Complex Working Conditions Research Articles

Stability discussion and application study of pseudo-corner models

Accurate plastic flow modelling under complex working conditions is crucial for metal deformation simulations. Recently, some advanced pseudo-corner models have been developed to describe corner effects and analyze strain localization problems. The present work consists of three parts. The first part discusses the intrinsic stability of the pseudo-corner model class, which forms the premise of application analysis. The second part applies the pseudo-corner models and the associated flow rule (AFR) to buckling onset estimation, plastic post-buckling analysis and shear band analysis. The experimental conditions are strictly reproduced and the optimal model parameters are determined. The results reveal that the pseudo-corner models and AFR are indistinguishable in the buckling onset estimation. AFR overestimates the post-buckling strength of circular tubes under axial compression, and cannot reproduce the shear band development during sheet bending; while the pseudo-corner models have better prediction performance in both scenarios. The results also suggest that the parameter values of pseudo-corner models are apparently inconsistent in the above two types of problems. Then in the third part, two representative influencing factors including strain gradient plasticity and initial imperfections are discussed, and this inconsistency is finally attributed to the shortwave surface defect which however is usually neglected by previous studies.

Research on Fault Prediction of Power Devices in Rod Control Power Cabinets Based on BiTCN-Attention Transfer Learning Model

The Insulated Gate Bipolar Transistor (IGBT) is the key power device in the rod control power cabinet of nuclear power plants; its reliable operation is of great significance for ensuring the safe and economical operation of the nuclear power plants. Therefore, it is necessary to conduct fault prediction research on IGBT to achieve better condition-based maintenance and improve its operational reliability. However, power cabinets often operate under multiple, complex working conditions, so predicting IGBT faults from single working condition data usually has limitations and low accuracy. Its failure probability has an important relationship with the actual operating conditions of the cabinet. In order to improve the reliability and maintainability of the control power cabinet in nuclear power plants, this paper takes IGBTs in the rod control power cabinet as the object and makes full use of the data of IGBTs under multiple working conditions to carry out research on the cross-condition fault prediction of IGBTs under multiple-source working conditions. A transfer learning (TL) model based on a bidirectional time convolutional network (BiTCN) combined with attention was proposed to solve the problem of low accuracy of cross-operating fault prediction in a multi-source domain. Firstly, an IGBT fault simulation model was built to collect the life cycle state data of the module under different working conditions. Then, after pre-processing such as removing outliers, kernel principal component analysis (KPCA) was used to integrate all source domain data, obtain source domain characterization data, and train the BiTCN-attention model. Finally, the BiTCN-attention model trained in the source domain was transferred, and the model was fine-tuned according to the target domain data. Simulation results show that the accuracy of the proposed BiTCN-attention transfer learning prediction method can reach more than 99%, which is significantly better than that of the recurrent neural network transfer learning (RNN-TL) model, long short-term memory network transfer learning (LSTM-TL) model, gated cyclic unit transfer learning (GRU-TL) model, and time convolutional network transfer learning (TCN-TL) model. This method can not only reduce the inconsistency of fault characteristic values caused by changes in working conditions but also accurately predict the degradation trend when only early fault data are available, providing an effective solution for IGBT fault prediction across working conditions in multi-source domains.

Load recognition of connecting-shaft rotor system under complex working conditions

A method for qualitatively recognizing the load of the rolling equipment's connecting-shaft rotor system is proposed in this paper due to the complexity of rolling production conditions and the limitations of single source response signals. The method is oriented towards fusing the vibration and motor's current information. First, singular value decomposition and wavelet packet analysis are used to preprocess the two types of response signals. Then, the Bayesian estimation method in feature-level fusion achieves qualitative recognition and analysis of rotor system load types. Corresponding load experiments are completed on a load recognition test platform based on vibration and the motor's current signals. The research results show that the load recognition method based on fusion information can recognize the type of load excitation with a recognition accuracy of 91.7 %, higher than other single-source response signal methods. Therefore, the feasibility of the aforementioned theoretical methods is verified.

Applications and analyses of ammonia slip control based on precise ammonia injection technologies guided by big data

Abstract After the implementation of ultra-low emission policies, the pollutant emissions from the coal-fired power generation systems in China have been further reduced, which creates more critical requirements for the control accuracies of denitrification systems using selective catalytic reduction technology and controls of ammonia slip. This article presents big data-based technologies for controlling ammonia slip, through precise ammonia injections, which were applied and demonstrated for a flue gas denitrification system of a Chinese coal-fired power plant. Through examinations and analyses of the basic operating conditions of the unit and parameters, such as NOx emission control efficiency, ammonia injection amount of the denitrification system, and non-uniformity of NOx concentrations in the denitrification zone were compared before and after the implementation of these technologies, the outcome proves that this artificial intelligence algorithm based on big data can effectively solve the automatic control problem of denitrification system under complex working conditions such as varied unit loads, day and night operation changed coal source. In addition to the effective controls of the ammonia slip of the system, the controls of NOx emission of the system become more stabilized, creating a successful example for wider applications of these precise ammonia injection control technologies in the future. Analyses show that NOx non-uniformities can be reduced by more than 50% under both stable and variable load conditions. NOx fluctuations at the unit outlet are tightly controlled within ±10 mg/Nm3 under variable load conditions and within ±5 mg/Nm3 under stable conditions. The average ammonia injection amounts under various load conditions have decreased by 15.7%.

Design and Modeling of Coreless Magnetoelectric Transducers for Snake-like Wave Energy Converters

With the development of the economy, people’s demand for energy is increasing, which has led to a shortage of fossil fuels. Wave energy is a widely distributed renewable energy source, and the development of wave energy generation technology can greatly alleviate the energy shortage problem. This study takes the snake-like wave energy converter (WEC) as an example and designs a coreless magnetoelectric transducer for it. The structure of the coreless magnetoelectric transducer is relatively simple, eliminating the iron core in the transducer, which can eliminate its own damping. At the same time, this structure can minimize the gap between the magnet and the coil, improve energy conversion efficiency, and work continuously under complex working conditions. This study takes two types of coreless magnetoelectric transducers as examples to analyze. This study aims to establish equivalent magnetic circuit models for the coreless magnetoelectric transducers, explore the effects of different magnets on the performance of the transducers, and optimize the parameters in the transducers. We used simulation software to analyze the transducer and verify the accuracy of the models. Finally, prototypes of the coreless magnetoelectric transducers were made, and a testing system for the transducer was established to test its energy conversion capability. Our experiments show that coreless magnetoelectric transducers have good energy conversion capabilities and can be used as transducers for snake-like WECs. At the same time, this type of transducer can also be applied to other types of WECs, providing a new approach for the research of WECs.

Degradation Modeling and RUL Prediction of Hot Rolling Work Rolls Based on Improved Wiener Process.

Hot rolling work rolls are essential components in the hot rolling process. However, they are subjected to high temperatures, alternating stress, and wear under prolonged and complex working conditions. Due to these factors, the surface of the work rolls gradually degrades, which significantly impacts the quality of the final product. This paper presents an improved degradation model based on the Wiener process for predicting the remaining useful life (RUL) of hot rolling work rolls, addressing the critical need for accurate and reliable RUL estimation to optimize maintenance strategies and ensure operational efficiency in industrial settings. The proposed model integrates pulsed eddy current testing with VMD-Hilbert feature extraction and incorporates a Gaussian kernel into the standard Wiener process to effectively capture complex degradation paths. A Bayesian framework is employed for parameter estimation, enhancing the model's adaptability in real-time prediction scenarios. The experimental results validate the superiority of the proposed method, demonstrating reductions in RMSE by approximately 85.47% and 41.20% compared to the exponential Wiener process and the RVM model based on a Gaussian kernel, respectively, along with improvements in the coefficient of determination (CD) by 121% and 19.76%. Additionally, the model achieves reductions in MAE by 85.66% and 42.61%, confirming its enhanced predictive accuracy and robustness. Compared to other algorithms from the related literature, the proposed model consistently delivers higher prediction accuracy, with most RUL predictions falling within the 20% confidence interval. These findings highlight the model's potential as a reliable tool for real-time RUL prediction in industrial applications.

Fault diagnosis of rolling bearing under complex working conditions based on time-frequency joint feature extraction-deep learning

The same independent distribution is not obeyed for the data collected under complex working conditions such as time-varying speed or loading, and the fault characteristic information is insufficient, resulting in low accuracy by using traditional methods. To solve the above problem, a fault diagnosis method based on time-frequency joint feature extraction combined with deep learning is proposed. Firstly, the original vibration signal is processed by variational mode decomposition (VMD) to obtain several intrinsic mode functions (IMFs), then the sensitive components are selected by calculating the steepness values of each IMF. Subsequently, the characteristic features of the selected sensitive component in time-domain, frequency-domain and time-frequency domain are calculated to form the time-frequency joint feature. The sparse attention mechanism (SAM) is combined with the advantages of recurrent neural network (RNN) and convolutional neural network (CNN) to form a hybrid deep learning model (SAM-RNN-DCNN). Finally, the time-frequency joint features are combined with the hybrid model for fault diagnosis. Experimental verifications are carried out by using data sets under variable rotational speed, variable load and strong noise interference, and the analysis results show that the proposed method has high diagnostic accuracy, good diagnostic performance and robustness under complex working conditions.

Stress Analysis of Ice Layers on Fan Rotor Blades in Aeroengines

Fan rotor blades are critical components of aeroengines. When an aircraft flies in icing conditions, ice accumulation on the fan surface may lead to periodic shedding. Severe ice shedding can cause mechanical damage to the engine, posing significant safety risks. This study investigates ice accretion on the surface of ROTOR 67 rotor blades and their stress distribution. This paper presents an in-depth analysis of the internal stress distribution of ice layers on the surface of rotating blades under different icing times and rotational speeds, especially the stress concentration of the areas at the contact surface between the ice layer and the blade. This study predicts the stress distribution of ice layers under different working conditions more accurately, which helps to identify potential ice layer fracture risks in advance. Moreover, it integrates the icing process, stress distribution, and shedding mechanism for analysis, providing a comprehensive perspective. It considers the interaction of various environmental factors and operating conditions, offering an in-depth understanding of ice layer behavior under complex working conditions.

Wind Turbine Bearing Failure Diagnosis Using Multi-Scale Feature Extraction and Residual Neural Networks with Block Attention

Wind turbine rolling bearings are crucial components for ensuring the reliability and stability of wind power systems. Their failure can lead to significant economic losses and equipment downtime. Therefore, the accurate diagnosis of bearing faults is of great importance. Although existing deep learning fault diagnosis methods have achieved certain results, they still face limitations such as inadequate feature extraction capabilities, insufficient generalization to complex working conditions, and ineffective multi-scale feature capture. To address these issues, this paper proposes an advanced fault diagnosis method named the two-stream feature fusion convolutional neural network (TSFFResNet-Net). Firstly, the proposed method combines the advantages of one-dimensional convolutional neural networks (1D-ResNet) and two-dimensional convolutional neural networks (2D-ResNet). It transforms one-dimensional vibration signals into two-dimensional images through the empirical wavelet transform (EWT) method. Then, parallel convolutional kernels in 1D-ResNet and 2D-ResNet are used to extract multi-scale features, respectively. Next, the Convolutional Block Attention Module (CBAM) is introduced to enhance the network’s ability to capture key features by focusing on important features in specific channels or spatial areas. After feature fusion, CBAM is introduced again to further enhance the effect of feature fusion, ensuring that the features extracted by different network branches can be effectively integrated, ultimately providing more accurate input features for the classification task of the fully connected layer. The experimental results demonstrate that the proposed method outperforms other traditional methods and advanced convolutional neural network models on different datasets. Compared with convolutional neural network models such as LeNet-5, AlexNet, and ResNet, the proposed method achieves a significantly higher accuracy on the test set, with a stable accuracy of over 99%. Compared with other models, it shows better generalization and stability, effectively improving the overall performance of rolling bearing vibration signal fault diagnosis. The method provides an effective solution for the intelligent fault diagnosis of wind turbine rolling bearings.

Research on Typical Fault Diagnosis of Gear based on GHM Multiwavelet Transform and MCKD Algorithm

Background: Gear is the key part of the transmission part of mechanical equipment, which has a high failure rate under complex working conditions or long-term operation, directly affecting the reliability of mechanical equipment. To realize online monitoring of gears and predict gear faults, whether the diagnosis of typical gear faults is accurate and timely is a key link, so it is necessary to study the diagnosis of typical gear faults. Objective: This paper aims to propose a method that can effectively diagnose typical faults of gears and is easy to program, which is beneficial to realize online monitoring of rotating machinery equipment faults. Method: A typical gear fault diagnosis method based on GHM multiwavelet transform and maximum correlation kurtosis deconvolution algorithm (MCKD) is proposed in this paper. The measured vibration signal is decomposed into components with multiple scale spaces and frequency bands through the GHM multiwavelet transform. Then, the MCKD algorithm is run to suppress the noise of GHM multiwavelet transform coefficients, and the periodic components containing fault information are retained and reconstructed. The typical faults such as gear pitting and broken teeth are tested. Results: GHM multiwavelet transform combined with MCKD algorithm can accurately locate the frequency band containing fault information from the vibration signal with multiple modulations, double sideband, and multi-frequency interference and effectively extract the fault frequency and its frequency multiplication. The root means a square error of the extracted fault frequency is 0.08. This method has a good noise suppression effect and high accuracy in extracting fault frequency. Conclusion: The method proposed in this paper is helpful in realizing automatic programming of gear fault diagnosis and is of great significance in realizing real-time monitoring and online diagnosis of rotating machinery equipment faults. More related patents will appear in the future.

Uniaxial tensile mechanical properties of NEPE propellant under wide temperature and wide strain rate

Abstract In order to gain insight into the mechanical properties of the NEPE propellant under a range of temperature and strain rate conditions, the uniaxial tensile mechanical property test of NEPE propellant with a wide temperature range of −40°C-70°C and a wide strain rate range of 0.0012s−1-1s−1 has been carried out based on the in-situ testing device of solid propellant microfabrication under complex working conditions and the supporting high and low temperature integrated machine. Additionally, the morphology of the solid particles of the propellant was observed and analyzed during stretching using the microscope equipment provided by the stretching machine. The results show that it has been demonstrated that both temperature and strain rate exert a more pronounced influence on the mechanical properties of propellants; at low temperatures, it has been demonstrated that the stress-strain curve of the propellant exhibits a more pronounced inflection point as the strain rate increases; the maximum tensile strength of the propellant demonstrates a gradual increase with a reduction in temperature and an increase in strain rate. Furthermore, a linear double logarithmic relationship is observed with the strain rate. In contrast, the maximum elongation exhibits no discernible pattern of change with temperature and strain rate; when observed during stretching, it was found that the main forms of propellant damage destruction at low temperatures were dehumidification between the particles and the matrix and particle rupture.

Machine Learning-Driven precise design of stable OLED Materials: Predicting and enhancing Multi-State C-N bond dissociation energies

The intrinsic stability of organic light-emitting diode (OLED) materials critically hinges on the bond dissociation energies (BDEs) of fragile bonds, particularly C-N bonds. Despite the necessity of considering BDEs in multiple electronic states due to the complex working conditions of organic materials in OLED devices, comprehensive exploration remains challenging. Herein, we constructed a high-quality dataset, CNBDE-24, comprising nearly 1500 data points for BDEs across multiple electronic states, obtained through an active learning-guided density functional theory (DFT) calculation strategy. Machine learning models trained on the CNBDE-24 dataset exhibit excellent accuracy in predicting BDEs for OLED materials in neutral, triplet excited (T1), and anionic states, achieving an adjusted coefficient of determination exceeding 0.90 on the test set and bypassing the need for high-cost calculation. Moreover, utilizing neutral state BDEs as pre-training data significantly reduces model error and provides deeper insights into the relationships between different BDEs. We find that BDEs in neutral are influenced by the electronic properties of the nitrogen atom, as indicated by descriptors such as Gasteiger charges. Incorporating C-N bonds into aromatic systems and slightly dispersing N atom charges can inherently increase multi-state BDEs without altering luminescent properties. Additionally, anionic BDEs are determined by the extent of charge transfer during bond cleavage and can be regulated by modifying the C-side fragment. High-throughput virtual screening reveals a new stable purple emitter with a dominant 0–0 transition at 360 nm, which was validated through DFT and excited-state property evaluations. Strategies for enhancing BDEs in multiple resonance thermally activated delayed fluorescence materials without altering excited-state properties are also demonstrated.

Design and Verification of Thermal Control System of Communication Satellite

The multiple working modes, complex working conditions, frequent changes in external heat flux, and high power consumption of communication satellites all pose great difficulties to their thermal design. This paper mainly describes the design of a thermal control system for high-power communication satellites. Firstly, new efficient heat transfer technologies and thermal control materials for spacecraft are introduced. Secondly, the structure and internal heat source of the satellite are introduced. Thirdly, the external heat fluxes are analyzed, and the position of the heat dissipation surface and extreme conditions are confirmed. Then, a thermal control system is designed around the difficulties of thermal control. With heat pipes, the temperature uniformity of +Y deck, −Y deck, and +Z deck increased by 8 °C, 9.9 °C, and 34.2 °C, respectively. Furthermore, the maximum temperature of the power controller, secondary power supply, bidirectional frequency converter, and solid discharge decreased by 32.5 °C, 22.0 °C, 14.0 °C, and 164 °C, respectively. Finally, a thermal balance test is performed. The test results show that the temperatures of the solid-state power amplifier, on-board computer, power controller, secondary power supply, and bidirectional frequency converter meet the requirements of the thermal control indices. In addition, the temperature of thermal-sensitive components such as batteries and the storage tank also meets the requirements. The thermal design scheme is reasonable and feasible, and the thermal balance test verifies the correctness of the thermal design.

Balance Sparse Decomposition Method with Nonconvex Regularization for Gearbox Fault Diagnosis

As an important part of rotating machinery, gearboxes often fail due to their complex working conditions and harsh working environment. Therefore, it is very necessary to effectively extract the fault features of the gearboxes. Gearbox fault signals usually contain multiple characteristic components and are accompanied by strong noise interference. Traditional sparse modeling methods are based on synthesis models, and there are few studies on analysis and balance models. In this paper, a balance nonconvex regularized sparse decomposition method is proposed, which based on a balance model and an arctangent nonconvex penalty function. The sparse dictionary is constructed by using Tunable Q-Factor Wavelet Transform (TQWT) that satisfies the tight frame condition, which can achieve efficient and fast solution. It is optimized and solved by alternating direction method of multipliers (ADMM) algorithm, and the non-convex regularized sparse decomposition algorithm of synthetic and analytical models are given. Through simulation experiments, the determination methods of regularization parameters and balance parameters are given, and compared with the L1 norm regularization sparse decomposition method under the three models. Simulation analysis and engineering experimental signal analysis verify the effectiveness and superiority of the proposed method.

Intelligent fault diagnosis of hoisting systems under complex working conditions using deep graph convolutional generative adversarial networks with limited data

Owing to the difficulty of collecting adequate fault data from hoisting systems under complex working conditions, data-driven diagnostic methods suffer from decreased accuracy due to inadequate fault information. To deal with this problem, we introduce an intelligent few-shot fault diagnosis approach using a deep graph convolutional generative adversarial networks (DGCGANs) algorithm. The goal of the proposed method is to acquire a more comprehensive few-shot fault feature information, thus facilitating the generation and augmentation of scarce data. Results of two cases including laboratory and real-world industrial datasets demonstrate the viability and efficacy of our approach for diagnosing few-shot faults. Specifically, the proposed DGCGAN method outperforms existing advanced diagnostics, thereby offering superior accuracy in identifying hoisting system faults with limited data. Finally, the practicality and adaptability of the proposed DGCGAN-based fault diagnosis method are further substantiated through comprehensive ablation studies.

Design and Research of Intelligent Valve Control

The design and research of intelligent valve control system aims to improve the efficiency and accuracy of fluid control in industrial process automation. By integrating advanced sensing technology, communication protocols and intelligent algorithms, the precise control of valve position, pressure and flow rate is achieved. STM32 controller and a variety of sensors are used in the system to realize real-time monitoring and feedback control of valve status. The control strategy based on PID algorithm enables the valve to maintain stable operating performance under complex working conditions. In addition, the safety and reliability of the system are also considered, and the robustness of the system is improved by redundant design and fault detection mechanism. The experimental results show that the designed intelligent valve control system has significant advantages in improving response speed, control precision and energy efficiency, and has a wide range of industrial application prospects.

Sensitivity Analysis of Fatigue Life for Cracked Carbon-Fiber Structures Based on Surrogate Sampling and Kriging Model under Distribution Parameter Uncertainty

The quality and reliability of wind turbine blades, as core components of wind turbines, are crucial for the operational safety of the entire system. Carbon fiber is the primary material for wind turbine blades. However, during the manufacturing process, manual intervention inevitably introduces minor defects, which can lead to crack propagation under complex working conditions. Due to limited understanding and measurement capabilities of the input variables of structural systems, the distribution parameters of these variables often exhibit uncertainty. Therefore, it is essential to assess the impact of distribution parameter uncertainty on the fatigue performance of carbon-fiber structures with initial cracks and quickly identify the key distribution parameters affecting their reliability through global sensitivity analysis. This paper proposes a sensitivity analysis method based on surrogate sampling and the Kriging model to address the computational challenges and engineering application difficulties in distribution parameter sensitivity analysis. First, fatigue tests were conducted on carbon-fiber structures with initial cracks to study the dispersion of their fatigue life under different initial crack lengths. Next, based on the Hashin fatigue failure criterion, a simulation analysis method for the fatigue cumulative damage life of cracked carbon-fiber structures was proposed. By introducing uncertainty parameters into the simulation model, a training sample set was obtained, and a Kriging model describing the relationship between distribution parameters and fatigue life was established. Finally, an efficient input variable sampling method using the surrogate sampling probability density function was introduced, and a Sobol sensitivity analysis method based on surrogate sampling and the Kriging model was proposed. The results show that this method significantly reduces the computational burden of distribution parameter sensitivity analysis while ensuring computational accuracy.

A gear fault diagnosis method based on reactive power and semi-supervised learning

Abstract In gearbox gear fault diagnosis based on motor current signals, the gear fault characteristic frequency component is often overshadowed by the fundamental frequency component of the current. In addition, the complex working conditions during actual production and use make it difficult to collect gear operation monitoring data containing labeled feature information. To address the above problems, a semi-supervised learning method based on reactive power signals is proposed for gear fault diagnosis of gearboxes. First, the method utilizes the Hilbert transform to process the current signal of the drive motor in the mechanical system, from which the reactive power is constructed. Then, the reactive power signal is analyzed by spectral analysis as a basis for gear fault diagnosis. Subsequently, the GAF-CNN-MTDL(Gramian angular field—convolutional neural network-mean teacher deep learning) fault diagnosis model is proposed to convert the reactive power signal into a two-dimensional image by using the GAF, and the semi-supervised training method of the average teacher is applied to input the fault dataset into the gear fault diagnosis model which is based on the CNN as the main backbone after the fault dataset has been divided into the labeled and the unlabeled dataset in accordance with a certain ratio. Finally, the gear fault dataset is used for method validation. The experimental outcomes demonstrate the method’s proficiency in effectively emphasizing the fault feature information pertaining to the gear part, and the introduced GAF-CNN-MTDL fault diagnosis model enables the utilization of a minimal number of labeled samples to achieve highly accurate gear fault diagnosis.

Research on seismic suppression performance of vertical circulation stereo garage based on friction damper and steel pipe

AbstractThe conventional vertical circulation stereo garage is susceptible to deformation and instability when subjected to extreme seismic loads and complex working conditions. The present study introduces the combination arrangement of steel pipe and friction damper to optimize vertical circulation stereo garage while also investigating their seismic suppression performance under three representative seismic waves (EL‐CNENTRO, TH1TG025(TH), and RH1TG025(RH)). The results showed that the garage structure hasbetter seismic suppression performance when the combination of steel pipes and friction dampers is applied in Scheme 4 compared to relying solely on diagonal tie rod, steel pipe or friction damper. And Scheme 4 achieves inhibition rates of up to 61.27%, 22.12%, 56.07%, and 12.84% for overall maximum deformation response, maximum Y‐direction deformation response, vertex acceleration response, and bottom shear response, respectively. This enhancement significantly improves the garage's seismic stability, providing valuable theoretical references for advancing the development and market promotion of vertical circulation stereo garages.

Research on a Chassis Stability Control Method for High-Ground-Clearance Self-Propelled Electric Sprayers

In response to the complex working conditions and poor driving stability of high-clearance self-propelled sprayers, a nonlinear model of the chassis power system was established based on the independently controllable torque of each wheel of the developed electric sprayer. A layered-architecture chassis drive control strategy was formulated, and a stability control framework comprising an instability judgment module, an upper controller, and a lower controller was constructed based on the analysis of the impact of the centroid slip angle, the yaw rate, and the wheel slip rate on driving stability. An ideal reference model was established based on the seven-degree-of-freedom model of the sprayer, and the current state of the sprayer body was determined using the instability judgment module. A drive anti-slip controller and a yaw moment controller based on fuzzy PID theory and sliding mode control theory were designed. Additionally, an optimal torque distribution algorithm was developed based on tire characteristics to rationally allocate drive torque to each wheel, ensuring the stability of the sprayer during operation. Simulation tests were conducted using MATLAB/Simulink to evaluate the sprayer under four different driving conditions during transport and field operations. The test results showed that the “SMC + optimal distribution” control method in the chassis stability control strategy reduced the maximum deviations of the yaw rate and centroid slip angle by an average of 89.5% and 13.6%, respectively, compared to no control. The wheel slip rate during straight driving was well maintained at around 15%, enhancing the driving stability of the sprayer.