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In order to effectively identify and extract the composite fault characteristics of the gear transmission system, a composite fault diagnosis method combining nonlinear mode decomposition (NMD) and Teager energy operator demodulation is proposed. Because the envelope demodulation of Hilbert transform has the disadvantages of large amount of calculation and end effect, it uses Teager energy operator to solve the problem of large amount of calculation, and NMD solves the problem that the fault signal features is not easy to extract under the mode aliasing. First, the NMD method is used to decompose the fault simulation signal, and the nonlinear modal component with practical physical significance is obtained. Secondly, the Teager energy operator demodulation is carried out for the nonlinear modal components, and the demodulation results are analyzed to verify the feasibility of the method. Then, the method is applied to the composite fault diagnosis of gear pitting wear in gear transmission system, and the characteristic frequency obtained from the test data is compared with the calculated characteristic frequency. The comparative analysis shows that the method can separate the fault characteristic frequencies of large and small gears. The comparative analysis with EMD and EEMD methods in simulation signal analysis and experimental research shows that this method is superior.

In this paper, a short-term wind speed prediction model, called CEEMDAN-SE-Improved PIO-GRNN, is proposed to optimize the accuracy of the short-term wind speed forecast. This model is established on account of the optimized General Regression Neural Network (GRNN) method optimized by three algorithms, which are Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Sample Entropy (SE), and Pigeon Inspired Optimization (PIO), separately. Firstly, decomposing the original wind speed sequences into several subsequences with different complexity by CEEMDAN. Then, the complexity of each subsequence is judged by SE and the similar subsequences are combined into a new sequence to reduce the scale of calculation. Afterwards, the GRNN model optimized by improved PIO is used to predict each new sequence. Finally, the predicted results are superposed as the eventual predicted value. Implementing the prediction for the wind speed data of a wind field in north China within 30 days by applying the different prediction models, namely, GRNN, CEEMDAN-GRNN, Improved PIO-GRNN, and CEEMDAN-SE-Improved PIO-GRNN which are proposed in this paper. Comparing the prediction curves of different models with the fitting curve of the actual wind speed shows that the optimal fitting effect and minimum error value are included in CEEMDAN-SE-Improved PIO-GRNN model. Specifically, the values of mean squared error (MSE), mean absolute error (MAE) and weighted mean absolute percentage error (WMAPE) separately decrease by 0.6222, 0.3334, and 8.5766%, which compare with the single prediction model GRNN. Meanwhile, diebold-mariano (DM) test shows that the prediction ability of the two models is significantly different. The above statements indicate the proposed model does great advance in the precision of short-term wind speed prediction.

An integrated fault tolerant controller is proposed for vehicle chassis system. Based on the coupled characteristics of vertical and lateral system, the fault tolerant controller mainly concentrates on the cooperative control of controllable suspension and lateral system with external disturbances and actuator faults. A nine-DOF coupled model is developed for fault reconstruction and accurate control. Firstly, a fault reconstruction mechanism based on sliding mode is introduced; when the sliding mode achieves, actuator fault signals can be observed exactly through selecting appropriate gain matrix and equivalent output injection term. Secondly, an active suspension controller, a roll moment controller and a stability controller is developed respectively; the integrated control strategy is applied to the system under different driving conditions: when the car is traveling straightly, the main purpose of the integrated strategy is to improve the vertical performance; the lateral controller including roll moment control and stability control will be triggered when there is a steering angle input. Simulations experiments verify the performance enhancement and stability of the proposed controller under three different driving conditions.

Extensive research efforts have been made to address the motion control of rigid-link electrically-driven (RLED) robots in literature. However, most existing results were designed in joint space and need to be converted to task space as more and more control tasks are defined in their operational space. In this work, the direct task-space regulation of RLED robots with uncertain kinematics is studied by using neural networks (NN) technique. Radial basis function (RBF) neural networks are used to estimate complicated and calibration heavy robot kinematics and dynamics. The NN weights are updated on-line through two adaptation laws without the necessity of off-line training. Compared with most existing NN-based robot control results, the novelty of the proposed method lies in that asymptotic stability of the overall system can be achieved instead of just uniformly ultimately bounded (UUB) stability. Moreover, the proposed control method can tolerate not only the actuator dynamics uncertainty but also the uncertainty in robot kinematics by adopting an adaptive Jacobian matrix. The asymptotic stability of the overall system is proven rigorously through Lyapunov analysis. Numerical studies have been carried out to verify efficiency of the proposed method.

An implementation of a two-stage piece-wise linearization method for reduction of the thermocouple approximation error is presented in the paper. First, the whole thermocouple measurement chain of a transducer is described, and possible error is analysed to define the required level of accuracy for linearization of the transfer characteristics. Evaluation of linearization functions and analysis of approximation errors are performed by the virtual instrumentation software package LabVIEW. The method is appropriate for thermocouples and other sensors where nonlinearity varies a lot over the range of input values. The basic principle of this method is to first transform the abscissa of the transfer function by a linear segment look-up table in such a way that significantly nonlinear parts of the input range are expanded before a standard piece-wise linearization. In this way, applying equal-segment linearization two times has a similar effect to non-equal-segment linearization. For a given examples of the thermocouple transfer functions, the suggested method provides significantly better reduction of the approximation error, than the standard segment linearization, with equal memory consumption for look-up tables. The simple software implementation of this two-stage linearization method allows it to be applied in low calculation power microcontroller measurement transducers, as a replacement of the standard piece-wise linear approximation method.

Measurement of DC current is critically important in a wide range of applications, such as in photovoltaic power generation and DC power transmission. In this paper, the DC current measuring method based on magnetic fluids with orthogonal magnetic fields is studied. Firstly, the induced voltage of measurement coil, which contains a series of harmonics and the amplitude of its fundamental component is 2.5 times larger than that of other components, is derived and analyzed. Then, influence factors of the induced voltage are systematically studied. As a result, with a DC component injection into the exciting current, the frequency of the induced voltage becomes a half of that without a DC component, and when the DC component reaches to or exceeds the amplitude of the AC component, the induced voltage nearly has no distortion. Finally, the input-output characteristic of the measurement method is examined, and the accuracy, measuring range, and sensitivity are discussed, indicating that the linearity of the foundamental component root mean square (RMS) value curve is better than the one of the voltage amplitude curve under the same DC current range, and the sensitivities of induced voltage amplitude and the fundamental component RMS value are 5.948 and 3.717 mΩ, respectively.

This paper analyzed the noise distribution of three pulleys and one belt system theoretically and experimentally. Aiming at the influence of the tensioner on the transmission noise of the synchronous belt, on the premise of theoretical analysis of the influence of the tensioner on the transmission noise of the synchronous belt, the noise test of the synchronous belt transmission system with and without the tensioner was carried out under the same experimental conditions. Based on the principle of acoustic array measurement, a three-pulley and one belt noise test device was designed. The noise pressure distribution nephogram and amplitude–frequency characteristic curve were obtained by noise tests at different speeds. Through the comparison of the results of two groups of tests, the influence rule of the tensioner on the transmission noise of the synchronous belt was obtained. The results show that the tensioner can effectively avoid the resonance of the synchronous belt, and the noise amplitude of the three-pulley and one belt drive system is 3 dB higher after the tensioner is installed. It provides a basis for vibration and noise reduction of the engine timing transmission system.

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

In the paper industry, to determine the installation positions of concentration sensor and flow sensor of pulp pipe transportation system, the length of pipe inlet section of pulp fluid must be determined. In order to solve the problem that it is difficult to determine the length of pipe inlet section of pulp fluid, the paper presents a method to determine the length of pipe inlet section of pulp fluid, and the relationship between the length of inlet section and pulp fluid parameters is studied by this method. Firstly, CFD (Computational Fluid Dynamics) method is applied to obtain flow velocity and solid phase concentration distribution data of pulp fluid at different axial positions in the pipe. Then, Pearson correlation coefficient method is applied to analyze these data. Finally, the length of pipe inlet section of pulp fluid is determined according to the obtained correlation coefficient, and the relationship between the length of pipe inlet section of pulp fluid and initial average flow velocity and solid phase concentration is obtained. The conclusion shows that the method can well obtain the length of pipe inlet section of pulp fluid, which provide some theoretical basis for the design of pulp pipe transportation system.

Bypass pipeline inspection gauges have the advantages of low cost and bringing no consumption in transportation efficiency and have been widely used in pipe cleaning, inspecting, and maintaining operations. The moving speed of bypass pipeline inspection gauges will seriously affect the results of the operations, so there are strict requirements on the moving speed of bypass pipeline inspection gauges. Because the moving speed of pipeline inspection gauge is difficult to measure or control in real time, it is important to predict it. This paper studies the influencing factors and their impact methods of pipeline inspection gauges’ motion. Through the combination of computational fluid dynamics simulation and friction mathematical model, the relationship between the value of the bypass hole diameter and the pipeline inspection gauges’ moving speed was studied. Under the selected research conditions, when the diameter of the bypass hole is increased from 0.1 to 0.5 m, the moving speed of pipeline inspection gauge in water and crude oil is, respectively, decreased from 2.779 to 0.589 m/s and from 2.777 to 0.373 m/s, and the relationship between them can be approximately described by a function. Based on this principle, the moving speed of pipeline inspection gauge can be predicted mathematically. The experiments also indicate that the density and dynamic viscosity of the transport medium and the deformation amount of the bypass pipeline inspection gauge sealing disk will affect the movement state of pipeline inspection gauge in the pipeline. This research has guiding significance for the design of the pipeline inspection gauges’ structure size, which is beneficial to the pipeline robot to better meet the needs of cleaning, inspecting, and maintaining operations, and has reference value for related researches.

The rapid development of online education, knowledge sharing, big data, and artificial intelligence technology has brought innovation to education. With the popularization of online education, the variety of teaching resources and the amount of curriculum resources have exploded, and traditional curriculum platforms have been unable to meet the growing demand. The paper builds a cloud computing platform based on Hadoop, which is used for big data analysis and application curriculum resource management. It elaborates on the architecture, functional design, resource storage, and implementation and test of the cloud resource platform. The cloud platform applies to and serves the field of education, which improves resource utilization and sharing, and it can provide better services for teachers, students, and staff.

To address the tracking control problem of heavy-haul trains (HHTs) with input saturation during operation, an anti-saturation sliding mode (SMES) control method based on dynamic auxiliary compensator (DAC) is presented. Firstly, an HHT model with nonlinear coupling and uncertain disturbances is built. Secondly, a new type of DAC is introduced to overcome the difficulty of traditional dynamic auxiliary compensator (TDAC) with a large upper bound on the compensation signal. Finally, an anti-saturation SMES control algorithm is designed to reduce the influence of input saturation on the tracking accuracy of each carriage. Simulation results verify the effectiveness of the algorithm in terms of tracking accuracy, anti-interference, and anti-saturation.

Steelmaking-refining-Continuous Casting (SCC) is a key process in iron and steel production. SCC scheduling is to determine an optimal schedule for the SCC process, which is a worldwide and important problem. High-quality SCC scheduling methods will help to allocate production resources effectively and increase the productivity. However, dynamic events (e.g. machine breakdown) may happen in the realistic SCC process, which will make the SCC schedule inexecutable or not optimal. In this case, SCC rescheduling is essential in order to obtain a new optimal schedule suitable for the current production environments. The SCC rescheduling can be modeled as hybrid flowshop rescheduling. In this paper, an Improved Imperialist Competitive Algorithm (IICA) is proposed to address the SCC rescheduling. In the proposed IICA, an empire initialization is first devised for constructing an initial population with diversity and certain quality. Moreover, multiswap-based local search and imperialist competition are designed to improve the exploitation ability of the IICA, while revolution and restart strategy are devised to enhance the exploration ability of the IICA. Comparison experiments with three kinds of ICA have shown the efficiency of the IICA.

The four-wheel independent driven electric vehicle (4WID-EV) can easily realize the four-wheel independent drive, which is convenient for the development and design of the direct yaw moment control system (DYC). Based on the theory of ADRC and the sliding mode control, a new type of DYC controller coordinated with the AFS controller using PID method is designed for the 4WID-EV in this paper. The coordinated control work area is divided according to the tire lateral force linear area. An improved particle swarm optimization algorithm which introduces linear decreasing inertia weight and annealing strategy is adopted to obtain the coordination work weight. The effectiveness of the DYC controller is verified by the Simulink simulation. It also shows that the performance is further improved after the coordinated control.

This paper proposes an aerostatic lubrication model with the use of finite difference method, and the solution of the model is designed with the combination of flux error feedback and grid parameter optimization. The model and solution are validated by the performance test on the slider with the diameter of orifice at 50 and 200 μm. Finally, the proposed model is applied to the optimal design of the aerostatic turntable, and the type, parameter and configuration of the restriction are determined by the optimal results. To guarantee the accuracy of key parts, the ultra-precision turning technology is applied to manufacture of turntable, and the runout of the end face meets the design requirements through the verification. The proposed model and solution are significant to the analysis and design of high-performance aerostatic equipments.

Today, most of the databases used for drug information mining are derived from the collection of many treatments under a single disease, and some special drug compatibility rules can be found from them. However, researchers’ exploration of medical data is not limited to this. The comparative analysis of drugs for different diseases has become a new research point. In this paper, the drug is used as a node, the relationship is the edge connecting the two nodes, the co-occurrence frequency of the drug is used as the weight of the edge to establish a network graph. We use the clustering algorithm of the weighted network graph center diffusion method combining the network topology and the edge weights to divide the network graph into communities. Then we proposed the Structural Clustering Algorithm on Weighted Networks (SCW), it helps to study the prescription of medical prescriptions and provides more scientific recommendations for auxiliary prescriptions. In the experiment, SCW is compared with the classic community discovery algorithm CPM, the network function modular analysis algorithm MCODE and the hierarchical network graph structure analysis algorithm BGLL. We analyze the results according to NMI, ARI and F-Measure. Finally, a case study of real data was conducted to ensure the correctness and effectiveness of the algorithm, and to obtain the potential drug combination in the medical prescription.

Modern network applications place higher demands on its controller, especially for those with time-varying delays and limited communication capacity. For such cases, the fuzzy system has already become an advanced and powerful tool to deal with the control problem in consideration of a guaranteed cost performance. In this paper, we introduce the event-triggered mechanism with quantization effect to the controller, which proves to be more effective in terms of the information transmission. We adopt the classical Lyapunov approach to find the sufficient conditions for the controller and we illustrate the effectiveness of the controller with a numerical simulation.

This paper presents a TADRC method via active disturbance rejection control (ADRC) with hyperbolic tangent function for path following of underactuated surface ships with input constraint, heading rate constraint, parameters uncertainties, as well as environment disturbances. The line of sight (LOS) guidance scheme that computes the desired heading angle on basis of cross tracking error and a look ahead distance, converts path following into heading control, and also renders good helmsman behavior. Moreover, hyperbolic tangent function is introduced to modify the linear extended state observer (LESO) to design a nonlinear observer (TESO) for promoting the estimation performance of the heading, heading rate and total disturbances including parameters uncertainties and environmental disturbances. Then, the linear error feedback control is modified by a nonlinear sliding mode control scheme with hyperbolic tangent function to handle the heading rate constraint and to obtain the better control action. Furthermore, the feedback control law is embedded in a standard Quadratic Programming (QP) cost function to handle the input constraints including rudder saturation and rudder rate limit. Finally, the comparison simulation demonstrates the effectiveness of the proposed method for the underactuated ship’s path following.

In order to improve the service life of the V-ribbed belt and the ride comfort of the vehicle, the friction noise of V-ribbed belt was studied by simulation and experiment. A theoretical model of V-ribbed belts vibration is established, and the conditions for unstable vibration are derived. Based on the theory of mode coupling, the causes of high frequency friction noise of V-ribbed belts are analyzed, and it is considered that the coupling between the vibration of the V-ribbed belts and the natural frequency of the belts’ cross section causes the self-excited vibration of the belt, resulting in high frequency friction noise. Firstly, ANSYS Workbench is used to identify the belts’ cross sections with 3, 4, and 6 wedges, and the natural frequencies of the belts’ cross sections are obtained. Then on the belt drive friction tester, the influences of tension and relative sliding speed on the frequency of high frequency friction noise is studied using a single factor test method, and the frequency response curve of high frequency friction noise is also analyzed. The results of the experiment and simulation show that the transverse vibration of the V-ribbed belts is closely related to the phase and amplitude of the high frequency friction noise, and the changes in tension and relative sliding speed do not affect the frequency of the high frequency friction noise. The frequency of high frequency friction noise has a good consistency with the natural frequency of belts’ cross section, which shows that mode coupling causes the strong self-excited vibration of the V-ribbed belts and the high frequency friction noise. It provides a method and theoretical basis for the control of friction noise.

A piecewise fast multi-power reaching law (PFMPRL) is proposed aiming at the problems of chattering and slow convergence in the reaching phase of sliding mode control (SMC). In this paper, the fast power reaching law and the double power reaching law are combined, and a nonlinear function is introduced to design the exponential term in PFMPRL. The proposed method ensures the characteristic of fast convergence of the system at all the phases of tendency. The characteristic of fixed-time convergence has also been satisfied. The study proves that the system state can converge to steady-state error bounds within a finite time in the presence of system uncertainty and bounded external disturbance. Compared with the existed methods, the proposed method has shorter convergence time and smaller steady-state error bound. To suppress the influence of model uncertainty and disturbance in system control, a non-linear disturbance observer (NDO) is introduced, and combined with the reaching law-based non-singular terminal sliding mode control (NTSMC), is applied to the cart inverted pendulum system. Simulation results and numerical analysis verify the effectiveness and superiority of this approach.

Stochastic disturbances play a profound problem in the power system, which have an important impact on the stability of the power system. The paper proposes the stability analysis of stochastic disturbance bounded value of linear power system, and presents that the stability of power system has bounded value under stochastic disturbance and additional disturbance, and gives the analysis process in combination with stochastic differentiation. The equation theory proposes a numerical solution based on mean stability to calculate the boundedness of infinite systems under the influence of stochastic disturbance and additional disturbance. The results show that the system has bounded value stability under the disturbance.

A Laplace ℓ 1 robust student’s T-filter is presented for satellites to estimate their attitude state despite severe measurement noise and modeling error. Although the student’s T-filter (STF) has the capability of handling measurement noise, it cannot address the unknown modeling error. It is further sensitive to the degree of freedom (DOF). Hence, the measurement covariance is updated by using the maximum correntropy criterion to accommodate the covariance of unknown modeling error in robust filtering design. Moreover, the Laplace distribution is derived to reduce the influence of the DOF parameter by forming a surrogate function of an optimization problem. Then, the majorization minimization approach is formulated to solve such an optimization problem and present the proposed filtering algorithm in the STF framework. Numerical simulation of applying the proposed attitude estimation scheme is performed by comparing it with the third/fifth-order cubature Kalman filters.

The determination of the optimal measurement area of the articulated arm measuring machine belongs to the multi-dimensional function optimization problem under complex constraints. To realize high-precision measurement of low-precision articulated arm measuring machine, we analyze the working principle and error source of the measuring machine, and establish the optimization target model of the optimal measurement area in this paper. We propose a method for determining the optimal measurement area of an articulated arm measuring machine based on improved FOA. The basic FOA algorithm is improved, the historical optimal individual and population centroid information are added in the population iteration update process, and the fruit fly individuals in each iteration are directly used as the taste concentration judgment value, which increases cooperation and information sharing among fruit fly individuals, and improves the global optimization ability and stability of the algorithm. In the designated area of the measuring machine, we have carried out comparative experiments on the optimization results of improved FOA and basic FOA, ACO, PSO, AL-SC-FOA, LGMS-FOA, IPGS-FFO. Experimental results show that the improved FOA, ACO, PSO, and IPGS-FFO algorithms do not fall into local optimum, and the optimal measurement area determined by them is consistent with the optimization results of other algorithms, and is superior to other algorithms in convergence speed and stability, so it is more suitable for determining the optimal measurement area of articulated arm measuring machine.

The visual guidance of AGV (automated guided vehicle) has gradually become one of the most important perception methods. Aiming at the problem that it is difficult to extract lane line accurately when AGV is running in complex working environment (such as uneven illumination, overexposure, lane line is not obvious, etc.), a scheme of lane line recognition under complex environment is proposed. Firstly, the variable scale image correction is carried out for the uneven illumination area in ROI (region of interest), and the threshold of Canny algorithm is adjusted adaptively according to the luminance of ROI region by Fuzzy-Canny algorithm; Secondly, the edge points matching the lane width feature are extracted by the way of aerial view. Finally, the curve fitting method based on RANSAC (Random Sample Consensus) is used to fit a curve with the lowest error rate and then get the lane center curve. The experimental results show that the processing algorithm used in this paper is feasible and effective, has strong robustness and fast computing performance, and can meet the requirements of intelligent AGV in various complex environments.

This manuscript aims for improving the vibrational behaviors of a cantilever beam model through an intermediate lumped mass via offering a new control methodology to suppress for such high oscillations of the system. The equation of the considered cantilever beam structure is gained applying Euler–Lagrange technique. Accordingly, the considered model is modified by mixing Integral Resonant Control (IRC) along with the Nonlinear Saturation Controller (NSC) as anew controller to the considered system. Due to the recommended control technique, the modified system model is studied and analyzed by the perturbation technique. Time histories figures of the measured system plus the new controller are involved to display the response before and after control. The frequency response figures of the modified model before and after new controller near simultaneous condition [Formula: see text] are gained. Each frequency-response curves have stable and unstable regions are determined numerically. Numerical results show the vibrations of the system are eliminated when adding combined IRC and NSC controllers. Finally, numerical outcomes are performed that illustrated an excellent agreement with the analytical ones. Comparison between this paper and recent papers of the cantilever beam are done.

The k-nearest neighborhoods (kNN) of feature points of complex surface model are usually isotropic, which may lead to sharp feature blurring during data processing, such as noise removal and surface reconstruction. To address this issue, a new method was proposed to search the anisotropic neighborhood for point cloud with sharp feature. Constructing KD tree and calculating kNN for point cloud data, the principal component analysis method was employed to detect feature points and estimate normal vectors of points. Moreover, improved bilateral normal filter was used to refine the normal vector of feature point to obtain more accurate normal vector. The isotropic kNN of feature point were segmented by mapping the kNN into Gaussian sphere to form different data-clusters, with the hierarchical clustering method used to separate the data in Gaussian sphere into different clusters. The optimal anisotropic neighborhoods of feature point corresponded to the cluster data with the maximum point number. To validate the effectiveness of our method, the anisotropic neighbors are applied to point data processing, such as normal estimation and point cloud denoising. Experimental results demonstrate that the proposed algorithm in the work is more time-consuming, but provides a more accurate result for point cloud processing by comparing with other kNN searching methods. The anisotropic neighborhood searched by our method can be used to normal estimation, denoising, surface fitting and reconstruction et al. for point cloud with sharp feature, and our method can provide more accurate result comparing with isotropic neighborhood.

This paper presents a scientific basis and reference for the application of ceramsite concrete in underground coal mines. Taking fly-ash ceramsite as raw material, a variety of mix proportions of C20 ceramsite concrete were proposed by changing the sand rate of concrete. The results showed that: (1) fly-ash ceramsite has different water absorption performance with different particle sizes. The water absorption stopped after soaked for 48 h. So, the ceramsite should be soaked for more than 48 h to prevent the cement from hydrating insufficient which affects the mechanical properties of the concrete. (2) Seven mix proportions of C20 ceramsite concrete were designed by changing the ceramsite proportion. The ceramsite proportion of 43% was determined as optimal scheme whose 28-day strength was 29.60 MPa and elastic modulus was 12.45 GPa. (3) The optimal scheme was applied and verified in the field. The early strength of ceramsite concrete promotes quickly, 3-day strength was 16.8 MPa, and the 28-day strength was 29.9 MPa. Compared with ordinary pebble concrete, ceramsite concrete can provide faster, higher strength support to the roadway. Meanwhile, ceramsite concrete have properties of lightweight, and its application will bring economic and social benefits.

This brief proposes a novel composite observer-based integral sliding mode tracking control algorithm for a class of nonlinear systems affected by both actuator faults and mismatched disturbances. First, different types of observers, including the extended state observer, the fault diagnosis observer, and the disturbance observer, are integrated to estimate the unknown system state, actuator faults, and mismatched disturbances timely. Then, in accordance with the estimation information, the integral sliding surface and the integral sliding mode controller are proposed, which can tolerate the actuator faults and reject the mismatched disturbances. Meanwhile, the state trajectories can be driven into the specified sliding surface in a finite time. Furthermore, not only the stability, but the favorable dynamical tracking and the output constraints of closed-loop augmented systems can be guaranteed. Finally, the validities of the proposed algorithm are embodied by the simulation results of typical A4D systems.

The series manipulator of the demolition robot has different working postures and large self-weight so that the bearing mass of the joint hydraulic cylinder is a variable value, and the change of mass affects the dynamic characteristics of the valve-controlled hydraulic cylinder system. In this paper, an equivalent mass estimation method of the big-arm joint hydraulic cylinder is given, and the natural frequency range of the big-arm hydraulic cylinder-load system is calculated. In order to suppress the influence of the change of the natural frequency and external disturbance of the hydraulic cylinder on the system, the linear active disturbance rejection control is adopted in the closed-loop position control. The simulation results show that the change of load mass of the joint hydraulic cannot be ignored when designing the joint position controller of multi-joint hydraulic manipulator. And the simulation tests prove that the linear active disturbance rejection control strategy keeps good dynamic characteristic and steady-state accuracy for the change of natural frequency and external disturbance of hydraulic cylinder and has good tracking performance for periodic sinusoidal signal. Linear active disturbance rejection control is robust in the variable mass control of the manipulator and meets the requirements of the joint position control of the heavy-duty hydraulic manipulator.

The multicriteria decision-making methodology is utilized to assess different portable hard disk drive alternatives, according to the purchaser/retailer/wholesaler liking with respect to various attributes. The hard disk drive comes under various types and has a number of attributes connected with it such as storage capacity, size, data transfer speed and physical dimensions. The modern market caters to a wide variety of customer needs. Therefore, it becomes the need of the hour to present a simple technique to select the best alternative for purchaser/retailer/wholesaler to satisfy their combined needs. Among the multicriteria decision-making methods, the more simple and widely used technique weighted aggregated sum product assessment is utilized in this work. The data of different hard disk drives were collected that were available in the Indian market and 24 different models of five brands were considered in decision-making. The equal weights method and objective weights method, that is standard deviation method, are utilized to allocate weights of significance to the criteria. The ranks obtained with simple additive weighting, weighted product method and weighted aggregated sum product assessment are presented, and final ranks are considered with weighted aggregated sum product assessment method because it is an amalgamation of the simple additive weighting and weighted product method. The result reveals that Western Digital comes out to be the first choice as a brand because the top three models belong to them with both equal and objective weights. While utilizing these techniques, a consumer can purchase the best hard disk drive and it is also very advantageous for merchants and sellers to aid users in procuring their gadgets while manufacture of hard disk drive can produce their product with unique technological features aimed at particular users. Furthermore, the subjective weights can be considered to select the best alternative.

Artificial intelligence is one of the hottest research topics in computer science. In general, when it comes to the needs to perform deep learning, the most intuitive and unique implementation method is to use neural network. But there are two shortcomings in neural network. First, it is not easy to be understood. When encountering the needs for implementation, it often requires a lot of relevant research efforts to implement the neural network. Second, the structure is complex. When constructing a perfect learning structure, in order to achieve the fully defined connection between nodes, the overall structure becomes complicated. It is hard for developers to track the parameter changes inside. Therefore, the goal of this article is to provide a more streamlined method so as to perform deep learning. A modified high-level fuzzy Petri net, called deep Petri net, is used to perform deep learning, in an attempt to propose a simple and easy structure and to track parameter changes, with faster speed than the deep neural network. The experimental results have shown that the deep Petri net performs better than the deep neural network.

The proton-exchange membrane fuel cell is a promising power source for automobile industry because of its zero pollution. However, its stack structure always faces increased contact resistance caused by assembly errors, leading to substantial energy loss during the working period. To enhance its output performance, the influence of assembly errors on contact resistance is studied for proton-exchange membrane fuel cell. The mechanical simulation model of fuel cell assembly process is established to provide contact resistance distribution with different assembly errors. An improved global sensitivity analysis method is proposed to evaluate the influence coefficient of each assembly error term on contact resistance based on a series of randomized simulation data. The case study of a single-layer fuel cell demonstrates the proposed method achieves higher efficiency than traditional sensitivity analysis methods, and finds out key assembly errors in regard to reducing contact resistance.

Providing security to the citizens is one of the most important and complex task for the governments around the world which they have to deal with. Street crimes and theft are the biggest threats for the citizens and their belonging. In order to provide security, there is an urgent need of a system that is capable of identifying the criminal in the crowded area. This paper proposes a facial recognition system using Local Binary Patterns Histogram Face recognizer mounted on drone technology. The facial recognition capability is a key feature for a drone to have in order to find or identify the person within the crowd. With the inception of drone technology in the proposed system, we can use it as a surveillance drone as well through which it can cover more area as compared to the stationary system. As soon as the system identifies the desired person, it tags him and transmits the image along with the co-ordinates of the location to the concerned authorities using mounted global positioning system. Proposed system is capable of identifying the person with the accuracy of approximately 89.1%.

This article proposes a novel robust adaptive wheel slip rate tracking control method with state observer. First, a modified tracking differentiator is proposed based on a combination of tangent sigmoid function with terminal attraction factor and linear function to improve convergence speed and avoid chattering phenomenon, and then, the modified tracking differentiator is used as state observer to smooth and estimate the states of the system. Second, a robust adaptive wheel slip rate tracking control law with fuzzy uncertainty observer and modified adaptive laws is derived based on Lyapunov-based method. The fuzzy uncertainty observer is used for estimating and compensating the additive uncertainty, and the modified adaptive laws are used for estimating the unknown optimal weight vector of the fuzzy uncertainty observer and the multiplicative uncertainty. Finally, the performance of the robust adaptive wheel slip rate tracking control method is verified based on the model-in-the-loop simulation system.

Wireless sensor network is a collection of sensor nodes designed with different routing capabilities to operate on real-world applications. In extreme environments, real-time applications of wireless sensor network ensure exchange of data, a difficult one between the sensor nodes, when less resources are consumed. Therefore, researchers are developing a routing protocol including optimal routing procedures to increase the longevity of the networks. In this paper, an improved route extension architecture is developed in wireless sensor network environment. This Improved Greedy Perimeter Stateless Routing is proposed to offer an improved transmission coverage and reduced power consumption capability. It deploys a periodic broadcast of hello message (or control messages) including the positional information between the sensor nodes. The experimental result concludes that the Improved Greedy Perimeter Stateless Routing method achieves improved routing capabilities than the traditional hybrid protocols like LEACH and Greedy Perimeter Stateless Routing.

In restructured power systems, transmission congestion is an imperative issue. Establishment of solar photovoltaic system at appropriate areas is likely to relieve congestion in transmission lines in the restructured power systems. Congestion management technique by utilizing solar photovoltaic sources, using an improved teaching learning–based optimization, is investigated in this article. Bus sensitivity factors which have the direct influence on the congested lines are utilized to locate the solar photovoltaic sources at appropriate areas. Congestion management is figured as an optimization problem with a goal of limiting the congestion management price utilizing the improved teaching learning–based optimization approach, which espouses the self-driven learning principle. IEEE-30 bus test system is simulated and tested in MATLAB environment so as to demonstrate the viability of the suggested methodology than different methodologies.

Many applications use the Global Positioning System data that provide rich context information for multiple purposes. Easier availability and access of Global Positioning System data can facilitate various mobile applications, and one of such applications is to infer the mobility of a user. Most existing works for inferring users’ transportation modes need the combination of Global Positioning System data and other types of data such as accelerometer and Global System for Mobile Communications. However, the dependency of the applications to use data sources other than the Global Positioning System makes the use of application difficult if peer data source is not available. In this paper, we introduce a new generic framework for the inference of transportation mode by only using the Global Positioning System data. Our contribution is threefold. First, we propose a new method for Global Positioning System trajectory data preprocessing using grid probability distribution function. Second, we introduce an algorithm for the change point–based trajectory segmentation, to more effectively identify the single-mode segments from Global Positioning System trajectories. Third, we introduce new statistical-based topographic features that are more discriminative for transportation mode detection. Through extensive evaluation on the large trajectory data GeoLife, our approach shows significant performance improvement in terms of accuracy over state-of-the-art baseline models.

This paper focuses on the disturbance rejection control problem for inertial stabilization of long-distance laser positioning with the movable platform. Due to various disturbances of the movable platform, the positioning system has significant disturbances that affect the positioning accuracy. Moreover, the nonminimum-phase property of the inertial stabilization system leads to great challenges for designing traditional disturbance-observer-based as well as rejection control methods. In this paper, a dual-compensator disturbance-observer-based control algorithm is proposed to ensure a much stronger rejection of disturbances than those of conventional methods. In particular, it is proven that the two compensators in the proposed method effectively estimate disturbances in different frequency regions. Furthermore, the analytical tuning laws for the proposed dual-compensator disturbance-observer-based control method are presented. The experimental setup including the laser positioning platform demonstrated the validity of the proposed method, which effectively rejected various disturbances.

This study uses a three-axis Gimbal model to inertially stabilize a platform that can be used to feed smooth images from a camera. In this article, three-axis Gimbal performance analysis is presented. An inertial measurement unit responds to movement and a three-phase brushless DC motor with 14 poles and 12 coils is used to rule out vibrations and movement from the surroundings. The controller combines sliding-mode control and model-free-adaptive control to design a novel control method based on data, which can decrease the computational time and difficulty of a nonlinear system. Simulations on MATLAB prove the efficiency of the given method. The simulation results validate that the designed controller has improved position control than the traditional proportional integral derivative, model-free-adaptive control, and model-free-learning-adaptive control.

In this research, the characterization test on nonlinear vibration of a fibre-reinforced composite thin plate is studied. First, in order to improve the efficiency and precision of the test, a laser scanning vibration system is designed and developed. Then, test methods and procedures of the harmonic distortion and nonlinear time-varying damping of such thin plates are proposed from the time-domain perspective. Corresponding test methods and procedures of nonlinear vibration properties such as the hard/soft stiffness and amplitude-dependent damping are also presented from the frequency-domain perspective. Finally, the TC500 carbon fibre/resin composite plate is selected as an example for the research to carry out a case study. Potential nonlinear vibration phenomena of such plates are characterized by the proposed test methods. It has been proved that the related test system and techniques adopted in this paper can provide an important reference for the establishment of the nonlinear vibration test methodology of fibre-reinforced composite structures.

A novel performance seeking control) method based on Beetle Antennae Search algorithm is proposed to improve the real-time performance of performance seeking control. The Beetle Antennae Search imitates the function of antennae of beetle. The Beetle Antennae Search has better real-time performance because of the objective function only calculated twice in Beetle Antennae Search at each iteration. Moreover, the Beetle Antennae Search has global search ability. The performance seeking control simulations based on Beetle Antennae Search, Genetic Algorithm and particle swarm optimization are carried out. The simulations show that the Beetle Antennae Search has much better real-time performance than the conventional probability-based algorithms Genetic Algorithm and particle swarm optimization. The simulations also show that these three probability-based algorithms can get better engine performance, such as more thrust, less specific fuel consumption and less turbine inlet temperature.

This paper proposes an input shaping technique for efficient payload swing control of a tower crane with cable length variations. Artificial neural network is utilized to design a zero vibration derivative shaper that can be updated according to different cable lengths as the natural frequency and damping ratio of the system changes. Unlike the conventional input shapers that are designed based on a fixed frequency, the proposed technique can predict and update the optimal shaper parameters according to the new cable length and natural frequency. Performance of the proposed technique is evaluated by conducting experiments on a laboratory tower crane with cable length variations and under simultaneous tangential and radial crane motions. The shaper is shown to be robust and provides low payload oscillation with up to 40% variations in the natural frequency. With a 40% decrease in the natural frequency, the superiority of the artificial neural network–zero vibration derivative shaper is confirmed by achieving at least a 50% reduction in the overall and residual payload oscillations when compared to the robust zero vibration derivative and extra insensitive shapers designed based on the average operating frequency. It is envisaged that the proposed shaper can be further utilized for control of tower cranes with more parameter uncertainties.

In this article, a dynamic output feedback based sliding mode learning control is proposed for uncertain mechanical system. After giving the model of uncertain mechanical system, the uncertainty and disturbance of it are discussed and they are assumed to be mismatched. The velocity of the uncertain mechanical system is assumed to be unmeasurable, and then a dynamic output feedback control strategy is utilized here. A dynamic output feedback-based sliding surface is constructed. The parameters of the designed surface are solved by Lyapunov function approach. Then a sliding mode learning controller is proposed for uncertain mechanical system to overcome the chattering of traditional sliding mode control. Finally, a numerical simulation is given to show the effectiveness of the proposed controller.

This paper investigates the issue of finite-time tracking control for flexible-joint robots. In the design scheme, the unknown continuous function is identified by a fuzzy system. By introducing the command filter technique, “explosion of complexity” problem which arises from repeated differentiation of virtual controllers is avoided. Meanwhile, errors resulting from the first-order filters can be reduced with the introduced compensation signal. Besides, the proposed method ensures that the tracking performance could be achieved within a limited time. Eventually, the simulation is given to demonstrate the effectiveness of the proposed scheme.

In this article, a novel continuous-time optimal tracking controller is proposed for the single-input-single-output linear system with completely unknown dynamics. Unlike those existing solutions to the optimal tracking control problem, the proposed controller introduces an integral compensation to reduce the steady-state error and regulates the feedforward part simultaneously with the feedback part. An augmented system composed of the integral compensation, error dynamics, and desired trajectory is established to formulate the optimal tracking control problem. The input energy and tracking error of the optimal controller are minimized according to the objective function in the infinite horizon. With the application of reinforcement learning techniques, the proposed controller does not require any prior knowledge of the system drift or input dynamics. The integral reinforcement learning method is employed to approximate the Q-function and update the critic network on-line. And the actor network is updated with the deterministic learning method. The Lyapunov stability is proved under the persistence of excitation condition. A case study on a hydraulic loading system has shown the effectiveness of the proposed controller by simulation and experiment.

In this article, an enhanced 2-degree-of-freedom internal model control strategy for typical industrial processes with time-delay is developed. For the proposed controller, it is composed of an inner loop feedback controller which is designed based on the internal model control theory and a weighted set-point tracking controller. Note that the adjustment of set-point tracking performance and disturbance rejection characteristics can be decoupled by employing the developed strategy, which indicates that more degrees of freedom are obtained for the proposed controller design; thus, better ensemble performance and stronger robustness are anticipated by regulating these two controllers separately, which may not be achieved in the conventional internal model control method. Case studies on two kinds of stable processes with time-delay verify the effectiveness of the proposed scheme finally.

In this paper, a novel modeling and parameter learning method for the Hammerstein–Wiener model with disturbance is proposed, and the Hammerstein–Wiener model is implemented to approximate complex nonlinear industrial processes. The proposed Hammerstein–Wiener model has two static nonlinear blocks represented by two independent neuro-fuzzy models that surround a dynamic linear block described by the finite impulse response model. The parameter learning method of the Hammerstein–Wiener model with disturbance can be summarized in the following three steps: First, the designed input signals are implemented to completely separate the parameter learning problem of output nonlinear block, linear block, and input nonlinear block. Meanwhile, the static output nonlinear block parameters can be learned based on input and output data of two sets of separable signals with different sizes. Second is to determine the dynamic linear block parameter using correlation analysis algorithm using one set of separable signal; thus, the process disturbance can be compensated by the calculation of correlation function. The final one is to achieve unbiased estimation of the static input nonlinear block parameters using least squares method according to the input–output data of random signal. Furthermore, with the parameter learning method, the proposed model can achieve less computation complexity and good robustness. The simulation results of two cases are provided to demonstrate the advantage of the proposed modeling and parameter learning method.

In wastewater treatment processes, the concentration of dissolved oxygen affects the performance of wastewater treatment directly. It is one of the key factors that determines effluent quality of the wastewater treatment. However, a simple closed-loop control has a high-energy consumption, and it cannot guarantee the effluent quality due to large perturbations in wastewater treatment plants, such as the influent rate, the temperature, and the complex biochemical reactions. In this paper, a new disturbance rejection controller is designed to address those perturbations. Dynamics of dissolved oxygen is transformed into a controllable canonical form. Discrepancy between the dissolved oxygen dynamics and the controllable canonical form is estimated by a disturbance observer and compensated by a control law. Stability and the bound of tracking errors are obtained. Finally, numerical results on the benchmark simulation model number 1 are presented to confirm the proposed method.

The rigid graph needs to be decomposed to solve the multi-equilibrium problem of the multi-agent formation control based on navigation function method. In this paper, a theorem and a scalable algorithm based on the Henneberg sequence of graphs are proposed for the decomposition of minimally rigid graph. The theorem demonstrates that if graph G( V, E) is a minimally rigid graph, then it can be decomposed as G = Gt[Formula: see text] Gc, where G t is a spanning tree of G and G c contains the remaining edges and their vertices. Moreover, the scalable algorithm is given to construct G t = ( V t, E t) and G c = ( V c, E c), and assign the edges in E c to n -- 2 distinct vertices in a scalable way via communication. Furthermore, a lemma is given to show when the number of vertices is less than eight, any edge can be chosen as the initialized edge, and the scalable algorithm mentioned above is always feasible. Finally, the effectiveness of the scalable algorithm is verified by numerical simulation.

This research presents a simple and novel improved ant colony optimization for path planning of unmanned wheeled robot. Our main concern is to avoid the random deadlock situation and to reach at the destination using the shortest path, to decrease lost ants and improve the efficiency of solutions. The aforementioned reasons, we design an adaptive heuristic function by adopting the Euclidean distance between the ant and the target destination, in order to avoid the initial blindness and later singleness of ant path searching. The historical best path when appropriate to retain the previous effort would supersede the current worst path. Simulation results under random maps show that the improved ant colony optimization considerably increases the number of effective ants. During the searching process, the probability to find the optimal path increases, as well as the search speed. Moreover, we also compare the improved ant colony optimization performance with the simple ant colony optimization.