Strong Disturbances Research Articles

Aerodynamic interference analysis and modeling of heterogeneous rotors

During closed-range rotorcraft missions, strong aerodynamic disturbances occur between multiple aircraft, thus affecting the stability and safety of cooperative flight processes. However, owing to the numerous control variables in rotor models, the cost and complexity of experiments and body-fitted grid simulation methods are high, thereby resulting in low analysis efficiency and difficulty in establishing a highly applicable aerodynamic-interference model. In this study, the effectiveness of a two-dimensional quasi-steady momentum source method for calculating the aerodynamic interference of heterogeneous rotors is validated using the sliding-mesh method. Using Latin sampling and momentum source methods to obtain aerodynamic-interference data points, a multiparameter-coupled heterogeneous-rotor aerodynamic-interference model is proposed and validated. The results indicate that the proposed aerodynamic-interference model provides clear physical significance and demonstrates high accuracy under different rotor configurations. In validation samples, the correlation coefficient between the predicted aerodynamic loss on the lower rotor and the computational fluid dynamics (CFD) calculation results is approximately 0.99, with the maximum errors in predicting thrust and moment losses are less than 7 % and 2 %. Meanwhile, the correlation coefficient between the predicted values on the upper rotor and the CFD calculation results is 0.82, with the maximum errors in predicting the dimensionless thrust and moment losses are less than 5.5 % and 1 %. The rapid aerodynamic-interference analysis model proposed herein can serve as a reference for the safe-flight strategy of rotorcraft during close-range cooperative missions.

Strong tracking adaptive window multi‐innovation cubature Kalman filter algorithm for lithium‐ion battery state of energy estimation

SummaryAccurate estimation of lithium‐ion battery state of energy (SOE) is an important prerequisite for prolonging battery life and ensuring battery safety. To achieve a high‐precision estimation of the SOE, while a ternary lithium‐ion battery being the specifically targeted in this study, a novel method for SOE estimation is proposed, which combines limited‐memory recursive least squares (LM‐RLS) with strong tracking adaptive window Multi‐innovation cubature Kalman filtering (STW‐MCKF). In the LM‐RLS algorithm, the model parameters at the current time are updated with a limited dataset to solve the data saturation problem and improve the recognition accuracy of the RLS algorithm. The CKF algorithm is optimized by the STW algorithm in the STW‐CKF algorithm to enhance its robustness under strong disturbances. Additionally, a self‐adaptive window multiple innovation strategy is proposed to improve the accuracy of SOE estimation and the stability of the CKF algorithm, while maintaining a balance between computational complexity and SOE estimation accuracy. To validate the effectiveness of the algorithm, experiments are conducted under DST and BBDST conditions. The results show that the STW‐MCKF algorithm has a maximum convergence time of 4 s and an SOE estimation error within 1.04% under DST conditions. Under BBDST conditions, the maximum convergence time is 3 s, and the SOE estimation error is within 2.34%. Furthermore, the STW‐MCKF algorithm demonstrates good stability under the two conditions, indicating the effectiveness of the proposed method for lithium‐ion battery SOE estimation.

Study on Spontaneous Combustion Risk Areas in Narrow Coal Pillar-Utilized Deep Mining and Double-Pipe Liquid CO2 Injection Technology

ABSTRACT Coal spontaneous combustion (CSC) in goafs, one of the most serious disasters in coal mines, not only wastes resources and destroys the ecological environment, but also causes casualties. As global underground mining gradually shifts to the deep, this problem becomes increasingly prominent. Deep mining is operated in a complex mechanical environment characterized with high stress, high permeability, high temperature, and strong mining disturbance. To avoid stress concentration areas, narrow coal pillars are often utilized in roadway excavation along the goaf. However, narrow coal pillars are likely to fracture and lose stability during the mining process. Consequently, goaf areas of two adjacent working faces might connect, which promotes the CSC risk in the goaf. Most of the current researches are focused on CSC risk areas under shallow mining conditions and the CSC-gas coupling disasters, yet research on CSC risk areas in the goaf triggered by narrow coal pillars under deep mining conditions is rarely reported. Moreover, there is a lack of relevant means to prevent and extinguish fires. This study takes the narrow coal pillar-utilized 130,205 working face in Yangchangwan Coal Mine as the research object. First, the development of coal pillar fractures was simulated by adopting the discrete element method of PFC. Besides, on-site monitoring and FLUENT numerical simulation were conducted to identify CSC risk areas in the goaf of the narrow coal pillar-utilized working face. Finally, a double-pipe liquid CO2 injection technology was proposed for fire prevention and extinguishing. The following beneficial results were obtained. Affected by high stress in deep mining, the coal pillar collapses, which expands the range of the oxidation zone on the return side. Meanwhile, the adjacent goaf is also exposed to CSC risk. Double-pipe liquid CO2 injection can effectively reduce air leakage near the working face and suppress spontaneous combustion of residual coal by inerting the goaf. This study can provide guidance and reference for fire prevention and extinguishing in goafs under the condition of narrow coal pillar-utilized deep mining.

A Novel Automatic Generation Control Method Based on the Large-Scale Electric Vehicles and Wind Power Integration Into the Grid.

In order to solve the problem of frequency instability of power system due to strong random disturbance caused by large-scale electric vehicles and wind power grid connection, an improved reinforcement learning algorithm, namely, optimistic initialized double Q, is proposed in this article from the perspective of automatic generation control. The proposed algorithm uses the optimistic initialization principle to expand the agent action exploration space, so as to prevent Q-learning from falling into local optimum by greedy strategy; meanwhile, it integrates double Q-learning to solve the problem of overestimation of action value in traditional reinforcement learning based on Q-learning. In the algorithm, the hyperparameter ατ is introduced to improve the learning efficiency, and the reward bτ based on exploration times is introduced to increase the Q value estimation to drive the exploration of the algorithm, so as to obtain the optimal solution. By simulating the two-area load frequency control model integrated with large-scale electric vehicles and the four-area interconnected power grid model integrated with large-scale wind power generation, it is verified that the proposed algorithm can obtain the global optimal solution, thus effectively solvinng the frequency instability caused by strong random disturbance in the grid-connected mode of large-scale wind power generation, and compared with many reinforcement learning algorithms, the proposed algorithm has better control performance.

The active disturbance rejection control method for small-scale turbojet engine

To enhance the disturbance resistance of the control system for a small-scale turbojet engine, research has been conducted on the active disturbance rejection control (ADRC) method. With gas generator speed N g as the control parameter, an ADRC control scheme was established. Tracking Differentiator (TD), Nonlinear State Error Feedback (NLSEF), and Extended State Observer (ESO) were analyzed in detail, and their parameters were designed. The ADRC method was compared with the Expert PID control method through simulation experiments. The results show that ADRC has a small overshoot and strong disturbance rejection capabilities, allowing for a faster recovery to a steady state when disturbances occur.

Research on a Sensorless ADRC Vector Control Method for a Permanent Magnet Synchronous Motor Based on the Luenberger Observer

A sensorless vector active disturbance rejection control (ADRC) method for permanent magnet synchronous motors (PMSM) utilizing a Luenberger observer is presented. This method aims to address the challenges associated with weak active disturbances, substantial steady-state speed amplitude fluctuations, and difficulty in achieving a balance between overshoot and speed control in the sensorless PMSM control system. Mathematical models of the Luenberger observer and the ADRC were analyzed, leading to the proposal of a second-order ADRC control method based on the Luenberger observer. A mathematical model of the permanent PMSM has been introduced. Additionally, the necessary conditions for the convergence of the Luenberger observer were derived and examined. The allowable range of error feedback gain values was determined, and rotor position data were acquired using a phase-locked loop. The principle of the ADRC was analyzed, and the ADRC simulation results, along with the PI simulation results, were detailed. When the target speed is 1000 r/min, the steady-state error and load disturbance resistance of the ADRC control method outperform those of the PI control method. Finally, the control method was experimentally tested on an STM32F4 chip, demonstrating the advantages of small steady-state error and strong active disturbance ability.

Coal burst mechanism in large-scale panel under extra-thick key strata

Extra-thick key strata (ETKS) are a key factor causing coal bursts. In this study, field monitoring, physical and numerical modelling, and theoretical analysis were comprehensively adopted to investigate the effect of ETKS on the static and dynamic stress and its impact on coal bursts. Firstly, the coal bursts and the stability of ETKS were investigated. The roof strata structure and the mining-induced stress in a large scale panel under ETKS were analyzed. Then, the coal burst mechanism under ETKS is discussed. The results show that when the panel reaches the 4th and 5th longwall faces, the inferior ETKS and the main ETKS break, respectively, and large-scale cantilever-articulated structures involving the ETKS are formed. This moment, the overburden load is transferred downwards massively, and the coal seam becomes overstressed and accumulates massive elastic energy. Meanwhile, the collaborative collapse of multiple strata in the large cantilever-articulated structures causes the sudden release of elastic energy and high-energy seismicity, which exerts strong dynamic disturbances on the overstressed coal mass and leads to coal burst. Finally, strategies for coal burst control under ETKS including deep-hole pre-splitting blasting and panel size optimization are proposed and validated.

A frequency cooperative control strategy for multimicrogrids with EVs based on improved evolutionary-deep reinforcement learning

The construction of microgrid has fully promoted the large-scale access of distributed power and the rapid development of electric vehicles (EVs). And microgrid system faces operational issues such as strong random disturbances from distributed power sources and loads, as well as unexpected events during operation. These issues can lead to unstable microgrid frequency, excessive discharge of EVs, and increased control costs. To solve the issues, a frequency cooperative control strategy for multimicrogrids with EVs based on improved evolutionary-deep reinforcement learning (EDRL) is proposed in this paper. First, consider the effect of the vehicle-to-grid (V2G) process on the minimum time required to fully charge an EV, and the impact of the output distribution of micro turbines (MTs) on the regulation cost, and based on the coupling relationship between generator terminal voltage regulation and system frequency control, a multimicrogrid comprehensive control model is constructed. Second, in order to deal with engineering tasks with deceptive rewards and sparse rewards such as integrated frequency control, evolutionary algorithms and deep reinforcement learning algorithms are combined to effectively assist the training process to jump out of local optimal solutions and approach optimal control strategies. Meanwhile, the algorithm is improved based on the novelty search and intelligent partition strategy, so that the strategy has better convergence characteristics and can reduce the cost of information transmission and computational complexity under the premise of ensuring the control effect. Furthermore, the state space, action space and reward function of the controller are defined. Finally, the simulation results show that the proposed controller has the ability of coordinated control, can effectively reduce the adjustment cost of MT unit and the unnecessary discharging of EVs while ensuring the frequency regulation requirements of each submicrogrid, which is far superior to PID control, fuzzy control, and traditional deep reinforcement learning control.

The Latitudinal and Longitudinal Variation of the Semidiurnal Lunar Tide in the Ionosphere During 2013

AbstractThe present paper studies the latitudinal and longitudinal variation of ionospheric semidiurnal lunar tide (M2) perturbations in 2013 with total electron content (TEC) data. Results show that the latitudinal distribution of M2 exhibits longitudinal difference. At 100 ∼ 120°E and 80 ∼ 100°W longitudes, the strongest M2 perturbations in the northern and southern hemispheres roundly appeared at about 10° ∼ 20°N and 15° ∼ 25°S magnetic latitude (MLAT), respectively, which supports previous suggestion that the M2 maximized in the EIA crest regions. Moreover, the latitude range of M2 at 80 ∼ 100°W longitudes was wider than that at 100 ∼ 120°E longitudes, especially for southern hemisphere (SH). The M2 maximized at 20° ∼ 30°N MLAT in the northern hemisphere (NH) and was absent in the SH at 50°W ∼ 20°E longitudes, which was distinctly different than those at other longitudes. This abnormal behavior of latitudinal distribution of M2 may be caused by the large magnetic declination angle at 50°W ∼ 20°E longitudes. Meanwhile, the longitudinal variation and hemispheric asymmetry of M2 amplitude are different between NH winter (SSW period) and other seasons. The M2 amplitudes were stronger disturbances in the NH and in the American longitudinal sector in NH winter. However, in other seasons, the M2 amplitude was largest in Asian sector, and the M2 amplitude in NH was larger than SH at 0° ∼ 130°E longitudes, which was contrast to these at 0° ∼ 110°W longitudes. These differences of M2 variation between NH winter and other seasons may be related to the differences of the combined effects of the geomagnetic configuration and background conditions of the atmosphere and ionosphere.

A novel controllable bias reinforcement learning method for distributed automatic generation control integrated with large-scale electric vehicles

Due to the introduction of large-scale renewable energy resources and flexible load access, strong random disturbances will bring to the current integrated energy system. The traditional control methods fall short in adequately addressing the frequency regulation needs of distributed systems. From the perspective of distributed automatic generation control (AGC), an optimal coordinated control method is proposed based on reinforcement learning. The proposed method is developed to mitigate the estimation bias of the action-values via the minimum action-value from multiple action-value estimates. Further, the bias of action-value estimates can be adjusted from positive to negative to enhance the control accuracy. Then this method uses behaviour utility trace to achieve adaptive adjustment of the exploration rate, preventing repetitive and unbalanced exploration. Simulation experiments have been performed on the two-area LFC model integrated with electric vehicles and the multi-area integrated energy system model to verify the effectiveness of the proposed control method.

Thermal performance and applied evaluation of the pre-bored grouting planted nodular pile in warm frozen soil

The thermal effect of pile foundation in frozen ground is crucial for the formation of its bearing capacity. To break through the problems of strong thermal disturbance and slow formation of bearing capacity in the existing pile foundations. In this paper, a new type of pile named Pre-bored Grouting Planted Nodular (PGPN) was first introduced to the permafrost area, which was a prefabricated composite pile. Its unique structure can reduce the amount of cementitious materials and improve the bearing capacity. To reveal the thermal performance of the PGPN pile to warm permafrost, and compare with that of bored concrete pile, the model experiment and numerical simulation of the pile were conducted in this paper. It was found that: 1) The refreezing time and thermal disturbance radius of the PGPN pile were 270 days and 1.8 m, respectively, which were one-third and half of those of cast-in-place pile. The cemented-soil around the PGPN pile could significantly reduce its hydration heat and reduce the thermal disturbance to frozen ground. 2) Because of the high cement concentration in cemented-soil, the early hydration reaction of the pile was strong, and the temporary high temperature provided a favorable environment for the strength maintenance of cemented-soil in frozen soil. On the contrary, the hydration reaction of bored concrete pile lasted a long time, which intensified the degree of thermal disturbance to permafrost; and 3) Affected by the cooling effect of the atmospheric environment and the self-recovery effect of the lower permafrost, the temperature field of the pile foundation refrozen from the upper and lower ends to the middle. The structure of the frozen soil was changed by hydration heat, making it difficult to return to its initial state. The research results can provide data support for the promotion and application of prefabricated pile foundation in permafrost regions.

Terminal Sliding Mode Force Control Based on Modified Fast Double-Power Reaching Law for Aerospace Electro-Hydraulic Load Simulator of Large Loads

This paper addresses the force-tracking problem of aerospace electro-hydraulic load simulators under the influence of high inertia, large loads, and a strong coupling force disturbance. An accurate mathematical model is initially derived to describe the characteristics of the load simulator system, the cause of the surplus force, and the strong phase lag due to large inertia. In order to overcome the position interference of the system and the large phase lag problem, a terminal sliding mode control strategy based on the modified fast double-power reaching law is proposed, based on the accurate mathematical model. This control strategy effectively suppresses the chattering problem of the sliding control and implements the finite time convergence of the system through the design of the reaching law and terminal sliding surface, ensuring the robustness of the system and the accuracy of the force-tracking problem. Finally, a comparison of the simulation and experimental results based on the design of different strategy controllers is performed to verify the effectiveness of the control strategy and system adaptability.

Research on the Control Method of a Brushless DC Motor Based on Second-Order Active Disturbance Rejection Control

This research addresses the issues of weak anti-disturbance ability, fast response, and incompatibility of overshoot in the control process of brushless DC motors (BLDCs). A six-step commutation control method based on second-order active disturbance rejection control (ADRC) is derived following the analysis of the BLDC model and the mathematical model of ADRC. A control model of the BLDC using both PI and ADRC is constructed. Detailed comparative and quantitative analyses of the simulation results using PI and ADRC are conducted, focusing on the anti-load disturbance capabilities using the integrated square error (ISE), integrated time square error (ITSE), integrated absolute error (IAE), and integrated time absolute error (ITAE). Experimental testing on the STM32F4 controller is also carried out, analyzing four error integral criteria in depth. The results indicate that both the ADRC and PI control modes can track the target signal without overshooting, demonstrating strong anti-load disturbance ability and robustness at varying working speeds. In the BLDC control system, using the ADRC control method can achieve fast and non-overshoot tracking of target signals compared to the PI control method, and ADRC has stronger resistance to load disturbances.

Impacts of Spatio-Temporal Changes in Anthropogenic Disturbances on Landscape Patterns in the Nandu River Basin, China

We explored the characteristics of landscape pattern (LP) changes in the Nandu River Basin (NRB) and its dynamic response mechanism to anthropogenic disturbance (AD). This is important for ecological protection and for land use decision-making in the basin in the context of the construction of a free trade port. Land use and land cover change (LULCC) data of 1990, 2000, 2010, and 2020 were analyzed with the help of the LP index, moving window method, hemeroby index, geo-information atlas, and geographic information system (GIS) spatial analysis to reveal the dynamic changes in LP characteristics in the NRB. Furthermore, this paper discusses into the correlation between LP and AD. The results indicate that over the past three decades, the NRB showed a staggered LP, dominated by forestland and cropland, whose total area has decreased by 25.27 km2 and 62.75 km2, respectively. On the other hand, the built-up land increased by 91.37%. The overall landscape fragmentation, landscape patch homogeneity, and landscape diversity have increased in the NRB. AD is the main reason for the dramatic changes in the LP of urban agglomerations centered in Haikou city. The area of minor disturbance (over 34%) occupies the largest proportion of the land, followed by higher (about 13~25%) and moderate disturbances (about 17~22%). The area of minor and strong disturbances has increased significantly, whereas those of moderate and higher disturbances have significantly decreased. The spatial distribution pattern of AD is gradually increasing from the southwest (natural landscape) to the northeast (human landscape). A transition in areas of low disturbance levels to higher levels is obvious. The area of the atlas that has transitioned from “minor → lighter disturbance (12)” and “higher → minor disturbance (41)” changed changed most significantly during 2000 to 2010 and from 2010 to 2020, occupying 26.79% and 11.99% of the transfer atlas, respectively. All regions encountering disturbances were significantly correlated with the largest patch index (LPI) from 1990 to 2020. The overall AD in the NRB has increased, especially in Haikou. Urbanization is the main factor for an increase in AD. The ecological and environmental management and monitoring in the basin need to be strengthened.

Boundary-aware value function generation for safe stochastic motion planning

Navigation safety is critical for many autonomous systems such as self-driving vehicles in an urban environment. It requires an explicit consideration of boundary constraints that describe the borders of any infeasible, non-navigable, or unsafe regions. We propose a principled boundary-aware safe stochastic planning framework with promising results. Our method generates a value function that can strictly distinguish the state values between free (safe) and non-navigable (boundary) spaces in the continuous state, naturally leading to a safe boundary-aware policy. At the core of our solution lies a seamless integration of finite elements and kernel-based functions, where the finite elements allow us to characterize safety-critical states’ borders accurately, and the kernel-based function speeds up computation for the non-safety-critical states. The proposed method was evaluated through extensive simulations and demonstrated safe navigation behaviors in mobile navigation tasks. Additionally, we demonstrate that our approach can maneuver safely and efficiently in cluttered real-world environments using a ground vehicle with strong external disturbances, such as navigating on a slippery floor and against external human intervention.

Enhanced Tracking in Legged Robots through Model Reduction and Hybrid Control Techniques: Addressing Disturbances, Delays, and Saturation

This study introduces a reduced-order leg dynamic model to simplify the controller design and enhance robustness. The proposed multi-loop control scheme tackles tracking control issues in legged robots, including joint angle and contact-force regulation, disturbance suppression, measurement delay, and motor saturation avoidance. Firstly, model predictive control (MPC) and sliding mode control (SMC) schemes are developed using a simplified model, and their stability is analyzed using the Lyapunov method. Numerical simulations under two disturbances validate the superior tracking performance of the SMC scheme. Secondly, an Nth-order linear active disturbance rejection control (LADRC) is designed based on a simplified model and optimization problems. The second-order LADRC-SMC scheme reduces the contact-force control error in the SMC scheme by ten times. Finally, a fourth-order LADRC-SMC with a Smith Predictor (LADRC-SMC-SP) scheme is formulated, employing each loop controller independently. This scheme simplifies the design and enhances performance. Compared to numerical simulations of the above and existing schemes, the LADRC-SMC-SP scheme eliminates delay oscillations, shortens convergence time, and demonstrates fast force-position tracking responses, minimal overshoot, and strong disturbance rejection. The peak contact-force error in the LADRC-SMC-SP scheme was ten times smaller than that in the LADRC-SMC scheme. The integral square error (ISE) values for the tracking errors of joint angles θ1 and θ2, and contact force f, are 1.6636×10−28 rad2⋅s, 1.7983×10−28 rad2⋅s, and 1.8062×10−30 N2⋅s, respectively. These significant improvements in control performance address the challenges in single-leg dynamic systems, effectively handling disturbances, delays, and motor saturation.

Comparative analysis of geomagnetic events identified by various indices

We presented the results of comparative analysis of geomagnetic events identified by various indices. A previously developed technique is used to identify magnetic storms by the Dst index. As a basis for identification, we chose the previously developed method for identifying geomagnetic storms based on the Dst index. A similar method was implemented to identify geomagnetic events by the ap and AE indices. Comparative analysis includes: (1) identification of common geomagnetic events identified by various indices; (2) identification of cases when an event is a strong geomagnetic disturbance by one of the indices (Dst, ap, AE) and is not a geomagnetic event by at least one of the two remaining indices; and (3) a comparative analysis of the diurnal and seasonal distribution of the number of geomagnetic events identified by different indices.

A Satellite View of the Wetland Transformation Path and Associated Drivers in the Greater Bay Area of China during the Past Four Decades

As a highly productive and biologically diverse ecosystem, wetlands provide unique habitat for a wide array of plant and animal species. Owing to the strong disturbance by human activities and climate change, wetland degradation and fragmentation have become a common phenomenon across the globe. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a typical case. The GBA has experienced explosive growth in the population and economy since the early 1980s, which has resulted in complicated transitions between wetlands and non-wetlands. However, our knowledge about the transformation paths, associated drivers, and ecological influence of the GBA’s wetlands is still very limited. Taking advantage of the land use maps generated from Landsat observations over the period of 1980–2020, here, we quantified the spatiotemporal transformation paths of the GBA’s wetlands and analyzed the associated drivers and ecological influence. We found that the dominant transformation path between wetland and non-wetland was from wetland to built-up land, which accounted for 98.4% of total wetland loss. The primary transformation path among different wetland types was from coastal shallow water and paddy land to reservoir/pond, with the strongest transformation intensity in the 1980s. The driving forces behind the wetland change were found to vary by region. Anthropogenic factors (i.e., population growth and urbanization) dominated in highly developed cities, while climate factors and aquaculture had a greater influence in underdeveloped cities. The findings presented in this study will provide a reference for wetland management and planning in the GBA.

Bipartite containment control of nonlinear multi‐agent systems with unknown inputs and state constraints

SummaryThis article addresses the issue of bipartite containment tracking for nonlinear strict feedback multi‐agent systems (MASs) with disturbances and measurement uncertainties. To provides a better performance against strong disturbance, a coordination transformation is applied such that all the nonlinear functions, controller disturbances and measurement uncertainties are lumped into a single disturbance signal in the last state equation. Then, the lumped disturbance is compensated through a novel disturbance rebuilding method. Moreover, in order to get better control performance, state constraints are also considered through a nonlinear transformation function (NTF). Different from traditional methods dealing with the state constraint problems, the NTF method can avoid using any discontinuous Lyapunov function. Finally, an adaptive finite‐time distributed controller is established via the disturbance estimates, backstepping technique and fuzzy logic system (FLS). The performance of the controller is illustrated by a simulation example in which the bipartite containment formation of followers is realized in a practical finite time.

Synthesized modeling and active disturbance rejection control for the Leighton Chajnantor Telescope

Abstract The Caltech Submillimeter Observatory telescope was completely disassembled in 2023 October and will be shipped from Hawaii, USA to the new site at Chajnantor Plateau, Chile in 2024, where it is expected to be refurbished and renamed the Leighton Chajnantor Telescope (LCT). Since the much stronger wind load at the new site will provide a great challenge for LCT’s realization of a higher pointing and tracking accuracy, an active disturbance rejection controller (ADRC) is designed for LCT’s pointing control system. Due to the unavailability of LCT during its relocation process, it is necessary to develop a testbed to verify the performance of ADRC by constructing a synthesized simulation model of LCT’s pointing control system, including a mechanical dynamics model of the antenna, an analog circuit model of the motor drive system, a mathematical model of the direct current motors and the reducers, and a wind disturbance model. On the testbed, ADRC undergoes simulations to verify its capability to eliminate the negative impact of strong wind disturbance on LCT’s pointing control performance. The simulation results show that ADRC can bring benefits in improving LCT’s pointing and tracking accuracy when facing strong wind disturbance.