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This paper aims to develop a robot for tightening charged bolt to solve the shortcomings of high labor intensity, low efficiency, high risk and poor reliability in artificially tightening drainage board bolt of strain clamp for high voltage transmission line. Realizing bolt-nut capture and location by manipulator is a critical process to complete the whole working task. To solve such key technology, an autonomous location control method for N-joint robot manipulator based on kinematics was proposed.

Through D-H kinematics analysis under flexible working environment of transmission line, the autonomous location control of double manipulators can be abstracted as a nonlinear approximation problem based on joint inverse kinematics. In addition, regarding the complex coupling relationship among different joint angles and the complex decoupling process which leads to the non-uniqueness of inverse solution, an improved backpropagation (BP) network was proposed based on the combination of dynamic adaptive adjustment of learning rate and variable momentum factor, so that the inverse kinematics of manipulator can be solved and the optimization evaluation mechanism of inverse solution can be presented. The proposed autonomous location control method is of adaptability to flexible environment and structural parameters of different drainage boards. The simulation results verified the effectiveness of the proposed method. Compared with the other location control, this method can achieve faster location speed, higher precision and lower hardware cost. Finally, the field operation test further validated that such autonomous location control method was of strong engineering practicability.

The proposed autonomous location control method is adaptable to a flexible environment and to the structural parameters of different types of drainage board. Simulation results confirm the effectiveness of the proposed method, which, in comparison with other approaches to location control, can achieve faster location, higher precision and lower hardware cost. Finally, a field test further confirms the engineering practicability of the proposed autonomous location control method.

The proposed method can achieve faster location speed, higher precision which meet the requirement of real-time control relative to the standard BP algorithm. Moreover, it is of strong adaptability to flexible environment and structural parameters for different drainage board. Field operation experiment further validated the engineering practicability of the method.

To realize classification of different pavements, a road roughness acquisition system design and an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation algorithm for road roughness detection is presented in this paper. The developed measurement system, including hardware designs and algorithm for software, constitutes an independent system which is low-cost, convenient for installation and small.

The inputs of restricted Boltzmann machine deep neural network are the vehicle vertical acceleration power spectrum and the pitch acceleration power spectrum, which is calculated using ADAMS finite element software. Adaboost Backward Propagation algorithm is used in each restricted Boltzmann machine deep neural network classification model for fine-tuning given its performance of global searching. The algorithm is first applied to road spectrum detection and experiments indicate that the algorithm is suitable for detecting pavement roughness.

The detection rate of RBM deep neural network algorithm based on Adaboost Backward Propagation is up to 96 per cent, and the false positive rate is below 3.34 per cent. These indices are both better than the other supervised algorithms, which also performs better in extracting the intrinsic characteristics of data, and therefore improves the classification accuracy and classification quality. Additionally, the classification performance is optimized. The experimental results show that the algorithm can improve performance of restricted Boltzmann machine deep neural networks. The system can be used for detecting pavement roughness.

This paper presents an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation for identifying the road roughness. Through the restricted Boltzmann machine, it completes pre-training and initializing sample weights. The entire neural network is fine-tuned through the Adaboost Backward Propagation algorithm, verifying the validity of the algorithm on the MNIST data set. A quarter vehicle model is used as the foundation, and the vertical acceleration spectrum of the vehicle center of mass and pitch acceleration spectrum were obtained by simulation in ADAMS as the input samples. The experimental results show that the improved algorithm has better optimization ability, improves the detection rate and can detect the road roughness more effectively.

This study aims to the three major problems of low cleaning efficiency, high labor intensity and difficult to evaluate the cleaning effect for manual insulators cleaning in ultra high voltage (UHV) converter station, the purpose of this paper is to propose a basic configuration of UHV vertical insulator cleaning robot with multi-freedom-degree mechanical arm system on mobile airborne platform and its innovation cleaning operation motion planning.

The main factors affecting the insulators cleaning effect in the operation process have been analyzed. Because of the complex coupling relationship between the influencing factors and the insulators cleaning effect, it is difficult to establish its analytical mathematical model. Combining the non-linear mapping and approximation characteristics of back propagation (BP) neural network, the insulator cleaning effect evaluation can be abstracted as a non-linear approximation process from actual cleaning effect to ideal cleaning effect. An evaluation method of robot insulator cleaning effect based on BP neural network has been proposed.

Through the BP neural network training, the robot cleaning control parameters can be obtained and used in the robot online operation control, so that the better cleaning effect can be also obtained. Finally, a physical prototype of UHV vertical insulator cleaning robot has been developed, and the effectiveness and engineering practicability of the proposed robot configuration, cleaning effect evaluation method are all verified by simulation experiments and field operation experiments. At the same time, this method has the remarkable characteristics of sound versatility, strong adaptability, easy expansion and popularization.

An UHV vertical insulator cleaning robot operation system platform with multi-arm system on airborne platform has been proposed. Through the coordinated movement of the manipulator each joint, the manipulator can be positioned to the insulator strings, and the insulator can be cleaned by two pairs high-pressure nozzles located at the double manipulator. The influence factors of robot insulator cleaning effect have been analyzed. The BP neural network model of insulator cleaning effect evaluation has been established. The evaluation method of robot insulator cleaning effect based on BP neural network has also been proposed, and the corresponding evaluation result can be obtained through the network training. Through the system integration design, the robot physical prototype has been developed. For the evaluation of other operation effects of power system, the validity and engineering practicability of the robot mechanism, motion planning and the method for evaluating the effect of robot insulator cleaning have been verified by simulation and field operation experiments.

This paper aims to predict the 28-day compressive strength of recycled thermal insulation concrete more accurately.

The initial weights and thresholds of BP neural network are improved by genetic algorithm on MATLAB 2014 a platform.

Genetic algorithm–back propagation (GA-BP) neural network is more stable. The generalization performance of the complex is better.

The GA-BP neural network based on the training sample data can better realize the strength prediction of recycled aggregate thermal insulation concrete and reduce the complex orthogonal experimental process. GA-BP neural network is more stable. The generalization performance of the complex is better.

To reduce the influence of temperature on MEMS gyroscope, this paper aims to propose a temperature drift compensation method based on variational modal decomposition (VMD), time-frequency peak filter (TFPF), mind evolutionary algorithm (MEA) and BP neural network.

First, VMD decomposes gyro’s temperature drift sequence to obtain multiple intrinsic mode functions (IMF) with different center frequencies and then Sample entropy calculates, according to the complexity of the signals, they are divided into three categories, namely, noise signals, mixed signals and temperature drift signals. Then, TFPF denoises the mixed-signal, the noise signal is directly removed and the denoised sub-sequence is reconstructed, which is used as training data to train the MEA optimized BP to obtain a temperature drift compensation model. Finally, the gyro’s temperature characteristic sequence is processed by the trained model.

The experimental result proved the superiority of this method, the bias stability value of the compensation signal is 1.279 × 10^–3°/h and the angular velocity random walk value is 2.132 × 10^–5°/h/vHz, which is improved compared to the 3.361°/h and 1.673 × 10^–2°/h/vHz of the original output signal of the gyro.

This study proposes a multi-dimensional processing method, which treats different noises separately, effectively protects the low-frequency characteristics and provides a high-precision training set for drift modeling. TFPF can be optimized by SEVMD parallel processing in reducing noise and retaining static characteristics, MEA algorithm can search for better threshold and connection weight of BP network and improve the model’s compensation effect.

The purpose of this paper is to improve back propagation neural network (BPNN) modeling in order to promote the forecast calculation precision of landslide deformation.

The genetic algorithm is adopted to optimize the architectural parameter of BPNN so as to avoided errors occurrence while using the trial‐and‐error method. Furthermore, the Sigmoid function is improved and revised to expand the output range of change‐over function from unipolar (only positive) to ambipolar (may be positive or negative), then the convergence time is reduced and the neural network can express more artificial intelligence.

The modeling can effectively reduce the probability to get into the local minima while employing neural networks to forecast the landslide deformation. It significantly promotes the forecast precision.

The improved BPNN modeling, which is very good in learning and processing information, can work out the complex non‐linear relation by learning model and using the present data or reciprocity of surroundings.

The revised BPNN modeling in this paper can be used to predict and calculate landslide deformation.

The paper demonstrates that the modeling can meet the demand of calculation precision.

In the extreme power environment of flexible transmission line, wind load, high voltage and strong electromagnetic interference, the motion performance of the robot manipulator is strongly affected by the extreme environment. Therefore, this study aims to improve the manipulator motion control performance of power cable maintenance robot and effectively reduce the influence of specific operation environment on the robot manipulator motion posture.

The mathematical model under three typical operation conditions, namely, flexible line, wind load and strong electromagnetic field have been established, correspondingly the mapping relationship between different environment parameters and robot operation conditions are also given. Based on the nonlinear approximation feature of neural network, a back propagation (BP) neural network is adopted to solve the posture control problems. The power cable line sag, robot tile angle caused by wind load and spatial field strength are the input signals of the BP network in the robot motion posture control method.

Through the training and learning of the BP network, the output control variables are used to compensate the actual robot operation posture. The simulation experiment verifies the effectiveness of the proposed algorithm, and compared with the conventional proportional integral differential (PID) control, the method has high real-time performance and sound stability. Finally, field operation experiments further validate the engineering feasibility of the control method, and at the same time, the proposed control method has the remarkable characteristics of sound universality, adaptability and easy expansion.

A multi-layer control architecture which is suitable for smart grid platform maintenance is proposed and a robot system platform for network operation and maintenance management is constructed. The human–machine–environment coordination and integration mode and intelligent power system management platform can be realized which greatly improves the intelligence of power system management. Mathematical models of the robot under three typical operation conditions of flexible wire wind load and strong electromagnetic field are established and the mapping relationship between different environmental parameters and the robot operation conditions is given. Through the non-linear approximation characteristics of BP network, the control variables of the robot joints can be obtained and the influence of extreme environment on the robot posture can be compensated. The simulation results of MATLAB show that the control algorithm can effectively restrain the influence of uncertain factors such as flexible environment, wind load and strong electromagnetic field on the robot posture. It satisfied the design requirements of fast response, high tracking accuracy and good stability of the control system. Field operation tests further verify the engineering practicability of the algorithm.

The purpose of this paper is to analyze the measurement error of a three-dimensional coordinate measurement system based on dual-position-sensitive detector (PSD) under different background light.

The mind evolutionary algorithm (MEA)-back propagation (BP) neural network is used to predict the three-dimensional coordinates of the points, and the influence of the background light on the measurement accuracy of the three-dimensional coordinates based on PSD is obtained.

The influence of the background light on the measurement accuracy of the system is quantitatively calculated. The background light has a significant influence on the prediction accuracy of the three-dimensional coordinate measurement system. The optical method, electrical method and photoelectric compensation method are proposed to improve the measurement accuracy.

BP neural network based on MEA is applied to the coordinate prediction of the three-dimensional coordinate measurement system based on dual-PSD, and the influence of background light on the measurement accuracy is quantitatively analyzed.

Prefabricated construction is often hindered by scheduling delays. This paper aims to propose a schedule delay prediction model system, which can provide the key information for controlling the delay effects of risk-related factors on scheduling in prefabricated construction.

This paper combines SD (System Dynamics) and BP (Back Propagation) neural network to predict risk related delays. The SD-based prediction model focuses on dynamically presenting the interrelated impacts of risk events and activities along with workflow. While BP neural network model is proposed to evaluate the delay effect for a single risk event disrupting a single job, which is the necessary input parameter of SD-based model.

The established model system is validated through a structural test, an extreme condition test, a sensitivity test, and an error test, and shows an excellent performance on aspect of reliability and accuracy. Furthermore, 5 scenarios of case application during 3 different projects located in separate cities prove the prediction model system can be applied in a wide range.

This paper contributes to academic research on combination of SD and BP neural network at the operational level prediction, and a practical prediction tool supporting managers to take decision-making in a timely manner against delays.

This paper aims to develop a signal acquisition system of surface electromyography (sEMG) and use the characteristics of (sEMG) signal to interference action pattern.

This paper proposes a fusion method based on combining the coefficient of AR model and wavelet coefficient. It improves the recognition rate of the target action. To overcome the slow convergence speed and local optimum in standard BP network, the study presents a BP algorithm which combine with LM algorithm and PSO algorithm, and it improves the convergence speed and the recognition rate of the target action.

Experiments verify the effectiveness of the system from two aspects the target motion recognition rate and the corresponding reaction speed of the robotic system.

The study developed a signal acquisition system of sEMG and used the characteristics of (sEMG) signal to interference action pattern. The myoelectricity integral values are presented to determine the starting point and end point of target movement, which is more effective than using single sample point amplitude method.