Proportional Integral Research Articles

Novel multi-degree-of-freedom force–displacement mixed control framework for low loading rate substructure hybrid testing

In substructure hybrid tests, imposing realistic multi-degree-of-freedom (MDOF) displacement and force boundary conditions on a specimen involves controlling several actuators to emulate the MDOF boundary conditions of the experimental substructure. This setup presents significant challenges for decoupling control in MDOF systems. Due to the typically high stiffness of the specimen in the axial direction, force control is preferred over displacement control, which has limited accuracy in this direction. Typically, force control in the axial direction is combined with simultaneous displacement control in the softer direction, forming an MDOF force-displacement mixed control scheme. This scheme transforms force control into displacement control in the inner loop in the axial direction to ensure safe loading. The scheme involves addressing two major challenges: the nonlinear force–displacement transformation and the nonlinear coordinate transformation from the local actuator space (LAS) to the global Cartesian coordinate (GCC) system. To address these challenges, the Modified Newton–Raphson iteration integrated with proportional integral plus feed-forward (MNR-PIFF) and incremental kinematic transformation integrated with proportional integral plus feed-forward (IKT-PIFF) methods are proposed. Based on these methods, a novel MDOF force–displacement mixed control (NMFDC) framework is developed for substructure hybrid testing. A 6-DOF cyclic test simulation using the NMFDC framework was conducted to demonstrate its superiority. Furthermore, a 6-DOF cyclic test and substructure hybrid test at low loading rates were performed to validate the NMFDC framework because of the loading equipment limit. Experimental results and numerical simulations indicate that the NMFDC framework achieves swifter response with lower error levels. For applications in fast and real-time hybrid testing, dynamic loading equipment need to be used, the PIFF controller in the NMFDC framework should be replaced by adaptive time delay compensation in future developments.

Advanced Controller System with Hybrid Renewable Sources Enabling Vehicle-To-Grid and Grid-To-Vehicle Operations

Power demand is rising worldwide, forcing the search for an alternative source to solve the imminent power crisis, which is expected to be accomplished through the deployment of grid synchronized electric vehicles (EV). Wind and solar energy are incorporated into this integration to those concerns. One smart grid innovation that permits energy exchange between the EV and the grid is vehicle to grid (V2G) technology. For the purpose of charging EVs, this research proposes employing hybrid energy. Here, the neighboring household’s linear and non linear demands are powered by the excess electricity from the photovoltaics (PV) panel, wind turbine, and AC generator that is utilized to charge the EV battery. Because of its dependability and independence from the real system model, a proportional integral (PI) controller is used to maximize the DC voltage extracted from the PV panel. The improved DC voltage overall is a result of the SEPIC converter’s improved DC output. Concurrently, an AC generator is incorporated into the system to supply extra power to the grid. An artificial neural network (ANN) controller is used to convert the power generated by the PV panel’s Sepic conversion process into an improved output that is then converted to AC using a 3 phase Voltage Source Inverter (VSI). Harmonic components in the VSI output are corrected with an LC filter to ensure grid compatibility and safe current injection into the grid. This paper discusses the usage of a bidirectional converter to adjust the EV battery’s voltage. MATLAB 2021a / Simulink software is used to simulate and validate the proposed approach.

A new approach to instantaneous power based control of DFIG using active disturbance rejection control

This paper presents the design and control of a wind energy converter system that uses a doubly fed induction generator. It introduces a model that incorporates instantaneous active and reactive power as state variables to represent both the stator and rotor sides of the doubly fed induction generator. Subsequently, a linear active disturbance rejection-based control regulator is proposed for the control loops. The parameters of the linear active disturbance rejection controllers (ADRC) are adjusted by two methods: the single parameter tuning approach and the genetic algorithm (GA) method. The performance of the suggested controller is ultimately compared with a tuned proportional-integral (PI) control regulator in the MATLAB environment. In order to examine the performance of the system, various test scenarios are taken into account. These scenarios include different percentages of load change in both sub-synchronous and super-synchronous modes of operation, faults with different fault periods are considered in both modes of operation. The conventional PI controller-based approach requires 35 control cycles to reach steady state under load change conditions and 50 control cycles under symmetrical fault conditions, regardless of whether the system is operating in sub synchronous or super synchronous mode. The single parameter-based tuned ADRC approach only requires 15 control cycles for load change and 20 control cycles for symmetrical fault scenarios. In contrast, for both the load change and symmetrical fault scenarios, the ADRC with GA-based parameter tuning reaches steady state in just 5 control cycles. Furthermore, a case involving wind speed variation is presented to demonstrate the maximum power point tracking capability of the proposed control structure. Similarly, for the robustness test of the control regulator, variations in resistance and inductance are introduced to observe their impact on the response of the control regulators. Finally, a real-time hardware in loop setup is presented to validate the control strategy during the load change in the sub-synchronous mode of operation of the doubly fed induction generator.

Model Predictive Control for Level Control of a Conical Tank

Conical tanks have a high application rate in industrial processes, especially in colloidal mills, chemical processes, and food processing. The use of conical tanks presents significant benefits because they contribute to sedimentation and reduce the accumulation of impurities compared to conventional cylindrical tanks. However, level control of a conical tank due to its shape requires advanced strategies to guarantee efficient control. In this research, a model predictive control (MPC) method was designed and implemented for the level control of a conical tank on a laboratory scale. To evaluate the performance of the controller, it was compared with a traditional proportional–integral (PI) controller, and the rise time, settling time, overshoot, and error in the steady state were analyzed when different set point changes were tested. In addition, the system was subjected to disturbances, and the MPC demonstrated better performance in a transient state, as well as smooth and stable action controls that allowed for an increase in the useful life of the actuator. In addition, an interactive graphical interface was developed that allowed a dynamic response in a real plant to be experienced; this provides an academic tool for designing control strategies before implementation in a real process.

ANFIS-based control of resonant converters for optimized charging system of electric vehicle (EV) batteries

This paper presents a resonant converter-based Electric Vehicle (EV) battery charging module utilizing Proportional-Integral (PI) and Adaptive Neuro-Fuzzy Inference System (ANFIS) control for an optimized charging system. The EV charging module is integrated with a resonant converter comprising a full bridge, HFTF, and DBR. The module utilizes a Resonant Converter which reduces the switching loss incurred during converter operation at high frequency by offering ZCS or ZVS at the switching time. A standard PI Controller manages the duty ratios of the primary full bridge switches with tuned gains. The CC and CV controllers each have their own PI Controller for current and voltage, respectively. To enhance the performance of the EV System, the standard PI Controllers in both the CC and CV control systems are replaced with ANFIS Controllers which are trained as per the data generated by the CC and CV control using an optimization technique that controls the duty ratio of the switches. The proposed ANFIS-based and PI-based control strategy provides an adaptive and flexible approach to control the battery voltage and current by intelligent adjustment of Constant Current (CC) and Constant Voltage (CV) operation modes and the passive elements switching across specific ranges of State-Of-Charge (SOC) to enhance the performance and safety of the charging system. MATLAB Simulation results demonstrated that the proposed ANFIS-based control reduces current ripple content compared to PI-based control. The ANFIS Controller improves overall battery performance, reliability, and stability, which makes it a better choice for next-generation EV charging systems.

DESIGN AND SIMULATION OF PV-POWERED MOTORPUMP RECIRCULATING WATER SYSTEM IN RICE FIELD CRAB FARM USING MPPT-BASED NEUROFUZZY AND BIDIRECTIONAL CONVETER CHARGER CONTROLLERS

Community enterprises engaged in rice field crab farming face significant challenges due to the absence of an electricity grid, resulting in energy shortages that hinder the operation of a 24-hour recirculating water system, essential for maintaining optimal conditions during the crab laviculture phase. Consequently, crab larva survival rates have experienced a significant 60% decline. To address this issue, this paper introduces the design and simulation of an off-grid photovoltaic solar-powered motor-driven water pumping system (PVWPS) using maximum power point tracking (MPPT) and charger controllers, to meet the power demands. The proposed MPPT-based neuro-fuzzy controller (NFC), in conjunction with a proportional-integral-derivative (PID) controller, maximizes the utilization of PV power for the motor pump. Additionally, a bidirectional converter facilitates battery charging and discharging. The motor speed, which directly influences the required pump flow rate, is regulated by controlling the voltage across the DC-linked capacitor, using an additional proportional-integral (PI) controller. The Kp, Ki, and Kd parameters of both the PID and PI controllers are meticulously adjusted through a genetic algorithm (GA) to optimize control performance. Simulation results demonstrate the effectiveness of the implemented MPPT-NFC and charge controller. The energy utilization efficiency can be increased by up to 97% and the overall efficiency of the PVWPS is improved by up to 10.5%, effectively overcoming the power shortage challenge. Consequently, the proposed PVWPS, utilizing MPPT-NFC with PID-controller and a bidirectional converter PI-controller, enables 24-hour operation of the recirculating water system, with the anticipation of an increased crab larva survival rate.

Tracking control of heterogeneous Markov jump multi-agent systems based on PI observer

This paper discusses the problem of tracking control for heterogeneous Markov jump multi-agent systems (MASs) with directed communication topology. For each follower, a distributed Proportional-Integral (PI) observer is constructed. A synchronous tracking controller is developed by considering the state of the node itself and the observation state of the observer, in conjunction with the feedback control strategy. To address the inconsistency between the controller mode and the system mode, the hidden Markov model (HMM) is employed to describe the relationship between different modes, an asynchronous controller with dual-mode dependence is constructed to stabilize the system and the stability criterion is obtained. Simulation results demonstrate that, under the condition of system mode transitions satisfying Markov jumps, both controllers can effectively achieve the tracking control of heterogeneous MASs.

Design of a novel intelligent adaptive fractional-order proportional-integral-derivative controller for mitigation of seismic vibrations of a building equipped with an active tuned mass damper

The primary objective of this study is to introduce a novel adaptive fractional order proportional–integral–derivative (FOPID) controller. The adaptive FOPID controller’s parameters are dynamically adjusted in real-time using five distinct multilayer perceptron neural networks. The extended Kalman filter (EKF) is employed to facilitate the parameter-tuning process. A multilayer perceptron neural network, trained using the error Backpropagation algorithm, is employed to identify the structural system and estimate the plant. The real-time estimated Jacobian is applied to the controller to control the model. The stability and robustness of the adaptive interval type-2 fuzzy neural networks controller are enhanced by utilizing the EKF and the feedback error learning strategy for compensator tuning. This improvement increases resilience against estimation errors, seismic disturbances, and unknown nonlinear functions. The primary objective is to address the challenges posed by maximum displacement, acceleration, and drift, as well as the uncertainties arising from variations in stiffness and mass. In order to validate the reliability of the proposed controller, the performance investigation is carried out on an 11-story building equipped with an active tuned mass damper under far and near-field earthquakes. Numerical findings show the remarkable effectiveness of the proposed controllers compared to their predecessors. In addition, it is revealed that the inclusion of the adaptive interval type-2 fuzzy neural networks compensator has increased the performance of the proposed controller and shows significant capabilities in reducing the seismic responses of structures during severe earthquake events.

Design and multiobjective optimization of a two‐point contact ladder‐climbing robot using a genetic algorithm

AbstractThis paper presents the design and optimization of a climbing robot. The design of a ladder‐climbing robot is done with fundamental mathematical considerations. The designed robot is robust enough to manage all environmental calamities, and at the same time, it is optimized for lightweight to reduce the actuator's cost and ease of transportation. An analytical evaluation is carried out for both static and dynamic conditions to determine strength and motion characteristics. The multiobjective optimization of the design parameters of a ladder‐climbing robot is done to obtain optimized values of design parameters. The formulation of an optimization problem that considers the minimization of weight and natural frequency is performed. Using an evolutionary genetic algorithm (GA) for the multicriteria optimization problem is solved, and a Pareto front solution is obtained. The optimal values of the parameters are decided based on the knee selection technique. As both objective functions are contradictory, the optimum results significantly improve the robot's performance. Controlling the proportional–integral–derivative (PID) parameters is crucial as the robot climbs with a two‐point contact gait pattern. The controlling parameters impart stability to the robot. PID parameters like proportional, integral and derivative gain are tunned using the GA. Finally, the developed prototype is tested on the ladders of the tower, and satisfactory climbing motion is achieved.

Design and implementation of single DC-link based three-phase multilevel inverter with CB-PWM techniques

Simulation and implementation of a single DC-link-based three-phase inverter are investigated in this article. The primary focus is on designing a single DC-link three-phase inverter for high power applications. Unlike conventional inverters that require 600 V to generate 400 V (RMS) at the output, the proposed system achieves this with only 330 V, facilitated by a 12-terminal 1:1 transformer. The system employs Proportional Integral (PI) and Neural Network (NN) controllers to optimize performance. Various Carrier-Based Pulse Width Modulation (CB-PWM) techniques, including Phase Disposition (PD), Phase Opposition Disposition (POD), and Alternative Phase Opposition Disposition (APOD), are implemented and evaluated based on Total Harmonics Distortion (THD) concerning the Modulation Index (MI) of the inverter. The proposed inverter achieves a THD reduction to 4.8%, demonstrating superior performance compared to recent studies. The system’s performance is validated through extensive MATLAB/Simulink simulations and practical implementation using XILINX FPGA software, confirming the efficacy of the proposed design.

Frequency Coordination Control Strategy for Large-Scale Wind Power Transmission Systems Based on Hybrid DC Transmission Technology with Deep Q Network Assistance

Wind power is currently the most mature representative of sustainable energy generation technology, which has been developed and utilized on a large scale worldwide. The random and fluctuating nature of wind power output poses a threat to the secure and stable operation of the system. Consequently, the transmission of wind power has garnered considerable attention as a crucial factor in mitigating the challenges associated with wind power integration. In this paper, an artificial-intelligence-aided frequency coordination control strategy applicable to wind power transmission systems based on hybrid DC transmission technology is proposed. The line commutated converter (LCC) station at the sending end implements the strategy of auxiliary frequency control (AFC) and automatic generation control (AGC) to cooperate with each other in order to assist the system frequency regulation. The AFC controller is designed based on the variable forgetting factor recursive least squares (VFF-RLS) algorithm for system identification. First, the VFF-RLS algorithm is used to identify the open-loop transfer function of the system. Then, the AFC controller is designed based on the root locus method to achieve precise control of the system frequency. The DC line power modulation quantity is introduced in the AGC to automatically track the active power fluctuation and frequency deviation of the system. The AGC utilizes the classical proportional-integral (PI) control. By selecting the integrated time absolute error (ITAE) performance index to construct the reward function, and using a deep Q-network (DQN) for controller parameter optimization, it achieves improved regulation performance for the AGC. The voltage source converter (VSC) station at the receiving end implements an adaptive DC voltage droop control (ADC)strategy. Finally, the effectiveness and robustness of the proposed frequency control strategy are verified through simulation experiments.

Comparison between Genetic Algorithms of Proportional–Integral–Derivative and Linear Quadratic Regulator Controllers, and Fuzzy Logic Controllers for Cruise Control System

One of the most significant and widely used features currently in autonomous vehicles is the cruise control system that not only deals with constant vehicle velocities but also aims to optimize the safety and comfortability of drivers and passengers. The accuracy and precision of system responses are responsible for cruise control system efficiency via control techniques and algorithms. This study presents the dynamic cruise control system model, then investigates a genetic algorithm of the proportional–integral–derivative (PID) controller with the linear quadratic regulator (LQR) based on four fitness functions, the mean squared error (MSE), the integral squared error (ISE), the integral time squared error (ITSE) and the integral time absolute error (ITAE). Then, the response of the two controllers, PID and LQR, with the genetic algorithm was compared to the response performance of the fuzzy and fuzzy integral (Fuzzy-I) controllers. The MATLAB 2024a program simulation was employed to represent the system time response of each proposed controller. The output simulation of these controllers shows that the type of system stability response was related to the type of controller implemented. The results show that the Fuzzy-I controller outperforms the other proposed controllers according to the least Jmin function, which represents the minimum summation of the overshoot, settling time, and steady-state error of the cruise control system. This study demonstrates the effectiveness of driving accuracy, safety, and comfortability during acceleration and deceleration due to the smoothness and stability of the Fuzzy-I controller with a settling time of 5.232 s and when converging the steady-state error to zero.

Model predictive control of switched nonlinear systems using online machine learning

This work introduces an online learning-based model predictive control (MPC) approach for the modeling and control of switched nonlinear systems with scheduled mode transitions. Initially, recurrent neural network (RNN) models are constructed offline, utilizing sufficient historical operational data to capture the nominal system dynamics for each mode. Subsequently, we employ real-time process data to develop online learning RNN models, aiming to approximate the dynamics of switched nonlinear systems in the presence of of bounded disturbances. In cases where the initial RNN model is unavailable for a specific switching mode due to very limited historical data, we use real-time data from closed-loop operations under a proportional–integral (PI) controller to build online learning RNN models. To evaluate the predictive performance of online learning RNNs, a theoretical analysis on their generalization error bound is developed using statistical machine learning theory. Additionally, considering the presence or absence of initial RNN models, two MPC schemes are developed. These schemes employ RNNs as prediction models to stabilize switched nonlinear systems, ensuring closed-loop stability by accounting for the generalization error bound derived for online learning RNNs. Finally, the effectiveness of the proposed MPC schemes is demonstrated through a nonlinear process example with two switching modes.

The Effect of Proportional, Proportional-Integral, and Proportional-Integral-Derivative Controllers on Improving the Performance of Torsional Vibrations on a Dynamical System

The primary goal of this research is to lessen the high vibration that the model causes by using an appropriate vibration control. Thus, we begin by implementing various controller types to investigate their impact on the system’s reaction and evaluate each control’s outcomes. The controller types are presented as proportional (P), proportional-integral (PI), and proportional-integral-derivative (PID) controllers. We employed PID control to regulate the torsional vibration behavior on a dynamical system. The PID controller aims to increase system stability after seeing the impact of P and PI control. This kind of control ensures that there are no unstable components in the system. By using the multiple time scale perturbation (MTSP) technique, a first-order approximate solution has been obtained. Using the frequency response function approach, the stability and steady-state response of the system at the primary resonance scenario (Ω1≅ω1,Ω2≅ω2) are considered as the worst resonance and addressed. Additionally examined are the nonlinear dynamical system’s chaotic response and the numerical solution for various parameter values. The MATLAB programs are utilized to attain simulation outcomes.

Closed-Loop Control of Melt Pool Temperature during Laser Metal Deposition.

Laser metal deposition (LMD) is a technology for the production of near-net-shape components. It is necessary to control the manufacturing process to obtain good geometrical accuracy and metallurgical properties. In the present study, a closed-loop control method of melt pool temperature for the deposition of small Ti6Al4V blocks in open environment was proposed. Based on the developed melt pool temperature sensor and deposition height sensor, a closed-loop control system and proportional-integral (PI) controller were developed and tested. The results show that with a PI temperature controller, the melt pool temperature tends to the desired value and remains stable. Compared to the deposition block without the controller, a flatter surface and no oxidation phenomenon are obtained with the controller.

Comparative analysis of proportional, proportional–integral, and proportional–integral–derivative controllers for thermal regulation in a cylindrical cooling system with multiple O-Ring heat sources

This study is intended to determine the thermal management capability of different closed-loop controllers inside a cylindrical three-dimensional cooling system with multiple O-Ring type heat sources. The motivation for this research stems from the need for efficient thermal regulation in advanced cooling systems, which is critical for applications ranging from electronics cooling to industrial processes. The uniqueness of this study lies in its comprehensive evaluation of different controllers such as proportional (P), proportional–integral (PI), and proportional–integral–derivative within a geometrically complex cooling environment using a novel trial-and-error approach for tuning controller parameters. The observational domain is a hollow cylinder, and the four discrete heat sources are placed at regular intervals along the cylinder's longitudinal axis, with all the remaining walls insulated. At the center of the system is a temperature probe that measures and provides feedback to the control module to continuously compare it to a pre-specified setpoint temperature. The flowing fluid, air, enters through the semi-circular inlet at one end, whose velocity is controlled by a controller response, and is discharged through the semi-circular outlet at the other end at atmospheric conditions. This study uses the Galerkin finite element method, using proper initial and boundary conditions to solve the governing equations, namely, the Navier–Stokes and heat energy equations. We analyze the time-dependent behaviors of the cooling system by measuring controller responses such as overshoot, rise time, oscillation, steady-state error, and settling time. Additionally, the impacts of the Reynolds number (Re), Richardson number (Ri), and Grashof number (Gr) on the overall mean Nusselt number over time are observed to understand the influence of flow regulation due to the controllers' actions. This comprehensive analysis provides insights into the controllers' inlet flow control based on the set temperature at the probe's center location. A unique trial-and-error approach for selecting Kp and Ki values, which is crucial for controller analysis, is also presented. Based on the results, it can be inferred that increasing the Kp value from 0.003 to 0.010 ms−1 K−1 reduces the steady-state error from 40.8% to 13.97%. For a Ki value of 0.006 ms−2 K−1, the PI controller achieves a zero steady-state error with faster settling at 3.29 s, along with an overshoot of approximately 80.51%. Conversely, a lower Kd value of about 0.0001 mK−1 results in a reduction in the settling time and overshoot compared to the PI controller, while a higher Kd value ensures optimum stability with a higher settling time and a stable Nusselt number of 1.93. This trial-and-error approach to parameter tuning provides valuable insights into the design of controlled environments and the effective management of thermal conditions in various thermo-fluidic applications.

Investigations of BLDC motor speed characteristics via THD under conventional and advanced hybrid controllers

This project investigates brushless direct current (BLDC) motor speed control through total harmonic distortion (THD) analysis, employing proportional integral (PI), fuzzy logic (FLC), adaptive neuro-fuzzy inference system (ANFIS), and an innovative hybrid ANFIS-PD/PI controller. Considering the vital role of BLDC motors in precision-dependent industries like robotics, electric vehicles, and industrial automation, our primary focus is on understanding BLDC motor operation and recognizing THD's significance as a performance metric. Controllers are meticulously implemented in real-time, fine-tuned, and optimized to achieve desired speed characteristics, incorporating considerations like response time, accuracy, and energy efficiency. The project's core involves THD analysis, quantifying harmonic content in the BLDC motor's speed waveform. This facilitates a comprehensive comparative evaluation of controller performance, assessing their capability to maintain speed stability and influence power quality. The discussion covers the merits and limitations of each controller, with a special emphasis on the hybrid ANFIS-PD/PI controller, seamlessly blending ANFIS adaptability with PD/PI control stability. Results illustrate the hybrid controller's excellence in optimizing BLDC motor speed control, demonstrating superior performance in speed accuracy, disturbance rejection, and THD reduction. These findings drive advancements in motor control technology, providing practical guidance for selecting controllers tailored to specific application requirements. Simulation results can be analyzed using MATLAB/Simulink 2018a Software.

High-power three-phase interleaved parallel NPC structure DC-DC converter modeling and simulation

Abstract In fields such as DC microgrids, renewable energy generation, and electric vehicle charging stations, three-phase NPC structure DC-DC converters play an irreplaceable role, constituting a significant part of modern power electronics technology. Mathematical models are established based on various operating modes of IGBT switching devices under PWM control, encompassing input voltage, output voltage, phase currents, average current, and duty cycle. Simulation models are constructed on the MATLAB platform to confirm the correctness of these mathematical models. Closed-loop control strategies are implemented using proportional-integral (PI) controllers. This control strategy serves to confirm the precision of the mathematical models.

Verification and Validation of Advanced Control Systems for a Spinal Joint Wear Simulator.

Wear simulation aims to assess wear rates and their dependence on factors like load, kinematics, temperature, and implant orientation. Despite its significance, there is a notable gap in research concerning advancements in simulator control systems and the testing of clinically relevant waveforms. This study addresses this gap by focusing on enhancing the conventional proportional-integral-derivative (PID) controller used in joint simulators through the development of a fuzzy logic-based controller. Leveraging a single-input multiple-output (SIMO) fuzzy logic control system, this study aimed to improve displacement control, augmenting the traditional proportional-integral (PI) tuning approach. The implementation and evaluation of a novel Fuzzy-PI control algorithm were conducted on the Leeds spine wear simulator. This study also included the testing of dailyliving (DL) profiles, particularly from the hip joint, to broaden the scope of simulation scenarios. While both the conventional PI controller and the Fuzzy-PI controller met ISO tolerance criteria for the spine flexion-extension (FE) profile at 1 Hz, the Fuzzy-PI controller demonstrated superior performance at higher frequencies and with DL profiles due to its real-time adaptive tuning capability. The Fuzzy-PI controller represents a significant advancement in joint wear simulation, offering improved control functionalities and more accurate emulation of real-world physiological dynamics.

Adaptive terminal sliding combined super twisting control design and flight tests for automatic carrier landing system

Considering the challenges posed by external disturbances on carrier-based aircraft landing control, higher demands are required for the precision and convergence of the carrier landing control system. First, this paper proposes an Adaptive Terminal Sliding Combined Super Twisting Control (ATS-STC) method to address the issues of low precision, slow convergence, and poor disturbance rejection capability resulting from external disturbances, such as carrier air-wake and deck motion. By introducing a nonlinear term into the sliding surface and employing an integral approach, the proposed ATS-STC method can ensure finite-time convergence and mitigate the chattering problem. An adaptive law is also utilized to estimate the external disturbances, thereby enhancing the anti-disturbance performance. Then, the stability and convergence time analysis of the designed controller are conducted. Based on the proposed method, an Automatic Carrier Landing System (ACLS) is developed to perform the carrier landing control task. Furthermore, a multi-dimensional validation is carried out. For the numerical simulation test, the Terminal Sliding Mode Control (TSMC) method and Proportion Integration Differentiation (PID) method are introduced as comparison, the quantitative assessment results show that the tracking error of TSMC and PID can reach 1.5 times and 2 times that of the proposed method. Finally, the Hardware-in-the-Loop (HIL) test and real flight test are conducted. All the experimental results demonstrate that the proposed control method is more effective and precise.