This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators (MOMMs). The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning (CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming (QP) and solved online utilizing a noise-tolerant zeroing neural network (NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demon-strated by numerical simulations and physical platform experiments.

With the increase in population and the progress of industrialization, the rational use of energy in heating systems has become a research topic for many scholars. The accurate prediction of heat load in heating systems provides us with a scientific solution. Due to the complexity and difficulty of heat load forecasting in heating systems, this paper proposes a short-term heat load forecasting method based on a Bayesian algorithm-optimized long- and short-term memory network (BO-LSTM). The moving average data smoothing method is used to eliminate noise from the data. Pearson’s correlation analysis is used to determine the inputs to the model. Finally, the outdoor temperature and heat load of the previous period are selected as inputs to the model. The root mean square error (RMSE) is used as the main evaluation index, and the mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R²) are used as auxiliary evaluation indexes. It was found that the RMSE of the asynchronous length model decreased, proving the general practicability of the method. In conclusion, the proposed prediction method is simple and universal.

To enable the timely adjustment of the control strategy of automobile active safety systems, enhance their capacity to adapt to complex working conditions, and improve driving safety, this paper introduces a new method for predicting road surface state information and recognizing road adhesion coefficients using an enhanced version of the MobileNet V3 model. On one hand, the Squeeze-and-Excitation (SE) is replaced by the Convolutional Block Attention Module (CBAM). It can enhance the extraction of features effectively by considering both spatial and channel dimensions. On the other hand, the cross-entropy loss function is replaced by the Bias Loss function. It can reduce the random prediction problem occurring in the optimization process to improve identification accuracy. Finally, the proposed method is evaluated in an experiment with a four-wheel-drive ROS robot platform. Results indicate that a classification precision of 95.53% is achieved, which is higher than existing road adhesion coefficient identification methods.

Water is a critical resource globally, covering approximately 71% of the Earth’s surface. Employing analytical models to forecast water quality parameters based on historical data is a key strategy in the field of water quality monitoring and treatment. By using a forecasting model, potential changes in water quality can be understood over time. In this study, the gated recurrent unit (GRU) neural network was utilized to forecast dissolved oxygen levels following variational mode decomposition (VMD). The GRU neural network’s parameters were optimized using the grey wolf optimizer (GWO), leading to the development of a VMD–GWO–GRU model for forecasting water quality parameters. The results indicate that this model outperforms both the standalone GRU model and the GWO–GRU model in capturing key information related to water quality parameters. Additionally, it shows improved accuracy in forecasting medium to long-term water quality changes, resulting in reduced root mean square error (RMSE) and mean absolute percentage error (MAPE). The model demonstrates a significant improvement in the lag of forecasting water quality parameters, ultimately boosting forecasting accuracy. This approach can be applied effectively in both monitoring and forecasting water quality parameters, serving as a solid foundation for future water quality treatment strategies.

This paper proposes a fractional order optimization method of a fractional order multi-variable Grey model (GM( $r$ ,2)) based on a full transfer learning LSTM network. Firstly, a GM( $r$ ,2) was built with adhesion coefficient as input variable, namely, correlation factor sequence, and driving intention as output variable, namely, system behaviour characteristic sequence. Secondly, a long short-term memory (LSTM) network was trained by means of driving intention dataset, which is defined as source domain dataset. Adhesion coefficient, which is selected as test dataset, is defined as target domain dataset. By utilizing relative degree of grey incidence, it is proved that source domain dataset is extremely similar with target domain dataset. The LSTM network trained by driving intention dataset can be transferred fully to calculate optimization data. Finally, with the optimization data predicted by the LSTM network, an optimized fractional order of GM( $r$ ,2) can be calculated by fractional-order accumulation matrix calculation and least square fitting. Comparing with particle swarm optimization (PSO), the proposed optimization method can effectively improve the convergence of fractional order optimization values. The optimized GM( $r_{opt}$ ,2) was applied in inferring diving intention for an active safety driving system of electric vehicles. Simulation experiments are performed in car-following and lane-changing processes, respectively. The driving intention inferred by GM( $r_{opt}$ ,2) can change not only longitudinal safety distance, but also lane-changing trajectory with different road conditions. It can ensure vehicle driving safety. Experimental data can demonstrate that the proposed optimization method of fractional order is effective, and the optimized GM( $r_{opt}$ ,2) is appropriate to infer driving intention.