Load distribution strategy for multi-lift system with helicopters based on power consumption and robust adaptive game control

It is of great significance to reasonably distribute the slung load to each helicopter while considering difference in power consumption,relative position and interaction comprehensively.Therefore,the load distribution strategy based on power consumption and robust adaptive game control is proposed in this paper.The study is on a"2-lead"multi-lift system of four tandem heli-copters carrying a load cooperatively.First,based on the hierarchical control,the load distribution problem is divided into two parts:the calculation of expected cable force and the calculation of the anti-disturbance cable force.Then,aimed at minimizing the maximum equivalent power of heli-copter,an optimization problem is set up to calculate the expected cable force.Specially,the agent power model is trained by BP neural network,the safe distance constraint between helicopters is set to 2.5 rotor diameters to reduce aerodynamic interference,and the helicopters with different perfor-mance can be considered by introducing the equivalent power factor into the objective function.Next,considering the difference and interaction between helicopters,the robust adaptive differen-tial game control is proposed to calculate the anti-disturbance cable force.Particularly,to solve the coupled Hamiltonian equations,an adaptive solving method for value function is proposed,and its stability is proved in the sense of Lyapunov.The simulation results indicate that the proposed load distribution method based on power consumption is applicable to the entire flight trajectory even there are differences between helicopters.The game control can consider interaction between heli-copters,can deal with different objective functions,and has strong robustness and small steady-state error.Based on the entire strategy,the cable force can be reasonably allocated so as to resist disturbance and improve the flight performance of the whole system.

Simultaneous enhancement in mechanical and corrosion properties of Al-Mg-Si alloys using machine learning

Al-Mg-Si alloys with high strength and good corrosion resistance are regarded as desirable materials for all-aluminum vehicles.However,the traditional trial-and-error experimental methods are insufficient to address the trade-offbetween strength and corrosion resistance.In this work,a non-dominated sorting genetic machine-learning algorithm(NSGA-II)was employed to optimize the chemical composition,so as to simultaneously improve the strength and corrosion resistance.Three high-performance Al-Mg-Si alloys with low Mg,Si,and Cu contents were successfully developed,where the yield strength(YS),ultimate tensile strength(UTS),and the elongation(δ)reached 375-380 MPa,410-416 MPa,and 13.7%-15.2%,re-spectively.Compared with higher-Cu-content 6013 alloy,the YS and UTS of the present alloys increase by about 60 MPa,and the intergranular corrosion resistance is also significantly improved.Microstruc-ture characterization demonstrated thatβ’’and QP phases introduced a significant synergistic precipita-tion strengthening effect;the dispersoids formed by trace Mn,Cr,Fe,Zr,and Ti contributed dispersion strengthening effect;and the good pitting corrosion resistance is attributed to lower Mg and Si contents.