In this paper, the low energy cost synchronization control strategy of Markovian switching complex systems/networks is mainly studied and analyzed through multiple perspectives. Firstly, in order to achieve synchronization of Markovian switching complex networks with low energy cost, a control scheme based on the optimal node selection strategy that does not depend on the network coupling strength is improved, and a finite-time controller with a simpler structure is constructed. Secondly, based on the event-triggered control strategy an effective trigger event is designed to achieve the low energy cost synchronization of Markovian switching complex networks on the basis of reducing the information transmission and interaction between networks. Finally, the two control strategies mentioned in this paper are compared and analyzed from multiple perspectives through numerical simulations to better guide practical engineering.

A recent study demonstrated that the use of feedforward information with conventional Predictive Functional Control (PFC) leads to unexpected inconsistencies, with subsequent negative impacts on tuning and behaviour. A proposal was made to define the coincident point differently and shown to reduce the lag in the closed-loop PFC responses and applied to some systems with benign dynamics. Other recent work has looked at parameterisations of the future input to deal with challenging open-loop dynamics and significantly extended the range of problems for which PFC can be effective. This paper combines the two concepts, and thus proposes an algorithm that has both more effective and simple tuning than original PFC as well as being applicable to a range of challenging dynamics.

In the regeneration mode, precise control of the Diesel Oxidation Catalyst (DOC) outlet temperature is crucial for the complete combustion of carbon Particulate Matter (PM) in the subsequent Diesel Particulate Filter (DPF) and the effective conversion of Nitrogen Oxides (NOx) in the Selective Catalytic Reduction (SCR). The temperature elevation process of the DOC involves a series of intricate physicochemical reactions characterized by high nonlinearity, substantial time delays, and uncertainties. These factors render effective and stable control of the DOC outlet temperature challenging. To address these issues, this study proposes an approach based on Long Short-Term Memory (LSTM) neural networks for Model Predictive Control (MPC), emphasizing precise control of the Diesel Oxidation Catalyst’s outlet temperature during the regeneration mode. To tackle the system’s nonlinear characteristics, LSTM is employed to construct a predictive model for the outlet temperature of the Diesel Oxid... [more]

In grid-connected photovoltaic (PV) systems, power quality and voltage control are necessary, particularly under unbalanced grid conditions. These conditions frequently lead to double-line frequency power oscillations, which worsen Direct Current (DC)-link voltage ripples and stress DC-link capacitors. The well-known dq frame vector control technique, which is effective under normal conditions, struggles with oscillatory component management in unbalanced grid conditions. To address this issue, this paper presents an advanced control approach designed for grid-connected PV inverters. The proposed approach is effective at reducing oscillations in the DC-link voltage at double the grid frequency, thereby enhancing system stability and component longevity. This method introduces a feedback control method designed to regulate oscillatory components that appeared within the dq frame and suppress the DC-link voltage oscillations under imbalance conditions, including single line-to-ground (SL... [more]

In nuclear power systems, centrifugal pumps often need to operate under extreme conditions. However, accurately determining the cavitation status of centrifugal pumps under such extreme conditions is challenging. To improve the recognition accuracy of the three statuses of non-cavitation, incipient cavitation, and severe cavitation while improving the anti-interference capability of the monitoring system, this study extracted cavitation features from centrifugal pumps’ motor current and vibration signals under three different operational conditions. It fused the features using feature-level multi-source information fusion (MSIF) based on the backpropagation neural network (BPNN) or support vector machine (SVM) to construct a cavitation status recognition model and analyzed the results to compare with those of recognition without information fusion. The results show that, compared with one signal source, MSIF can significantly improve the recognition accuracy of cavitation statuses. Com... [more]

This paper introduces an innovative adaptive control approach utilizing a nonlinear filter for a specific subset of nonlinear discrete-time systems, considering the presence of both input and output noise. The system can be transformed into a nonlinear autoregressive moving average with exogenous inputs (NARMAX) model. The concept of discrete Nussbaum gain is introduced to address the theoretical constraint associated with unknown directions of feed-forward or control gains, and the extended adaptive tuning sequence is introduced to facilitate the acceleration of parameter updating. In the case of no noise, asymptotical output tracking and global stability are achieved with the adaptive control. Further, in the presence of input noise and output noise, a novel nonlinear filter is designed to generate a more accurate filtered output, which improves the control system’s ability to adapt and track accurately. Finally, examples are provided to showcase the effectiveness and precision of th... [more]

This study aims to address the problem of poor synchronous accuracy when facing a time-varying load in conventional load-sensitive synchronous drive systems. The new electro-hydraulic load-sensitive (EHLS) diverter synchronous drive system was proposed by combining the diverter valve and the EHLS synchronous drive system. The variable pressure margin compensation control was proposed to further improve the system’s synchronous control performance. Based on the system control strategy and component mathematical model, the simulation models of the EHLS, EHLS synchronous, and EHLS diverter synchronous drive systems were established using AMESim, respectively, and the synchronous control performance of the systems was obtained. The simulation results show that the EHLS drive system realized the primary functions of the load-sensitive system and could realize the variable load-sensitive pressure margin control. The EHLS synchronous drive system had poor synchronous control accuracy, but var... [more]

Aiming at the problem of difficult fault diagnosis work caused by the difficulty of data acquisition of the bearing in the traction part of a coal mining machine, a method of ADAMS simulation and HHT feature extraction of the bearing fault of a coal mining machine is proposed. First of all, take the traction section bearing as the research object, use the virtual prototype in the establishment of the healthy state of coal mining machine traction section model based on the establishment of the bearing inner ring fault, rolling body fault, outer ring fault of the coal mining machine traction section dynamics model, and then after the EMD decomposition, each IMF component of the Hilbert transform, to obtain the signal in the time-frequency plane of the time-frequency joint characteristics, to get the HHT marginal spectra and to different Under different working conditions, the bearing vibration signal features are mined by quantitative feature extraction. Finally, a variety of mainstream... [more]

The aim of this work is to study the causes of accidents in chemical processes, develop a methodology for accident prevention via control, and illustrat its realization by examples using a variety of strategies. The general concept of critical situations was introduced systematically covering both emergency and pre-emergency situations. In large-scale chemical plants, examples of accidents are presented. Accident causes as a result of disturbances and control faults in technological processes are analyzed. Approaches for preventing accidents are considered. The revealing of critical situations is presented as a problem of pattern recognition, and the subtasks of the recognition are analyzed. An emergency scale based on the assessment of various states of the chemico-technological process is introduced and applied for distinguishing the different levels of accident. The real obstacles in the prevention of accidents via control are shown and analyzed. Matrices of critical situations with... [more]

Effective fluid-loss control in oil wells is a critical concern for the oil industry, particularly given the substantial reserves situated in carbonate reservoirs globally. The prevalence of such reservoirs is expected to rise with the slow depletion of hydrocarbons, intensifying the need to address challenges related to deteriorating reservoir properties post well-killing operations. This deterioration results in significant annual losses in hydrocarbon production at major oil enterprises, impacting key performance indicators. To tackle this issue, this study focuses on enhancing well-killing technology efficiency in carbonate reservoirs with abnormally low formation pressures. To address this issue, the authors propose the development of new blocking compositions that prevent the fluid loss of treatment fluids by the productive reservoir. The research tasks include a comprehensive analysis of global experience in well-killing technology; the development of blocking compositions; an i... [more]

The pre-tightening force loss (PTFL) of bolts is an important but underestimated cause of roadway instability. In mine anchorage systems, the actual pre-tightening force of bolts is only 50% to 80% of the design value. Through a case study at Xiahuo Coal Mine, it was found that the essential causes of PTFL are the increasing friction coefficient between supporting units controlled by factors such as pre-tightening torque levels, pre-tightening cycles, and surrounding rock roughness. This study investigates the behavioral characteristics of PTFL and its influence on surrounding rock failure in roadways. This research reveals a linear correlation between pre-tightening force and torque, with an average torque conversion coefficient of approximately 0.19. However, the PTFL increases with higher levels of pre-tightening torque, increasing pre-tightening cycles, and rougher surrounding rock conditions. For every 30 N·m increase in pre-tightening torque, the PTFL increases by approximately 1... [more]

This paper investigates the decentralized fuzzy control problems for nonlinear-state-unmeasured interconnected descriptor systems (IDSs) that utilize the observer-based-feedback approach and the proportional−derivative feedback control (PDFC) method. First of all, the IDS is represented as interconnected Takagi−Sugeno (T−S) fuzzy subsystems. These subsystems can effectively capture the dynamic behavior of the system through fuzzy rules. For the stability analysis of the system, this paper uses the free-weighing Lyapunov function (FWLF), which allows the designer to set the weight matrix, to achieve the desired control performance and design the controller more easily. Furthermore, the control problem can be transformed into a set of linear matrix inequalities (LMIs) through the Schur complement, which can be solved using convex optimization methods. Simulation results confirm the effectiveness of the proposed method in achieving the desired control objectives and ensuring system stabil... [more]

Organobentonites have been applied for the removal of two common non-steroidal anti-inflammatory drugs, ibuprofen (IBU) and diclofenac sodium (DS), from aqueous solutions. Two surfactants, one with and the other without benzyl group (octadecyldimethylbenzylammonium chloride, ODMBA, and hexadecyltrimethylammonium bromide, HDTMA), in amounts equivalent to 50, 75, and 100% of the cation exchange capacity of bentonite were used for the preparation of organobentonites. Successful modification of bentonite was confirmed by several methods: X-ray powder diffraction (XRPD), point of the zero charge (pHPZC), determination of exchanged inorganic cations in bentonite, determination of textural properties, and scanning electron microscopy (SEM). Kinetic and thermodynamic data on the adsorption of IBU and DS showed that drug adsorption was controlled by the type and the amount of surfactant incorporated into the bentonite and by their arrangement in the interlayer space and at the surface of organo... [more]

Backstepping-based fixed-time tracking control is proposed for a robotic arm system to solve the problem of trajectory tracking control under system uncertainties, which ensures the robotic arm system can realize stable tracking control within a fixed time independent of the initial state of the system. In addition, to address the uncertainties in the robotic arm system, a control strategy based on disturbance observer compensation is designed, which provides feed-forward compensation through the accurate estimation of the system uncertainties and enhances the system’s robustness. Finally, a two-link robotic arm model is used for simulation experiments, and the comparison results show that the control scheme designed in this article can effectively improve the robotic arm’s tracking accuracy and convergence speed.

Model uncertainty creates a largely open challenge for industrial process control, which causes a trade-off between robustness and performance optimality. In such a case, we propose a generalized conditional feedback (GCF) system to largely eliminate conflicts between robustness and performance optimality. This approach leverages a nominal model to design an optimal control in the virtual domain and defines an ancillary feedback controller to drive the physical process to track the trajectory of the virtual domain. The effectiveness of the proposed GCF scheme is demonstrated in a simulation for six typical industrial processes and three model-based control methods, and in a half-quadrotor system control test. Furthermore, the GCF scheme is open to existing optimal control and robust control theories.

A time−frequency residual convolution neural network (TFRCNN) was proposed to identify various rolling bearing fault types more efficiently. Three novel points about TFRCNN are presented as follows: First, by constructing a double-branch convolution network in the time domain and the frequency domain, the respective features in the time domain and the frequency domain were extracted to ensure the rich and complete feature representation of raw data sources. Second, specific residual structures were designed to prevent learning degradation of the deep network, and global average pooling was adopted to improve the network’s sparsity. Third, TFRCNN was better than the other models in terms of prediction accuracy, robustness, generalization ability, and convergence. The experimental results demonstrate that the prediction accuracy rate of TFRCNN, trained using mixing load data, reached 98.88 to 99.92% after optimizing the initial learning rate and choosing the optimizer and loss function.... [more]

In this paper, a closed hydrostatic drive powertrain (HSDP) composed of an engine, a variable pump, a variable motor, and an energy-efficient adaptive speed-regulating controller (ADC) based on power following is proposed and investigated. The controller can more than guarantee accurate regulation of motor speed through online efficiency estimation based on established loss models of the pump and the motor. It also facilitates the optimal efficiency control of the engine and hydrostatic system through two redundant control freedoms of the HSDP system, making an energy-saving adjustment of the motor speed. At the same time, the controller can prevent engine overload stall and high system pressure by limiting the displacement of the pumps and motors in real time based on the system loads to improve the automatic adaptability of the system to varying loads. Field testing experiments performed by means of a heavy transportation vehicle under different conditions were conducted to verify th... [more]

This study explores the progression of global healthcare and medical waste (HMW) disposal technologies and emerging practices in China including the COVID-19 pandemic period through patent technology innovation analysis. Trends were identified through both the Derwent Innovation Index database and bibliometric methods. Based on the bibliometric analysis of 4128 patents issued from 2002 to 2021, the development status and research trends of HMW disposal technology were revealed. Regarding patents, China significantly advanced post-2011. However, a large number of applications are filed only in China and are more focused on domestic rather than overseas markets. As the pandemic remains a threat, and increasing amounts of medical waste are generated, new technologies are being sought in China that will be safer for humans and the environment, and will also be in line with the zero waste technology trend. Incineration and waste crushing are core methodologies in medical waste disposal. Fut... [more]

A Distributed Control System is a concept of Network Control Systems whose applications range from industrial control systems to the control of large physical experiments such as the ALICE experiment at CERN. The design phase of the Distributed Control Systems implementation brings several challenges, such as predicting the throughput and response of the system in terms of data-flow. These parameters have a significant impact on the operation of the Distributed Control System, and it is necessary to consider them when determining the distribution of software/hardware resources within the system. This distribution is often determined experimentally, which may be a difficult, iterative process. This paper proposes a methodology for modeling Distributed Control Systems using a combination of Finite-State Automata and Petri nets, where the resulting model can be used to determine the system’s throughput and response before its final implementation. The proposed methodology is demonstrated... [more]

The shape and convexity are crucial quality assessment indicators for hot-rolled electrical steel strips. Besides bending rolls, shifting rolls, and the original roll profile, the thermal roll profile also plays a significant role in controlling the shape and convexity during the hot-rolling process. However, it is always overlooked due to its dynamic uncertainty. To solve this problem, it is necessary to achieve online cooling-status control for the local thermal expansion of rolls. Based on the existing structure of a mill, a pair of special partition-cooling beams with an intelligent cooling system was designed. For high efficiency and practicality, a new online predictive model was established for the dynamic temperature field of the hot-rolling process. An equivalent treatment was applied to the boundary condition corresponding to the practical cooling water flow. In addition, by establishing the corresponding target distribution curve for the partitioned water flow cooling, onlin... [more]

In intelligent process monitoring and fault detection of the modern process industry, conventional methods mostly consider singular characteristics of systems. To tackle the problem of suboptimal incipient fault detection in nonlinear dynamic systems with non-Gaussian distributed data, this paper proposes a methodology named Gap-Mixed Kernel-Dynamic Canonical Correlation Analysis. Initially, the Gap metric is employed for data preprocessing, followed by fault detection utilizing the Mixed Kernel-Dynamic Canonical Correlation Analysis. Ultimately, fault identification is conducted through a contribution method based on the T2 statistic. Furthermore, a comparative analysis was conducted using Canonical Variate Analysis, Dynamic Canonical Correlation Analysis, and Mixed Kernel-Dynamic Canonical Correlation Analysis on the Tennessee Eastman process. Experimental results indicate varying degrees of improvements in the detection rate, false alarm rate, missed detection rate, and detection ti... [more]

In recent years, the Broad Learning System (BLS) has been acknowledged for its potential to revolutionize traditional artificial intelligence methods due to its short training time, strong interpretability, and simple structure. In the evolution of BLS, Prof. C. L. Philip Chen’s team introduced the Fuzzy Broad Learning System (FBLS) by replacing the feature nodes of BLS with fuzzy subsystems, thereby further reducing the training time. However, the traditional FBLS, with its straightforward structure, falls short in achieving higher fault diagnosis accuracy when handling raw vibration signals. This paper presents a bearing fault diagnosis approach employing multi-domain feature selection and the fuzzy broad learning system (MS-FBLS), aiming to enhance the diagnostic accuracy of FBLS through multi-domain feature selection. Primarily, a set of 49 features spanning time domain, frequency domain, time-frequency domain, and entropy values is extracted from the original vibrational signals.... [more]

Regarding the difficulty of extracting fault information in the faulty status of UAV (unmanned aerial vehicle) engines and the high time cost and large data requirement of the existing deep learning fault diagnosis algorithms with many training parameters, in this paper, a small-sample transfer learning fault diagnosis algorithm is proposed. First, vibration signals under the engine fault status are converted into a two-dimensional time-frequency map by multiple simultaneous squeezing S-transform (MSSST), which reduces the randomness of manually extracted features. Second, to address the problems of slow network model training and large data sample requirement, a transfer diagnosis strategy using the fine-tuned time-frequency map samples as the pre-training model of the ResNet-18 convolutional neural network is proposed. In addition, in order to improve the training effect of the network model, an agent model is introduced to optimize the hyperparameter network autonomously. Finally, e... [more]

In some applications of microgrids and distributed generation, it is necessary to feed islanded or stand-alone loads with high-quality voltage to provide low total harmonic distortion (THD). To fulfil these demands, an LC filter is usuallyconnected to the output terminals of power electronics converters. A cascaded voltage and current control loop with pulse-width modulation schemes are used to regulate the voltages and currents in these systems. However, these strategies have some drawbacks, particularly when multiple-input−multiple-output plants (MIMO) are controlled using single-input−single-output (SISO) design methods. This methodology usually produces a sluggish transient response and cross−coupling between different control loops and state variables. In this paper, a model predictive control (MPC) strategy based on the concept of optimal switching sequences (OSS) is designed to control voltage and current in an LC filter connected to a three-level neutral-point clamped converter... [more]

In the dynamic and complex environment of industrial control rooms, operators are often inundated with numerous tasks and alerts, leading to a state known as task overload. This condition can result in decision fatigue and increased reliance on cognitive biases, which may compromise the decision-making process. To mitigate these risks, the implementation of decision support systems (DSSs) is essential. These systems are designed to aid operators in making swift, well-informed decisions, especially when their judgment may be faltering. Our research presents an artificial intelligence (AI)-based framework utilizing dynamic influence diagrams and reinforcement learning to develop a powerful decision support system. The foundation of this AI framework is the creation of a robust, interpretable, and effective DSS that aids control room operators during critical process disturbances. By incorporating expert knowledge, the dynamic influence diagram provides a comprehensive model that captures... [more]