the other hand, these existing impedance models without considering all unbalance factors are inaccurate. Both of these may bring wrong stability analysis results. Therefore, this paper proposes a simple impedance extension method and considers all unbalance factors to derive the impedance model of the converter. However, due to the established higher-order impedance model, the traditional impedance-ratio based stability analysis method is not applicable to multi-input multi-output (MIMO) systems. The commonly used eigenvalue-based GNC and diagonalization-based methods applicable to MIMO systems are cumbersome and not suitable for quantitative analysis. Therefore, in this paper, a quantitative analysis tool of system stability based on the phase-frequency characteristics of determinants is developed. Then, stability regions in the multi-parameter space are obtained. Finally, the established impedance model and the quantitative analysis results are validated via both simulations and hardware experiments.© 2017 Elsevier Inc. All rights reserved.

Wideband CPW fed four port MIMO antenna based microwave sensor for deep brain temperature measurement

This study presents a wideband CPW fed four-port multi-input multi-output (MIMO) antenna-based microwave sensor for measuring deep brain temperature inside human head. The range of frequency is from 5.45 to 8.56 GHz at the resonance frequency of 7.25 GHz. Coplanar Waveguide (CPW) fed approach on a low-cost FR4 substrate is used in designing the planar antenna. The presented antenna is compact, having overall dimensions of 40 × 40 × 1.6 mm3. In the operational frequency range, the obtained diversity performance for MIMO antenna shows Envelope Correlation Coefficient (ECC) < 0.05, Diversity Gain > 9.95, total active reflection coefficient (TARC ) < −13 dB and channel capacity loss (CCL) < 0.125 bits/S/Hz. For deep brain temperature measurements, the proposed sensor provides electric field of 3.92 V m−1 and specific absorption rate (SAR) value of 0.16 W kg−1 at the 9 mm distance inside the human head Phantom. The proposed sensor is capable of measuring 0.1 °C change in temperature at 10 W of input power for 60 s when all four ports are active.

Suppression of Combustion Oscillations in Hydrogen-Enriched Can-Type Combustors Through Fuel Staging

Abstract To achieve decarbonization in power-generating gas turbines, the technology of mixing hydrogen with natural gas is garnering significant attention. However, when blending natural gas with hydrogen, the altered combustion characteristics can lead to combustion instability in gas turbine combustors. Although fuel staging can effectively suppress combustion instability for can-type combustors, further research on mitigation strategies for hydrogen cofiring and their predictive methods is required. This study involves hydrogen cofiring experiments using a full-scale can-type combustor. Moreover, the resulting suppression of combustion instability is analyzed through fuel staging by utilizing three-dimensional (3D) computational fluid dynamics (CFD) and one-dimensional (1D) thermo-acoustic analysis. The experiments used a full-scale industrial can-type combustor with a five-around-one nozzle configuration. Hydrogen was blended with natural gas up to a volume fraction of 30%, maintaining a constant thermal power. Fuel staging was applied by controlling two out of five outer nozzles (ONs) along with the remaining three. Before the 1D thermo-acoustic analysis, the internal flame structure of the combustor was examined through 3D CFD analysis. Based on the results, a multi-input multi-output (MIMO) system was constructed for 1D thermo-acoustic analysis of the can-type combustor. The application of time delays derived from 3D CFD analysis to the 1D model revealed that differences in flame time delays across the nozzles cause combustion instability suppression observed in fuel staging.

Anti Wind‐Up and Robust Data‐Driven Model‐Free Adaptive Control for MIMO Nonlinear Discrete‐Time Systems

ABSTRACTThis article addresses a solution to one of the main challenges of online data‐driven control (DDC) methods: reducing the sensitivity of the model‐free adaptive control (MFAC) method to initial conditions and control parameters with the new control cost function and added the output error rate and integral along with a new anti‐wind up strategy for multi‐input multi‐output (MIMO) systems. The parameters introduced to the new control law have been validated using the boundary‐input boundary‐output (BIBO) approach to design and converge the controller. The simulation findings on a nonlinear auto‐regressive moving average model with exogenous inputs (NARMAX) system with triangular control input demonstrate that the proposed control rule will outperform to prototype MFAC. Furthermore, to analyze the sensitivity of the controller to the initial conditions and the uncertainties of the control parameters, 30 Monte Carlo simulations were performed with random initial conditions in the presence of disturbance in the control input, and output noise, and the results were compared with the prototype MFAC and conventional PID controller using standard criteria such as integral time absolute error, standard deviation, steady‐state error, and mean maximum error, which shows a noticeable superiority of proposed controller relative to the prototype MFAC.

Unimodular Multi-Input Multi-Output Waveform and Mismatch Filter Design for Saturated Forward Jamming Suppression.

Forward jammers replicate and retransmit radar signals back to generate coherent jamming signals and false targets, making anti-jamming an urgent issue in electronic warfare. Jamming transmitters work at saturation to maximize the retransmission power such that only the phase information of the angular waveform at the designated direction of arrival (DOA) is retained. Therefore, amplitude modulation of MIMO radar angular waveforms offers an advantage in combating forward jamming. We address both the design of unimodular MIMO waveforms and their associated mismatch filters to confront mainlobe jamming in this paper. Firstly, we design the MIMO waveforms to maximize the discrepancy between the retransmitted jamming and the spatially synthesized radar signal. We formulate the problem as unconstrained non-linear optimization and solve it using the conjugate gradient method. Particularly, we introduce fast Fourier transform (FFT) to accelerate the numeric calculation of both the objection function and its gradient. Secondly, we design a mismatch filter to further suppress the filtered jamming through convex optimization in polynomial time. The simulation results show that for an eight-element MIMO radar, we are able to reduce the correlation between the angular waveform and saturated forward jamming to -6.8 dB. Exploiting this difference, the mismatch filter can suppress the jamming peak by 19 dB at the cost of an SNR loss of less than 2 dB.

DBFact applied to minimum variance performance assessment for nonminimum phase multivariate systems from closed‐loop data

AbstractThis paper introduces an approach for determining a minimum variance control (MVC) benchmark for nonminimum phase (NMP) multi‐input multi‐output (MIMO) systems using closed‐loop operational data. The MVC benchmark is derived from the MVC law of DBFact factorization introduced by Lima, Trierweiler, and Farenzena. Unlike other factorization methods, DBFact offers advantages such as non‐iterative computation and ensuring internal stability of the MVC law. This approach considers the inherent directionality of NMP MIMO systems, enhancing the reliability of the control performance index. However, the original method relies on prior knowledge of the process model. To overcome this limitation, this paper proposes a method for calculating the MVC benchmark when prior knowledge is absent. It introduces a MIMO system identification strategy employing minimally invasive signal tests. The methodology is evaluated across various control conditions using a quadruple‐tank plant with additional time delays. The study emphasizes the importance of directionality in assessing MIMO system performance, particularly in evaluating individual loop performances. Results demonstrate the identification procedure's effectiveness in accurately calculating the proposed MVC benchmark, even with a mere 1% increase in output variance considered.

Fixed-Time Fault-Tolerant Adaptive Neural Network Control for a Twin-Rotor UAV System with Sensor Faults and Disturbances

This paper presents a fixed-time fault-tolerant adaptive neural network control scheme for the Twin-Rotor Multi-Input Multi-Output System (TRMS), which is challenging due to its complex, unstable dynamics and helicopter-like behavior with two degrees of freedom (DOFs). The control objective is to stabilize the TRMS in trajectory tracking in the presence of unknown nonlinear dynamics, external disturbances, and sensor faults. The proposed approach employs the backstepping technique combined with adaptive neural network estimators to achieve fixed-time convergence. The unknown nonlinear functions and disturbances of the system are processed via an adaptive radial basis function neural network (RBFNN), while the sensor faults are actively estimated using robust terms. The developed controller is applied to the TRMS using a decentralized structure where each DOF is controlled independently to simplify the control scheme. Moreover, the parameters of the proposed controller are optimized by the gray-wolf optimization algorithm to ensure high flight performance. The system’s stability analysis is proven using a Lyapunov approach, and simulation results demonstrate the effectiveness of the proposed controller.

An efficient parallel self-attention transformer for CSI feedback

In massive multi-input multi-output (MIMO) systems, it is necessary for user equipment (UE) to transmit downlink channel state information (CSI) back to the base station (BS). As the number of antennas increases, the feedback overhead of CSI consumes a significant amount of uplink bandwidth resources. To minimize the bandwidth overhead, we propose an efficient parallel attention transformer, called EPAformer, a lightweight network that utilizes the transformer architecture and efficient parallel self-attention (EPSA) for CSI feedback tasks. The EPSA expands the attention area of each token within the transformer block effectively by dividing multiple heads into parallel groups and conducting self-attention in horizontal and vertical stripes. The proposed EPSA achieves better feature compression and reconstruction. The simulation results display that the EPAformer surpasses previous deep learning-based approaches in terms of reconstruction performance and complexity.

A robust OFDM IM-QAM NOMA scheme for URLLC and eMBB downlink service coexistence

This work introduces a non-orthogonal multiple access (NOMA) scheme suitable for low-rate ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) services multiplexing in downlink communication. Tailored to minimize mutual interference, URLLC information is conveyed via a modified index modulation (IM) scheme on top of quadrature amplitude modulation (QAM) transferring eMBB traffic. Aiming at providing a proof of concept of the proposed scheme, we calculate the bit error rate of both services over Rayleigh fading with diversity, as well as the eMBB service achievable rate with typical multi-input multi-output (MIMO) configuration when the URLLC service utilizes space–time coding or diversity. The proposed scheme achieves a low bit error rate for the URLLC IM signal, at the cost of a lower information rate, while affecting the performance of the eMBB user mainly due to power sharing among the IM and QAM signals. To further investigate the feasibility of the proposed scheme, we calculate the transmission energy required by the base station to support both services over a typical cellular channel model, for various service requirements and user distances, in comparison to an orthogonal multiple access (OMA) puncturing scheme utilizing time-multiplexing of QAM for the eMBB traffic and BPSK for the URLLC traffic. Overall, our results show that the proposed scheme attains better performance compared to the puncturing scheme and offers a robust solution with easy user pairing for low-rate URLLC and typical eMBB downlink service multiplexing for 5G communications and beyond.

Metaheuristic Algorithm-Based Proportional–Integrative–Derivative Control of a Twin Rotor Multi Input Multi Output System

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