Industrial processes are often nonlinear and multivariate and suffer from non-Gaussian noise and outliers in the process data, which cause significant challenges in data-driven modelling. To address these issues, a robust soft-sensing algorithm that integrates Huber’s M-estimation and adaptive regularisations with multilayer perceptron (MLP) is proposed in this paper. The proposed algorithm, called RAdLASSO-MLP, starts with an initially well-trained MLP for nonlinear data-driven modelling. Subsequently, the residuals of the proposed model are robustified with Huber’s M-estimation to improve the resistance to non-Gaussian noise and outliers. Moreover, a double L1-regularisation mechanism is introduced to minimise redundancies in the input and hidden layers of MLP. In addition, the maximal information coefficient (MIC) index is investigated and used to design the adaptive operator for the L1-regularisation of the input neurons to improve biased estimations with L1-regularisation. Including shrinkage parameters and Huber’s M-estimation parameter, the hyperparameters are determined via grid search and cross-validation. To evaluate the proposed algorithm, simulations were conducted with both an artificial dataset and an industrial dataset from a practical gasoline treatment process. The results indicate that the proposed algorithm is superior in terms of predictive accuracy and robustness to the classic MLP and the regularised soft-sensing approaches LASSO-MLP and dLASSO-MLP.

Globally, wind power plays a leading role in the renewable energy industry. In order to ensure the normal operation of a wind farm, the staff will regularly check the equipment of the wind farm. However, manual inspection has some disadvantages, such as heavy workload, low efficiency and easy misjudgment. In order to realize automation, intelligence and high efficiency of inspection work, inspection robots are introduced into wind farms to replace manual inspections. Path planning is the prerequisite for an intelligent inspection robot to complete inspection tasks. In order to ensure that the robot can take the shortest path in the inspection process and avoid the detected obstacles at the same time, a new path-planning algorithm is proposed. The path-planning algorithm is based on the chaotic neural network and genetic algorithm. First, the chaotic neural network is used for the first step of path planning. The planning results are encoded into chromosomes to replace the individuals with the worst fitness in the genetic algorithm population. Then, according to the principle of survival of the fittest, the population is selected, hybridized, varied and guided to cyclic evolution to obtain the new path. The shortest path obtained by the algorithm can be used for the robot inspection of the wind farms in remote areas. The results show that the proposed new algorithm can generate a shorter inspection path than other algorithms.

The development of powered electronic technology has made many aware of the design and control of ship power systems (SPSs), and has made medium voltage DC (MVDC) architecture the main research direction in the future. The negative impedance characteristic of constant power load (CPL) generated by the coupling of powered electronic converters will seriously affect the stability of the systems if these converters are not properly controlled. The conventional linear control method can only guarantee the small-signal stability of the system near its equilibrium point. When the operating point changes in a large range, linear control methods will be ineffective. More importantly, research for the large-signal stability of the multi-converter system with CPLs is still rarely involved. In this paper, a sliding-mode-based duty ratio controller (SMDC) is proposed for voltage regulation and current sharing of the multiple parallelly-connected DC–DC converters system loaded by CPLs. By controlling the output voltage of each converter with SMDC, large-signal stability of the coupled bus voltage is ensured. Meanwhile, proportional current sharing between the parallel converters is achieved by droop control integrated in the reference value of converter voltage. Simulation studies were conducted in MATLAB/Simulink, where two typical operating conditions, including the variation of load power and bus voltage, were designed to verify the effectiveness of the proposed method. Moreover, a traditional PID controller was used as a comparison to reflect the superiority of the former. Simulation results showed that the proposed method is able to guarantee large-signal stability of the system in the presence of large-scale variations in load power and bus voltage. The output current of the parallel converters can also be distributed in desired proportions according to the droop coefficient.

In this paper, a new sub-pixel intensive particle image identification method used for particle tracking velocimetry (PTV), is discussed. With aiming to improve the performance of accuracy and robustness, a two-stage deep learning framework consisting of two independent convolutional neural networks (CNN) is proposed in this method. The first neural network is to segment the particle blobs from the image, and the second one is to locate the position of the segmented particles at the sub-pixel level. A synthetic dataset containing particle images and the ground-truth positions is generated for network training. The effect of different characteristic parameters (e.g., the particle conditions; the tuning of the loss function; the noise level) is evaluated on synthetic images. To further verify the generalization capabilities of the technique, we also apply the proposed network to real-world images. Both synthetic and real-world experiment results strongly demonstrate that the proposed method possesses better accuracy and robustness than other conventional methods.