Collaborative Control Research Articles

Collaborative Control of UAV Swarms for Target Capture Based on Intelligent Control Theory

Real-time dynamic capture of a single moving target is one of the most crucial and representative tasks in UAV capture problems. This paper proposes a multi-UAV real-time dynamic capture strategy based on a differential game model to address this challenge. In this paper, the dynamic capture problem is divided into two parts: pursuit and capture. First, in the pursuit–evasion problem based on differential games, the capture UAVs and the target UAV are treated as adversarial parties engaged in a game. The current pursuit–evasion state is modeled and analyzed according to varying environmental information, allowing the capture UAVs to quickly track the target UAV. The Nash equilibrium solution in the differential game is optimal for both parties in the pursuit–evasion process. Then, a collaborative multi-UAV closed circular pipeline control method is proposed to ensure an even distribution of capture UAVs around the target, preventing excessive clustering and thereby significantly improving capture efficiency. Finally, simulations and real-flight experiments are conducted on the RflySim platform in typical scenarios to analyze the computational process and verify the effectiveness of the proposed method. Results indicate that this approach effectively provides a solution for multi-UAV dynamic capture and achieves desirable capture outcomes.

Collaborative Control and Intelligent Optimization of a Lead–Bismuth Cooled Reactor Based on a Modified PSO Method

Accelerator-driven subcritical (ADS) reactors with lead–bismuth eutectic (LBE) coolants are some of the Gen-IV nuclear energy systems that can generate clean electricity and potentially transmute spent fuel. The dynamic characteristics and control strategy of an ADS reactor are substantially different from those of traditional nuclear reactors. In this paper, a new collaborative control strategy is proposed using an accelerator beam and a control rod, and the control system’s parameters are optimized using a modified particle swarm optimization (PSO) method. To test the control performance, a simulation platform is developed with a nonlinear reactor dynamic model, a power compensation control system and a coolant temperature control system. Four typical control transients are used, including a ±10% full-power (FP) step change load and a ±5% FP/min linear variable load. The simulation results show that the collaborative control strategy has a better load tracking capability and a higher power control accuracy than the beam single-control strategy and the rod single-control strategy. The results also show that the performance of the collaborative control system in terms of the reactor’s power and coolant temperature is significantly improved based on the modified PSO parameter optimization.

Path planning and optimization for transmission line barrier operations in complex terrain based on multimachine collaborative control

Abstract To address the challenges of path planning for transmission line barrier operations in complex terrains, this paper proposes a solution based on multimachine collaborative control. A path-planning algorithm that integrates multiple classical algorithms and incorporates multiobjective optimization methods is constructed to handle the complexities of challenging terrains. Auction algorithm and genetic algorithm are adopted to achieve optimal load balance by dynamically adjusting task allocation. Simulation results show that the proposed method effectively enhances task execution efficiency, optimizes path planning, and reduces energy consumption in complex terrains and dynamic environments, while maintaining high-obstacle avoidance success rates and communication stability.

A novel framework for online decision-making and feedback optimization of complex products process parameter based on edge-cloud collaboration

Background Intelligent manufacturing is perceived as a manufacturing mode with powerful learning and cognitive capabilities empowered by information technologies such as the internet of things, edge computing, and cloud computing. The mode can be used to address the problems of low intelligence and poor timeliness of traditional process planning. Methods The framework includes the multi-objective process planning method based on real-time data, and the process closed-loop optimization mechanism of “cloud-based theoretical process planning plus edge MEC side online simulation verification and real-time feedback adjustment”, which realizes online process planning and iterative optimization in mass customization. Results The feasibility of the online analysis method for thin-walled part milling deformation is verified by taking the finishing process of aerospace thin-walled parts as an example. The experimental results show that the simulation time on the single analysis step is reduced from 6s to 1s, and the accuracy rate is 86.9%. Conclusions A new intelligent process planning theoretical framework integrating with online process planning and autonomous collaborative control, namely, digital twin and multi-access edge computing process planning (DT-MEC-PP) is proposed in this paper.

Operational strategy, capabilities, and successfully accomplishing business strategy

This study investigates the vital role of corporations’ operational capabilities, which involve resources, competencies, and processes, in attaining the business’s strategic goals for profitability and sustainability. The methodology employs a selective literature review to analyze case studies, books, and articles that give an overview of operational management and its importance in achieving corporate goals. The results exhibit the challenges posed by rapid market changes, revealing the deficiencies of traditional operational systems focused solely on efficiency and cost. To attain sustained success, manufacturers must focus on a client-valued approach, align objectives with capabilities, and support inter-functional links. Companies with distinctive managerial processes outperform in profitability, emphasizing the significance of investing in management capabilities. Operational excellence necessitates a focus on efficiency, innovation, and customer experience, prompting a shift towards a learning style that encourages innovation. Aligning innovation efforts with business strategy is essential for sustainable competitive advantage. Technological innovation, particularly AI adoption, plays a transformative role in operational innovation, highlighting the importance of dynamic capabilities in managing technological change. Yet, effective inventory and supply chain management are important for meeting customer demand and maximizing efficiency through collaboration and adaptable control.

Collaborative control technology and application of support and modification for soft-rock roadway in a kilometer-deep coal mine

The soft-rock roadways in kilometer-deep coal mines are often damaged by large deformation and have to be periodically expanded and repaired, which seriously restricts the safe and efficient production of coal mines. A typical soft-rock roadway in a kilometer-deep coal mine is selected as the engineering, and the main reasons for roadway deformation are analyzed, and the ground stress and mechanical characteristics are obtained. The Flac3D numerical model, which can accurately reflect the deformation characteristics of surrounding rock in kilometer-deep soft-rock roadway, has been constructed, and the evolution laws of stress field and its damage mechanism have been analyzed with the vertical stress, vertical displacement and plastic zone. The damage range of roadway surrounding rock is elucidated by endoscopic observation, and the synergistic control technology of support and modification is proposed. The on-site engineering application is carried out in Qianyingzi Coal Mine, and the results show that the collaborative control technology of support and modification ensures the safety and stability of deep soft-rock roadway. The results provide important and preliminary technical references for realizing safe and intelligent mining in coal mines.

Enhancing ride comfort of semi-active suspension through collaboration control using dung beetle optimizer optimized Fuzzy PID controller

The enhancement of automobile quality is a significant area of modern research and development, with a focus on improving ride comfort and driving experience. This is achieved not only through structural improvements but also through the optimization of suspension control strategies. To enhance the ability of online real-time adjustment and overcome the limitations of fuzzy control, this study combines dung beetle optimizer (DBO), Fuzzy control, and Proportional-Integral-Derivative (PID) control to propose a reduced optimization complexity control method to improve MR damper control. The control parameters generated by fuzzy control are utilized as the initial values for DBO iteration to obtain the optimal solution, which is subsequently fed into the PID controller to regulate changes in the actuator’s magnetic field. Use MATLAB/Simulink to build a quarter semi-active suspension model with magnetorheological (MR) dampers for simulation analysis. Under sinusoidal and random disturbances, compared with the passive suspension, the vehicle acceleration of the Fuzzy-DBO-PID controller is reduced by 51%, 50.82%, 47.56% and 11.42%. Compared with Fuzzy-PID, the vehicle acceleration of Fuzzy-DBO-PID is reduced by 25.98%, 32.86%, 29.41%, and 7.19% on sinusoidal and random disturbances, which improves the ride comfort and verifies the effectiveness of the proposed control strategy.

An integrated thermal management system for fuel cell vehicles based on collaborative control

An integrated thermal management system for fuel cell vehicles based on collaborative control

Human Collaborative Control of Lower-Limb Prosthesis Based on Game Theory and Fuzzy Approximation.

For leg prosthesis user, the soft tissue and skin under the stump of are not accustomed to weight bearing, excessive continuous contact pressure can lead to the risk of degenerative tissue ulceration. This article presents a novel human-robot collaborative control scheme that achieves control weight self-adjustment for robotic prostheses to minimize interaction torque. To establish the human-robot interaction relationship, we regard the contact pressure between human residual limb and the prosthetic receiving cavity as the interaction force. We aim at reducing the interaction force under the premise of minimally changing the original motion trajectory of the robotic prosthesis. The control scheme mainly includes trajectory optimization based on a dual-agent game control scheme under a cooperative relationship, and a fuzzy logic system for improving the control accuracy of trajectory tracking of robotic prostheses with unknown dynamic parameters. Experiments were carried out on two amputee participants to verify the proposed human-robot interactive control scheme in a robotic prosthesis. The results show that the interaction torque could be reduced while maintaining minimal trajectory tracking error. The proposed control scheme could potentially facilitate the dexterous manipulation of leg prostheses, thus benefiting amputees.

An Online Simulation-Based Collaborative Optimization Control Method for Solar thermal Energy, Heat Pumps and Building Operations

An Online Simulation-Based Collaborative Optimization Control Method for Solar thermal Energy, Heat Pumps and Building Operations

Improved quantum particle swarm-based collaborative optimisation and control method for flexible job shop scheduling and predictive maintenance

Improved quantum particle swarm-based collaborative optimisation and control method for flexible job shop scheduling and predictive maintenance

Failure mechanism analysis of ultra-large-section soft-rock roadway in kilometer deep coal mine and its collaborative control of “grouting-anchoring-pouring”

Failure mechanism analysis of ultra-large-section soft-rock roadway in kilometer deep coal mine and its collaborative control of “grouting-anchoring-pouring”

Summary of stability analysis and collaborative control technology research on multi PCS parallel connection of grid type energy storage power stations

Summary of stability analysis and collaborative control technology research on multi PCS parallel connection of grid type energy storage power stations

An Energy Storage System for Regulating the Maximum Demand of Traction Substations

With the development of electrified railways towards high speed and heavy load, the peak power of traction loads is increasing, and the maximum demand and negative sequence current of traction substations are also increasing. Therefore, this article proposes an energy storage system (ESS) based on Li-ion batteries for regulating the maximum demand of traction substations. An ESS is connected to the DC bus of a railway power conditioner (RPC), which is connected to the two power supply arms of the traction substation. In response to the large fluctuation of traction load, this paper proposes a maximum demand active regulation method based on short-term prediction of traction load. The short-term prediction of traction load adopts a time series short-term load prediction method based on BP neural network error correction. Then, based on the load prediction value of the traction substation and the state of charge of the ESS, a collaborative control strategy for ESS and RPC is formulated to enable RPC to achieve a negative sequence suppression function simultaneously. Finally, simulation experiments were conducted using MATLAB, and the results showed that compared with the traditional maximum demand regulation method based on peak power reference values, the method proposed in this paper significantly reduces the number of ESS charging and discharging cycles, improves the regulation effect of maximum demand, and has a higher net income during the lifecycle. At the same time, it also takes into account the negative sequence current suppression function, thereby improving the comprehensive economic benefits of railways and the quality of power grids.

The right-of-way assignment strategy under mixed traffic: considering drivers’ confidence in CAVs

With the development of communication and automated driving technology, connected and automated vehicles (CAVs) will coexist with human-driven vehicles (HDVs) for a long time. Can CAVs and HDVs share a lane or should there be a CAV-only lane? This paper considers drivers’ confidence in CAVs and establishes a car-following model for driving behaviour simulation. The setting condition for the CAV-only approach and the control measure without it are then proposed to optimize carbon emissions and traffic efficiency. The simulation results show that the best CAV permeability range and the corresponding impact of drivers’ confidence heterogeneity on this permeability range for setting the CAV-only approach are obtained. Moreover, the control measures that enable CAVs to gather at the beginning of the platoon help optimize the efficiency and emissions without the CAV-only approach. The results provide a reference for right-of-way assignment and collaborative control with different levels of CAV permeability and confidence.

Adaptive Control Strategy of Parallel Virtual Synchronizer of Wind–Solar–Storage Microgrid Based on Neural Network

In order to solve the problem that the impedance of each line of the parallel system of the wind–solar–storage virtual synchronous machine(VSG) is inconsistent, resulting in the power circulation between the parallel VSG, a multi-parameter collaborative adaptive control strategy for the parallel virtual synchronizers of a wind–solar–storage microgrid based on a neural network was proposed. Firstly, the topology of the virtual synchronous machine parallel system of the wind–solar–storage microgrid was built, and the VSG was analyzed. Then, the neural network algorithm was used to realize the adaptive adjustment of each parameter of VSG, which improves the uneven power distribution and the influence of circulation. Next, the parameters of multiple parallel VSG control systems were configured. Finally, MATLAB2021a/Simulink was used to model the system, and the VSG capacity under different scale conditions was simulated and analyzed. The simulation results show that when the capacity ratio of VSG1 and VSG2 is 1:1, the active power output is 9000 W, and the reactive power output is 7500 Var, which realizes accurate distribution, and when the capacity ratio of VSG1 and VSG2 is 2:1, the output values of active power and reactive power are 12,000 W/6000 W and 10,000 Var/5000 Var, and the output is carried out according to the ratio of 2:1, which shows that the control strategy can effectively improve the power allocation accuracy, suppressing circulation.

Collaborative Control Strategy of Electric–Thermal–Hydrogen-Integrated Energy System Based on Variable-Frequency Division Coefficient

To address the issues of diverse energy supply demands and power fluctuations in integrated energy systems (IESs), this study takes an IES composed of power-generation units, such as wind and photovoltaic units, along with various energy-storage systems, including electrical, thermal, and hydrogen storage, as the research subject. A collaborative control strategy is proposed for the IES, which comprehensively considers the status of diverse energy-storage systems like battery packs, thermal tanks, and hydrogen tanks. First, a mathematical model of the IES is constructed. Then, a dual-layer collaborative control strategy is designed, considering different operating modes of the IES, which includes a multi-energy-storage power allocation control layer based on second-order power-frequency processing and distribution and an adaptive adjustment layer for adjusting power-frequency coefficients based on adaptive fuzzy control. Finally, MATLAB is used to simulate and validate the proposed strategy. The results indicate that the collaborative control strategy based on variable-frequency coefficients optimizes the allocation of fluctuating power among multiple energy-storage systems, enhances the stability of bus voltage, reduces the deep charge and discharge time of battery packs, and extends the service life of battery packs.

Research on the Primary Frequency-Regulation Strategy of Wind-Storage Collaborative Participation Systems Considering the State of Charge of Energy Storage

The system inertia insufficiency brought on by a high percentage of wind power access to a power grid can be effectively resolved by wind-storage collaborative participation in primary frequency regulation (PFR). However, the impact of energy storage participation in system-frequency regulation is significantly influenced by its state of charge (SOC). In this paper, considering the SOC of energy storage (ES) and the stochastic characteristics of wind turbine (WT) output, the control strategy of wind-storage collaborative participation in the PFR of a system is proposed. Firstly, a WT adaptive inertia control and a model of storage droop control were constructed. Additionally, to prevent the problem of secondary frequency drop brought on by a separate rotational kinetic energy control, a wind-storage collaborative frequency-regulation control scheme was constructed. Secondly, considering changes in wind speed and the SOC of ES, an improved dynamic droop-control strategy for ES is proposed. This strategy was combined with the adaptive inertia control of the WT to establish the PFR of the WT collaborative participation system. Lastly, a simulation example of a two-region, four-machine system was used to validate the efficacy of the frequency-control strategy presented in this paper. The results show that a significant percentage of WTs connected to a power grid can effectively have their frequency-response ability improved by wind-storage collaborative control.

Research Progress on the Synergistic Treatment of Multi Pollutants at Low Temperatures Using Carbon-Based Materials

Hg0, VOCs and NO are the main pollutants in sintering flue gas from the steel industry. In the wake of the swift progression of China’s steel sector over recent years, the separate removal of gaseous pollutants can no longer meet the current ultra-low emission requirements. The efficient collaborative management of multiple pollutants has low operating costs and conforms to the mainstream of ultra-low emission transformation. The efficient multi-pollutant synergistic treatment has low operating costs in comparison to traditional individual removal of gaseous pollutants. Furthermore, it aligns with the mainstream of ultra-low emission retrofit in China. How to achieve collaborative control of Hg0, VOCs and NO has become the focus of current research. Carbon-based materials (CBMs) have long been favored worldwide as the mainstream adsorbents for removing multiple air pollutants from flue gas. Based on the summary of the mechanisms for simultaneous removal of pollutants, this paper analyzes the future development directions of CBMs for the concurrent detoxification of Hg0, VOCs and NO, and provides a prospect for future research, providing technical support and theoretical basis for low-temperature cooperative control of pollutants in sintering, waste incineration and other processes.

Stable acceleration of a LHe-Free Nb3Sn demo SRF e-linac

Abstract The design, construction, and commissioning of a novel liquid helium-free (LHe-free) Nb3Sn superconducting radio frequency (SRF) electron accelerator at the Institute of Modern Physics of the Chinese Academy of Sciences (IMP, CAS) will be presented. A 650 MHz 5-cell elliptical cavity was coated using the tin vapor diffusion method for electron beam acceleration. The cavity was slowly cooled down across 18 K with the high-precision collaborative control of ten individual GM cryocoolers. This process was accompanied by the characteristic magnetic flux expulsion of Nb3Sn films. Horizontal tests of the LHe-free cryomodule show stable operation in both continuous wave (CW) and pulse modes, with maximum peak electric fields (E pk) of 6.02 and 14.90 MV m−1, respectively. The Nb3Sn SRF electron accelerator achieved stable beam acceleration, reaching a maximum energy of 4.6 MeV with an average macropulse beam current exceeding 100 mA. Additionally, stable electron beam acceleration was achieved for the first time at a cavity temperature of 10 K. This pioneering achievement demonstrates a principal validation for the feasibility of applying Nb3Sn thin film SRF cavities in both large-scale scientific facilities and compact industrial accelerators. It also opens up possibilities for further upgrades in operating temperature, cooling methods, and refrigeration equipment for SRF accelerators.