Control Method Research Articles

Research on Closed-Loop Control of Screen-Based Guidance Operations in High-Speed Railway Passenger Stations Based on Visual Detection Model

Due to adjustments to the operation plan of guided trains at high-speed railway stations, a large amount of information is inevitably displayed, sometimes with delays, omissions, and misalignments. The effective management of guidance information can provide important support for the personnel flow operation of high-speed railway stations. Aiming to meet the requirements of high real-time and high accuracy of guided job control, a closed-loop control method based on a guided job is proposed, which provides enhanced text detection and recognition in a target area. Firstly, using the introduction of the triplet attention mechanism in YOLOv5 and the addition of fusion modules, the feature pyramid network is used to enhance the effective feature and feature interactions between the modules to improve the detection speed of the display. Then, the text on the guide screen is recognized and extracted in combination with the PaddleOCR model, and then, the results are proofread against the original plan to adjust the screen information. Finally, the effectiveness and feasibility of the method are verified by experimental data, with the accuracy of the improved model reaching 90.6% and the speed reaching 1 ms, which meets the requirement of real-time closed-loop control of Screen-Based Guidance Operations.

A Novel Nonlinear Adaptive Control Method for Longitudinal Speed Control for Four-Independent-Wheel Autonomous Vehicles

As autonomous driving technology and four-independent-wheel chassis systems advance, four-independent-wheel autonomous vehicles have increasingly become a focal area of modern research. The longitudinal control problem for four-independent-wheel autonomous vehicles presents challenges such as complex models, high nonlinearity, and strong system uncertainties. This paper proposes a novel hierarchical control algorithm to address these challenges, innovatively combining the advantages of adaptive backstepping and dynamic sliding mode control algorithms in the upper controller, allowing it to effectively overcome the impact of uncertain system parameters and suppress the common chattering phenomenon in the output of typical sliding mode control methods. Based on the design of the upper controller, an innovative optimized longitudinal force distribution strategy and the construction of a tire reverse longitudinal slip model are proposed, followed by the design of a fuzzy PID controller as the lower slip ratio controller to achieve precise whole-vehicle longitudinal speed tracking and improve overall control performance. This method not only improves the accuracy of speed tracking but also enhances the robustness and adaptability of the control system. Finally, the effectiveness and superiority of the proposed hierarchical control method are verified through CarSim simulations.

DeepRFQC: automating quality control for P-wave receiver function analysis using a U-net inspired network

This paper introduces DeepRFQC, an automated method for quality control in P-wave receiver function analysis. Leveraging a U-Net inspired deep learning model, which has previously shown promise in denoising and phase detection, DeepRFQC efficiently distinguishes usable from noisy receiver functions. We examine a Proterozoic Trans-Hudson Orogen dataset from northern Canada, including seismic events from 1990 to 2023, which is expanded for training purposes by data augmentation techniques. With 1,508,449 trainable parameters, the DeepRFQC model attains a commendable 96.6% validation accuracy, on a test dataset from the X5 seismic network; tests on stations from different tectonic environments indicate that the model is effective even in environments very different from the training set. Validation through the H-κ stacking method shows consistent and plausible results. As manual quality control is a major bottleneck in receiver-function processing, automated methods such as this one will allow for efficient examination of large data sets.

High‐speed end‐to‐edge data caching and forwarding architecture based on field programmable gate array

AbstractThe application of the Internet of Things generates massive end‐device data. Traditional end‐to‐edge data caching methods lack parallelism, consume numerous resources, and lack data backup mechanisms. We propose a lightweight high‐speed data caching and forwarding architecture based on a field‐programmable gate array. In the basic scheme, a double‐data‐rate memory read/write mixed control method is proposed to efficiently cache massive amounts of data. To improve reliability, we propose address‐space virtualization to forward data to backup devices. To satisfy the demand for higher performance scenarios, we propose a real‐time data shunting mechanism to realize an optimized scheme that supports higher data rates. These two schemes support single‐link high‐speed data caching and forwarding at 10 and 40 Gbps. Compared with the existing method, the basic and optimized schemes improve the performance by 25% and four times and save 66% and 40% of the resources, respectively. Thus, we provide a lightweight and reliable high‐performance end‐to‐edge data caching solution for resource‐constrained edge devices.

Autonomous UAV Chasing with Monocular Vision: A Learning-Based Approach

In recent years, unmanned aerial vehicles (UAVs) have shown significant potential across diverse applications, drawing attention from both academia and industry. In specific scenarios, UAVs are expected to achieve formation flying without relying on communication or external assistance. In this context, our work focuses on the classic leader-follower formation and presents a learning-based UAV chasing control method that enables a quadrotor UAV to autonomously chase a highly maneuverable fixed-wing UAV. The proposed method utilizes a neural network called Vision Follow Net (VFNet), which integrates monocular visual data with the UAV’s flight state information. Utilizing a multi-head self-attention mechanism, VFNet aggregates data over a time window to predict the waypoints for the chasing flight. The quadrotor’s yaw angle is controlled by calculating the line-of-sight (LOS) angle to the target, ensuring that the target remains within the onboard camera’s field of view during the flight. A simulation flight system is developed and used for neural network training and validation. Experimental results indicate that the quadrotor maintains stable chasing performance through various maneuvers of the fixed-wing UAV and can sustain formation over long durations. Our research explores the use of end-to-end neural networks for UAV formation flying, spanning from perception to control.

Characterization of Disinfection By-Products Originating from Residual Chlorine-Based Disinfectants in Drinking Water Sources

In this study, samples from the Yangtze River, Han River, and Liangzi Lake in Wuhan City were utilized to characterize the formation of disinfection by-products (DBPs) from chlorine-based disinfection residues in drinking water sources. The results indicated that the main DBPs in drinking water sources were trichloromethane (TCM) and trichloroacetic acid (TCAA). The generation of DBPs was significantly positively correlated with oxidative substances, aromatic compounds, pH, and ammonia nitrogen (NH3-N) content in the water. The concentration of TCAA increased from 0 to 2.45 ± 0.31 mg/L when the reaction time increased to 72 h. As the NaClO concentration increased from 5 mg/L to 15 mg/L, the concentrations of TCAA, TBM, and DCAN increased from 2.03 ± 0.04 mg/L, 0 mg/L, and 0 mg/L to 2.49 ± 0.34 mg/L, 0.21 ± 0.07 mg/L, and 0.10 ± 0.04 mg/L before decreasing to 1.75 ± 0.19 mg/L, 0.17 ± 0.07 mg/L, and 0.04 ± 0.05 mg/L, respectively. The orthogonal experimental results showed that Br−, NH3-N, and pH all had significant influences on the TCM generation, whereas temperature affected the formation of TCAA in the Han River. This work reveals the factors influencing the generation of DBPs from chlorine-based disinfection residues, offering a prevention and control method for DBPs in drinking water sources from a theoretical perspective.

A review of finite control set model predictive control for linear machines

AbstractLinear machines (LMs) have become excellent candidates for linear motion applications due to their abilities of generating thrust without any transmissions, and have been widely adopted in transportation, industries etc. Among the numerous control methods of LM drive systems, finite control set model predictive control (FCS‐MPC) method has been given special attention due to its clear concept, fast response performance, and ability to handle constrained multivariate non‐linear control problems. It is widely used to improve the performance and efficiency of overall LM drive systems. Firstly, based on the three elements of FCS‐MPC, the advantages and shortcomings of the FCS‐MPC method in applications are fully investigated. The research status in FCS‐MPC has been summarised, such as multistep MPC, parameters robustness improvement, fast optimisation, and multi‐vector MPC. Then, the key issues in FCS‐MPC such as delay compensation, parameters design, and stability analysis are discussed, and their typical research methods and current research status are explained. Finally, the future development trends of FCS‐MPC in LM drive systems are discussed.

Quantitative Analysis of Amorphous Form in Indomethacin by Near Infrared Spectroscopy Combined with Partial Least Squares Regression Analysis

Indomethacin (INDO) is a synthetic non-steroidal antipyretic, analgesic, and anti-inflammatory drug that commonly exists in both amorphous and crystalline states. Its amorphous state (A-INDO) is utilized by pharmaceutical companies as an active pharmaceutical ingredient (API) in the production of INDO drugs due to its higher apparent solubility and bioavailability. The crystal state also encompasses various crystal forms such as the α-crystal form (α-INDO) and γ-crystal form (γ-INDO), with the highly crystalline and insoluble γ-INDO being commercially available. A-INDO, existing in a thermodynamically high-energy state, is susceptible to several factors during the preparation, storage, and transportation of API leading to its conversion into γ-INDO, thus impacting the bioavailability and efficacy of INDO drugs. Therefore, quantitative analysis of the A-INDO/γ-INDO content in INDO API becomes essential for controlling the production quality of INDO. The primary objective of this study is to investigate the feasibility of NIR for the quantitative analysis of A-INDO in INDO API, and to further elucidate its quantitative analysis mechanism. The NIR spectral data were collected for A-INDO and γ-INDO binary mixture samples with different resolutions, and these spectra were then selected and reconstructed using the interval partial least square (iPLS) method. Different pretreatment methods were employed to enhance the reconstructed spectra by highlighting relevant eigen information while eliminating invalid information caused by environmental factors or physical characteristics of samples. The most suitable PLSR model for quantitative analysis of A-INDO within the range of 0.0000–100.0000% w/w% was established, screened, and validated. From various perspectives, including distribution of spectral effective information, impact of resolution on PLSR model performance, variance contribution/cumulative variance contribution of PLSR model principal components (PCs), PCI loadings, relationship between spectral scores, and A-INDO content, feasibility assessment was conducted for the quantitative analysis of A-INDO in INDO using NIR spectroscopy. Additionally, a detailed investigation on the quantitative analysis mechanism of the optimal PLSR model was undertaken including the correlation between the characteristic peaks of spectra and information regarding hydrogen groups or hydrogen bonds in A-INDO or γ-INDO molecules. This study aims to provide theoretical support for the quantitative analysis of A-INDO in INDO API as well as serve as a reliable reference method for API quantification and quality control in similar drugs.

Development of an HPPD-Inhibitor Resistant Wheat and Multiomics Integrative Analysis of Herbicide Toxicity and OsHIS1 Detoxification in Wheat.

Weed infestation in agricultural fields significantly diminishes crop yields. Herbicides are widely used as a primary method of weed control. Developing herbicide-resistant crops through the expression of resistant genes represents a sustainable approach. This study generated wheat germplasms highly resistant to 4-hydroxyphenylpyruvate dioxygenase (HPPD)-inhibiting herbicides by transforming the rice HPPD INHIBITOR SENSITIVE 1 (OsHIS1) gene into Xinong 511, conferring resistance to mesotrione at levels up to nine times the typical field application rate (1350 g ai ha-1). Agronomic trait evaluations under greenhouse and field conditions showed no additional effects on wheat. Herbicide susceptibility assays confirmed the specific resistance to different HPPD inhibitors. Transcriptome and metabolome analyses revealed regulation of flavonoid and photosynthesis-antenna protein pathways in the herbicide functional. Collectively, OsHIS1 could be applied in the production of herbicide-resistant wheat.

Investigating surface area and recovery efficiency of healthcare-associated pathogens to optimize composite environmental sampling.

Hospital surfaces are known to contribute to the spread of healthcare-associated antimicrobial pathogens. Environmental sampling can help locate reservoirs and determine intervention strategies, although sampling and detection can be labor intensive. Composite approaches may help reduce time and costs associated with sampling and detection. We investigated optimum surface areas for sampling antimicrobial-resistant organisms (AROs) with a single side of cellulose sponge, created theoretical composites (TC) by adding recovery results from multiple optimum areas, then compared the TC to the standard Centers for Disease Control and Prevention sampling method (one sponge using all sides, whole tool; (WT)). Five AROs were evaluated: carbapenemase-producing KPC+ Klebsiella pneumoniae (KPC), Acinetobacter baumannii (AB), methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus faecalis (VRE) and Clostridioides difficile spores (CD). Steel coupons comprising four surface areas (323; 645; 1,290 and 2,258 cm2) were inoculated, dried, and sampled with one sampling pass using the larger side (face) or the smaller side (edge) of a pre-moistened cellulose sponge tool. Based on the optimum areas determined for each organism, composite areas were 1,290 cm2 for MRSA and VRE, 1,936 cm2 for AB, 2,580 cm2 for CD spores and 3,870 cm2 for KPC. Total colony forming units (CFU) recovered using a composite approach was greater or comparable than using multiple WT samplings (over the same area as the composite) for MRSA, VRE and AB (130%; 144% and 95%) yet less than if using multiple WT samplings for KP and CD (47% and 66%). We propose a conservative composite sampling strategy if the target organism is unknown; 323 cm2 sampling area for each of the four sides of the sponge, (1290 cm2 total). The conservative composite sampling strategy improved the recovery of KP (from 47% to 85% of multiple WT samplings), while MRSA, VRE, AB and CD (131%; 144%; 97% and 66%) remained within 5% to that of the optimum area TC.

Research on Direct-Drive Brake Actuator and Its Control Method for Electric Vehicles

<div>With the rapid development of electric vehicles, the need for improved reliability and safety performance of electric vehicle braking systems has become paramount. In response to this demand, a novel direct-drive brake-by-wire actuator based on linear motors was designed to address these challenges. This article presents the structure and principles of the proposed braking actuator. Leveraging the traditional electromechanical brake systems as a foundation, the prototype was modified and fabricated. Additionally, the control and drive system for the braking actuator was established using the TMS320F28335 digital signal processor. Moreover, the current-position dual closed-loop control algorithm was devised to regulate the braking force accurately. Experimental results demonstrate that the direct-drive brake-by-wire actuator exhibits rapid responsiveness and precise braking force modulation, showcasing promising prospects in the field of electric vehicle braking.</div>

High-precision reconstruction of a heat flux boundary via a programmable scanning electron beam controlled by the voltage waveform design method

A scanning electron beam can be used to construct heat flux boundaries (HFBs). However, the long current response time in the deflection coil using a current control method (CCM) cause the deviation of the current waveform, potentially reducing the accuracy of the reconstructed HFB relative to the target HFB. The impact of the current response time on the reconstruction accuracy increases as the field frequency increases, and the accuracy can be reduced from 0.89 to 0.69. To shorten the current response time and improve the reconstruction accuracy, a voltage waveform design method (VWDM) is introduced as a replacement for the CCM. The result indicates that the accuracy of the HFB reconstructed by a scanning electron beam controlled by the VWDM can reach 0.91. Additionally, increasing the maximum output voltage of the power amplifier used to generate the voltage can further improve the reconstruction accuracy of the HFB with the VWDM. This study provides a new approach for the accurate construction of HFBs for rapid thermal processing, additive manufacturing and thermal assessment of hypersonic vehicles.

An MPC-DCM Control Method for a Forward-Bending Biped Robot Based on Force and Moment Control

For a forward-bending biped robot with 10 degrees of freedom on its legs, a new control framework of MPC-DCM based on force and moment is proposed in this paper. Specifically, the Diverging Component of Motion (DCM) is a stability criterion for biped robots based on linear inverted pendulum, and Model Predictive Control (MPC) is an optimization solution strategy using rolling optimization. In this paper, DCM theory is applied to the state transition matrix of the system, combined with simplified rigid body dynamics, the mathematical description of the biped robot system is established, the classical MPC method is used to optimize the control input, and DCM constraints are added to the constraints of MPC, making the real-time DCM approximate to a straight line in the walking single gait. At the same time, the linear angle and friction cone constraints are considered to enhance the stability of the robot during walking. In this paper, MATLAB/Simulink is used to simulate the robot. Under the control of this algorithm, the robot can reach a walking speed of 0.75 m/s and has a certain anti-disturbance ability and ground adaptability. In this paper, the Model-H16 robot is used to deploy the physical algorithm, and the linear walking and obstacle walking of the physical robot are realized.

Research on Radial Vibration Model and Low-Frequency Vibration Suppression Method in PMSM by Injecting Multiple Symmetric Harmonic Currents

Driven by frequency conversion, the windings of a three-phase permanent magnet synchronous motor (PMSM) contain both odd and even harmonic currents. Due to the motor’s pole–slot conductance modulation, the interaction between the magnetic fields generated by these harmonic currents and the permanent magnet field results in harmonic radial vibrations of the motor. This paper analyzes the three-phase currents of the prototype and derives the radial magnetomotive force (MMF) spatiotemporal models for symmetric harmonic currents. By integrating Maxwell’s magnetic force formula and vibration response formula, the radial vibration models for symmetric harmonic currents are developed. The characteristics of vibrations caused by odd and even harmonic currents, as well as positive sequence and negative sequence harmonic currents, are analyzed separately. A cyclic sequence, low-frequency vibration suppression control method incorporating multiple harmonic current injections was designed. Experimental results of this method are compared with those obtained using an ideal sinusoidal current. Except for the second harmonic vibration, all other vibrations are significantly suppressed, with a maximum suppression rate of 92.28%. The total vibration level is reduced by 12.7619 dB, and the average torque is reduced by 0.67% with the total harmonic distortion of the current at 2.89%. The experimental results show that the vibration method in this paper has little influence on the average torque of the motor, the current distortion rate is small, and the vibration suppression effect is good.

Performance Characteristics of a Calculated Index Control Method for the phi Multianalyte Assay with Algorithmic Analysis.

Multianalyte assays with algorithmic analysis (MAAAs), such as the Prostate Health Index (phi), are increasingly utilized for generating disease risk scores. Currently, imprecision and bias in phi are not directly monitored by quality control (QC) assessment of the index but rather by QC assessment of individual components. This may not be adequately controlling for imprecision and bias in the calculated multicomponent phi value itself. Inter- and intra-assay phi precision was compared to precision of the individual component assays. QC measurements from total prostate-specific antigen (PSA), free PSA, and p2PSA were used to calculate a single calculated phi QC metric (PHIc). The frequency of QC failure of PHIc, relative to individual components QC by Westgard rules (13S and 22S), was determined. The effects of varying analyte component assay bias on the resulting PHIc metric were also examined. Average measured phi imprecision (6.7% CV) was higher than individual phi analyte component imprecision (3.9-4.5% CV) across 2 Beckman Coulter Unicel DxI 800 instruments. A retrospective examination of PHIc QC over 84 quality control determinations was concurrently carried out for both PHIc and component assay failure patterns, which were dependent on SDs utilized for Westgard evaluation. Finally, reinforcing nonlinear changes in PHIc were observed in select cases of introduced simulated bias of individual component measurements. An additional calculated phi QC measure can be introduced to monitor MAAA precision/bias, and in principle calculated index controls may represent a complementary supplemental QC method that could be applied to other MAAA indices.

SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters

The growing use of sensors in fresh waters for water quality measurements generates an increasingly large amount of data that requires quality assurance (QA)/quality control (QC) before the results can be exploited. Such a process is often resource-intensive and may not be consistent across users and sensors. SentemQC (QA-QC of high temporal resolution sensor data) is a cost-efficient, and open-source Python approach developed to ensure the quality of sensor data by performing data QA and QC on large volumes of high-frequency (HF) sensor data. The SentemQC method is computationally efficient and features a six-step user-friendly setup for anomaly detection. The method marks anomalies in data using five moving windows. These windows connect each data point to neighboring points, including those further away in the moving window. As a result, the method can mark not only individual outliers but also clusters of anomalies. Our analysis shows that the method is robust for detecting anomalies in HF sensor data from multiple water quality sensors measuring nitrate, turbidity, oxygen, and pH. The sensors were installed in three different freshwater ecosystems (two streams and one lake) and experimental lake mesocosms. Sensor data from the stream stations yielded anomaly percentages of 0.1%, 0.1%, and 0.2%, which were lower than the anomaly percentages of 0.5%, 0.6%, and 0.8% for the sensors in Lake and mesocosms, respectively. While the sensors in this study contained relatively few anomalies (<2%), they may represent a best-case scenario in terms of use and maintenance. SentemQC allows the user to include the individual sensor uncertainty/accuracy when performing QA-QC. However, SentemQC cannot function independently. Additional QA-QC steps are crucial, including calibration of the sensor data to correct for zero offsets and implementation of gap-filling methods prior to the use of the sensor data for determination of final real-time concentrations and load calculations.

Variable impedance control for synchronizer position tracking based on gain scheduling

Achieving fast and stable synchronizer position tracking control can greatly improve the power and comfort of transmission shifting. In order to further improve the shift speed and stability of the synchronizer, this article takes the synchronizer control of a three-speed seamless transmission as the research object and proposes a variable impedance control method based on gain scheduling. Firstly, dynamic models of the shift actuator and synchronizer was established. Then, an impedance outer loop controller and a position inner loop controller were designed. In the impedance outer loop, adjust the synchronizer reference position based on the difference between the reference shift force and the actual shift force. At the same time, based on the actual control requirements of different motion stages of the synchronizer, the optimal impedance control parameters for each stage are selected for gain scheduling control. In the position inner loop, a prescribed performance-active disturbance rejection control is adopted, which solves the problem of system disturbance while ensuring the instantaneous performance of position tracking. Finally, simulations and bench tests were conducted on the position tracking control of synchronizer. The simulation and bench test results show that the control strategy proposed achieves fast and stable synchronizer position tracking, with superior control performance, which preliminarily verifies the effectiveness of the control strategy.

Genome-wide methylation and transcriptome differential analysis of skeletal muscle in broilers with valgus-varus deformity

ABSTRACT 1. Valgus-varus deformity (VVD) is a disease that severely affects leg function in broilers and for which there is no effective control method current available. Although DNA methylation has an important impact on most physiological and pathological processes, its involvement in skeletal muscle growth and development in VVD broilers is unknown. In this study, genome-wide DNA methylation was analysed in VVD-affected and normal broilers using whole genome resulphite sequencing. 2. The results showed that in the cytosine-phosphoric acid-guanine (CG) sequence environment there was a methylation rate of about 55% and 4,265 differentially methylated regions (DMRs) were found in the CG. Of these, 550 were located in the promoter, 547 in the exon region, and 1,718 in the intron region. 3. All differentially methylated genes (DMGs) were analysed for enrichment of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The GO was enriched in pathways related to protein degradation such as proteasome complex, endopeptidase complex and extracellular region. The KEGG pathways were enriched in signalling pathways related to protein degradation and catabolism such as proteasome, nitrogen metabolism, adherens junction and alanine. 4. Protein interactions analysis revealed that FOS, MYL9, and FRAS1 had a high degree of interactions, in which the DNA methylation level of the MYL9 promoter region was negatively correlated with mRNA expression level. Further studies showed that 5-azacytidine (5-AzaC) inhibited DNMT1 and DNMT3A gene expression and promoted MYL9 expression. 5. This study systematically investigated overall DNA methylation patterns in the leg muscle of VVD and normal broilers. It screened common differential genes in conjunction with transcriptomic data to further identify genes associated with muscle growth and development. This study provides new insights to better understand the pathogenesis of VVD from an epigenetic perspective.

Anti-bump control for switched systems with decentralised adaptive event-triggering

A control system may exist bumps during mode transitions and discontinuous control input would seriously affect performance. In this paper, the anti-bump (or bumpless) switching control for triggered and switched systems under injection attacks is studied. This control method combines decentralised adaptive event-triggering mechanism (DAETM) and anti-bump condition. The main work is as follows: First, a DAETM is considered to allocate network resources properly and resist data injection attacks. Then, a data buffer is adopted to consolidate data from decentralised channels for ensuring timely utilisation. In addition, a controller gain condition is presented to achieve the bumpless switching between subsystems. Moreover, by adopting piecewise Lyapunov function method, sufficient conditions are obtained for triggered switched systems to be exponentially stable and anti-bump under injection attacks. Finally, the design method effectiveness is verified by actual circuit simulation.

Feedback Stabilization of Chain-Like MDOF Nonlinear Structural Systems Under Purely Parametric Gaussian White Noises

A feedback control method is proposed to asymptotically stabilize the chain-like multi-degree-of-freedom (MDOF) nonlinear structural system under purely parametric Gaussian white noises. First, the motion equation of a chain-like nonlinear structural system with [Formula: see text] coupled elements and an undetermined control law is derived. It is reduced to a one-dimensional partially averaged Itô stochastic differential equation of the controlled Hamiltonian by applying the stochastic averaging method, which bypasses the challenge of calculating multiple Lyapunov exponents. Next, based on the dynamical programming principle, the dynamical programming equation of the averaged system with an undetermined cost function is established and addressed to obtain the optimal control law. Then, the Lyapunov exponent of the completely averaged Itô equation is derived. For the given requirement of stabilizing the system, the Lyapunov exponent is entirely fixed and used to analyze the stability of the originally chain-like MDOF nonlinear structural system. A controlled chain-like 8-DOF nonlinear structural system is taken as an example to illustrate the application and effectiveness of the proposed procedure and the effect of control forces on stabilizing the system.