A distributed model predictive control approach to cooperative adaptive cruise control of the heterogeneous platoon

This paper presents a distributed model predictive control algorithm to solve the cruise control problem of a heterogeneous platoon. Each following vehicle in the platoon can use the communication equipment to receive the information of the leading vehicle and its preceding adjacent one. The vehicles in the platoon are dynamically decoupled and have different dynamic parameters. Each vehicle solves a local optimal control problem independently. The cost function of each vehicle’s local optimal control algorithm is designed with traceability as the control objective, and its asymptotic stability is guaranteed by using the terminal constraint method. In addition, the timestamps of all vehicles in the platoon are synchronous, which means that in each sampling period, a specific vehicle in the platoon cannot obtain the solution results of other vehicles’ local optimal control problems at the current sampling moment. Under this restriction, the constraints that each vehicle needs to meet to realize the platoon’s string stability are also designed. Finally, the simulation results show the effectiveness of the algorithm.

Distributed estimation and control of a four-tank process

A multivariable process of four interconnected water tanks is considered for modeling and control. The objective of the current study is to design and implement a distributed control and estimation (DEC) for a multivariable four-tank process. Distributed model and inter-nodal communication structure are derived from global state–space matrices, thus combining the topology of plant flow sheet and the interaction dynamics across the plant subunits. Using experimental data, the process dynamics and disturbance effects are modeled. A typical lab-scale system was simulated and the obtained results demonstrated the potential of the DEC algorithm.

Integrated Communication and Control Design for Fuel-Efficient Vehicle Platooning

As a promising application for autonomous driving, vehicle platooning aims at increasing traffic throughput, improving road safety, and reducing air pollution and fuel consumption. However, frequent traffic perturbations will bring more fuel consumption because vehicles driving in a platoon require more control to ensure safe driving, especially in high-density scenes. In this paper, considering the traffic perturbations and high-density scenes, we integrate communication and control systems to reduce the fuel consumption of a platoon. By obtaining the velocities of multiple vehicles ahead through a long-term evolution-vehicle (LTE-V) network, we propose a modified distributed model predictive control (DMPC) method to smooth traffic perturbations and handle the constraints of vehicle state and control. In addition, considering a limited number of uplink channels that can be reused in the platoon and the uncertainty of wireless channels, a radio resource allocation optimization problem in the LTE-V network is modeled. This problem is solved in two steps including maximum vehicle-to-vehicle (V2V) broadcast distance and minimum weight matching. This resource allocation scheme increases the platoon-based V2V broadcast distance while ensuring the ergodic capacity requirement of the cellular user (CUE) uplink communication and the reliability of platoon-based V2V communication. Simulation results show that the proposed method improves fuel efficiency compared to the existing schemes.

Towards virtual modeling environments for functional structural plant models based on Jupyter notebooks: Application to the modeling of mango tree growth and development

Functional-Structural Plant Models (FSPMs) are powerful tools to explore the complex interplays between plant growth, underlying physiological processes and the environment. Various modeling platforms dedicated to FSPMs have been developed with limited support for collaborative and distributed model design, reproducibility and dissemination. With the objective to alleviate these problems, we used the Jupyter project, an open-source computational notebook ecosystem, to create virtual modeling environments for plant models. These environments combined Python scientific modules, L-systems formalism, multidimensional arrays and 3D plant architecture visualization in Jupyter notebooks. As a case study, we present an application of such an environment by reimplementing V-Mango, a model of mango tree development and fruit production built on interrelated processes of architectural development and fruit growth that are affected by temporal, structural and environmental factors. This new implementation increased model modularity, with modules representing single processes and the workflows between them. The model modularity allowed us to run simulations for a subset of processes only, on simulated or empirical architectures. The exploration of carbohydrate source-sink relationships on a measured mango branch architecture illustrates this possibility. We also proposed solutions for visualization, distant distributed computation and parallel simulations of several independent mango trees during a growing season. The development of models on locations far from computational resources makes collaborative and distributed model design and implementation possible, and demonstrates the usefulness and efficiency of a customizable virtual modeling environment.

Flood Discharge Prediction Based on Remote-Sensed Spatiotemporal Features Fusion and Graph Attention

Floods have brought a great threat to the life and property of human beings. Under the premise of strengthening flood control engineering measures and following the strategic thinking of sustainable development, many achievements have been made in flood forecasting recently. However, due to the complexity of the traditional lumped model and distributed model, the hydrologic parameter calibration process is full of difficulties, leading to a long development cycle of a reasonable hydrologic prediction model. Even for modern data-driven models, the spatial distribution characteristics of the rainfall data are also not fully mined. Based on this situation, this paper abstracts the rainfall data into the graph structure data, uses remote sensing images to extract the elevation information, introduces the graph attention mechanism to extract the spatial characteristics of rainfall, and employs long-term and short-term memory (LSTM) network to fuse the spatial and temporal characteristics for flood prediction. Through well-designed experiments, the forecasting effect of flood peak value and flood arrival time is verified. Furthermore, compared with the LSTM model and BIGRU model without spatial feature extraction, the advantages of spatiotemporal feature fusion are highlighted. The specific performance is that the RMSE (the root means square error) and R2(coefficient of determination) of the GA-RNN model have been significantly improved. Finally, we conduct experiments on the observed ten rainfall events in the history of the target watershed. According to the hydrological prediction specifications, the model can be evaluated as a Class B flood forecasting model.

A bio-inspired adaptive model for search and selection in the Internet of Things environment

The Internet of Things (IoT) is a paradigm that can connect an enormous number of intelligent objects, share large amounts of data, and produce new services. However, it is a challenge to select the proper sensors for a given request due to the number of devices in use, the available resources, the restrictions on resource utilization, the nature of IoT networks, and the number of similar services. Previous studies have suggested how to best address this challenge, but suffer from low accuracy and high execution times. We propose a new distributed model to efficiently deal with heterogeneous sensors and select accurate ones in a dynamic IoT environment. The model’s server uses and manages multiple gateways to respond to the request requirements. First, sensors were grouped into three semantic categories and several semantic sensor network types in order to define the space of interest. Second, each type’s sensors were clustered using the Whale-based Sensor Clustering (WhaleCLUST) algorithm according to the context properties. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was improved to search and select the most adequate sensor matching users’ requirements. Experimental results from real data sets demonstrate that our proposal outperforms state-of-the-art approaches in terms of accuracy (96%), execution time, quality of clustering, and scalability of clustering.