Inspired by the collective behaviors observed in bird flocks and fish schools,this paper proposes a novel Decentralized Model Predictive Flocking Control(DMPFC)framework to enable UAV swarms to autonomously track pred...Inspired by the collective behaviors observed in bird flocks and fish schools,this paper proposes a novel Decentralized Model Predictive Flocking Control(DMPFC)framework to enable UAV swarms to autonomously track predefined reference trajectories while avoiding collisions and maintaining a stable quasi[Math Processing Error]-lattice formation.Unlike traditional approaches that rely on switching between predefined swarm formations,this framework utilizes identical local interaction rules for each UAV,allowing them to dynamically adjust their control inputs based on the motion states of neighboring UAVs,external environmental factors,and the desired reference trajectory,thereby enabling the swarm to adapt its formation dynamically.Through iterative state updates,the UAVs achieve consensus,allowing the swarm to follow the reference trajectory while self-organizing into a cohesive and stable group structure.To enhance computational efficiency,the framework integrates a closed-form solution for the optimization process,enabling real-time implementation even on computationally constrained micro-quadrotors.Theoretical analysis demonstrates that the proposed method ensures swarm consensus,maintains desired inter-agent distances,and stabilizes the swarm formation.Extensive simulations and real-world experiments validate the approach’s effectiveness and practicality,demonstrating that the proposed method achieves velocity consensus within approximately 200 ms and forms a stable quasi[Math Processing Error]-lattice structure nearly ten times faster than traditional models,with trajectory tracking errors on the order of millimeters.This underscores its potential for robust and efficient UAV swarm coordination in complex scenarios.展开更多
Simulation is an important and useful technique helping users understand and model real life systems. Once built, the models can run proving realistic results. This supports making decisions on a more logical and scie...Simulation is an important and useful technique helping users understand and model real life systems. Once built, the models can run proving realistic results. This supports making decisions on a more logical and scientific basis. The paper introduces method of simulation, and describes various types of its application. The authors used the method of analysis of the creation and implementation of the programme code. The authors compared parallel instruction of computing defined to pipelined instructions. The power of simulation is that a common model can be used to design a large variety of systems. An important aspect of the simulation method is that a simulation model is designed to be repeated in actual computer systems, especially in multicore processors. For this reason, it is important to minimize average waiting time for fetch and decode stage instructions. The objective of the research is to prove that the parallel operation of programme code is faster than sequential operation code on the multi processor architecture. The system modeling uses methods and simulation on the parallel computer systems is very precise. The time benefit gained in simulation of mathematical model on the pipeline processor is higher than the one in simulation of mathematical model on the multi processors computer system.展开更多
The dynamic response of fuel cell vehicle is greatly affected by the pressure of reactants.Besides,the pressure difference between anode and cathode will also cause mechanical damage to proton exchange membrane.For ma...The dynamic response of fuel cell vehicle is greatly affected by the pressure of reactants.Besides,the pressure difference between anode and cathode will also cause mechanical damage to proton exchange membrane.For maintaining the relative stability of anode pressure,this study proposes a decentralized model predictive controller(DMPC)to control the anodic supply system composed of a feeding and returning ejector assembly.Considering the important influence of load current on the system,the piecewise linearization approach and state space with current-induced disturbance compensation are com-paratively analyzed.Then,an innovative switching strategy is proposed to prevent frequent switching of the sub-model-based controllers and to ensure the most appropriate predictive model is applied.Finally,simulation results demonstrate the better stability and robustness of the proposed control schemes compared with the traditional proportion integration differentia-tion controller under the step load current,variable target and purge disturbance conditions.In particular,in the case of the DC bus load current of a fuel cell hybrid vehicle,the DMPC controller with current-induced disturbance compensation has better stability and target tracking performance with an average error of 0.15 kPa and root mean square error of 1.07 kPa.展开更多
基金supported in part by the Guangdong Provincial Universities'Characteristic Innovation Project under Grant 2024KTSCX360in part by the Guangdong Educational Science Planning Project under Grant 2023GXJK837.
文摘Inspired by the collective behaviors observed in bird flocks and fish schools,this paper proposes a novel Decentralized Model Predictive Flocking Control(DMPFC)framework to enable UAV swarms to autonomously track predefined reference trajectories while avoiding collisions and maintaining a stable quasi[Math Processing Error]-lattice formation.Unlike traditional approaches that rely on switching between predefined swarm formations,this framework utilizes identical local interaction rules for each UAV,allowing them to dynamically adjust their control inputs based on the motion states of neighboring UAVs,external environmental factors,and the desired reference trajectory,thereby enabling the swarm to adapt its formation dynamically.Through iterative state updates,the UAVs achieve consensus,allowing the swarm to follow the reference trajectory while self-organizing into a cohesive and stable group structure.To enhance computational efficiency,the framework integrates a closed-form solution for the optimization process,enabling real-time implementation even on computationally constrained micro-quadrotors.Theoretical analysis demonstrates that the proposed method ensures swarm consensus,maintains desired inter-agent distances,and stabilizes the swarm formation.Extensive simulations and real-world experiments validate the approach’s effectiveness and practicality,demonstrating that the proposed method achieves velocity consensus within approximately 200 ms and forms a stable quasi[Math Processing Error]-lattice structure nearly ten times faster than traditional models,with trajectory tracking errors on the order of millimeters.This underscores its potential for robust and efficient UAV swarm coordination in complex scenarios.
文摘Simulation is an important and useful technique helping users understand and model real life systems. Once built, the models can run proving realistic results. This supports making decisions on a more logical and scientific basis. The paper introduces method of simulation, and describes various types of its application. The authors used the method of analysis of the creation and implementation of the programme code. The authors compared parallel instruction of computing defined to pipelined instructions. The power of simulation is that a common model can be used to design a large variety of systems. An important aspect of the simulation method is that a simulation model is designed to be repeated in actual computer systems, especially in multicore processors. For this reason, it is important to minimize average waiting time for fetch and decode stage instructions. The objective of the research is to prove that the parallel operation of programme code is faster than sequential operation code on the multi processor architecture. The system modeling uses methods and simulation on the parallel computer systems is very precise. The time benefit gained in simulation of mathematical model on the pipeline processor is higher than the one in simulation of mathematical model on the multi processors computer system.
基金supported in part by the Technological Innovation and Application Demonstration in Chongqing(Major Themes of Industry:cstc2019jscx-zdztzxX0033,cstc2019jscx-fxyd0158).
文摘The dynamic response of fuel cell vehicle is greatly affected by the pressure of reactants.Besides,the pressure difference between anode and cathode will also cause mechanical damage to proton exchange membrane.For maintaining the relative stability of anode pressure,this study proposes a decentralized model predictive controller(DMPC)to control the anodic supply system composed of a feeding and returning ejector assembly.Considering the important influence of load current on the system,the piecewise linearization approach and state space with current-induced disturbance compensation are com-paratively analyzed.Then,an innovative switching strategy is proposed to prevent frequent switching of the sub-model-based controllers and to ensure the most appropriate predictive model is applied.Finally,simulation results demonstrate the better stability and robustness of the proposed control schemes compared with the traditional proportion integration differentia-tion controller under the step load current,variable target and purge disturbance conditions.In particular,in the case of the DC bus load current of a fuel cell hybrid vehicle,the DMPC controller with current-induced disturbance compensation has better stability and target tracking performance with an average error of 0.15 kPa and root mean square error of 1.07 kPa.
