Mixed-weight least-squares (MWLS) predictive control algorithm, compared with quadratic programming (QP) method, has the advantages of reducing the computer burden, quick calculation speed and dealing with the case in...Mixed-weight least-squares (MWLS) predictive control algorithm, compared with quadratic programming (QP) method, has the advantages of reducing the computer burden, quick calculation speed and dealing with the case in which the optimization is infeasible. But it can only deal with soft constraints. In order to deal with hard constraints and guarantee feasibility, an improved algorithm is proposed by recalculating the setpoint according to the hard constraints before calculating the manipulated variable and MWLS algorithm is used to satisfy the requirement of soft constraints for the system with the input constraints and output constraints. The algorithm can not only guarantee stability of the system and zero steady state error, but also satisfy the hard constraints of input and output variables. The simulation results show the improved algorithm is feasible and effective.展开更多
In this work,a new method to solve the Reynolds equation including mass-conserving cavitation by using the physics informed neural networks(PINNs)is proposed.The complementarity relationship between the pressure and t...In this work,a new method to solve the Reynolds equation including mass-conserving cavitation by using the physics informed neural networks(PINNs)is proposed.The complementarity relationship between the pressure and the void fraction is used.There are several difficulties in problem solving,and the solutions are provided.Firstly,the difficulty for considering the pressure inequality constraint by PINNs is solved by transferring it into one equality constraint without introducing error.While the void fraction inequality constraint is considered by using the hard constraint with the max-min function.Secondly,to avoid the fluctuation of the boundary value problems,the hard constraint method is also utilized to apply the boundary pressure values and the corresponding functions are provided.Lastly,for avoiding the trivial solution the limitation for the mean value of the void fraction is applied.The results are validated against existing data,and both the incompressible and compressible lubricant are considered.Good agreement can be found for both the domain and domain boundaries.展开更多
We address the problem of optimizing a distributed monitoring system and the goal of the optimization is to reduce the cost of deployment of the monitoring infrastructure by identifying a minimum aggregating set subje...We address the problem of optimizing a distributed monitoring system and the goal of the optimization is to reduce the cost of deployment of the monitoring infrastructure by identifying a minimum aggregating set subject to delay constraint on the aggregating path. We show that this problem is NP-hard and propose approximation algorithm proving the approximation ratio with lnm+1, where is the number of monitoring nodes. At last we extend our modal with more constraint of bounded delay variation. Key words network - distributed monitoring - delay constraint - NP-hard CLC number TP 393 Foundation item: Supported by the National Natural Science Foundation of China (60373023)Biography: LIU Xiang-hui(1973-), male, Ph. D. candidate, research direction: algorithm complexity analysis, QoS in Internet.展开更多
基金National Key Basic Research and Development(No.2002CB312200)
文摘Mixed-weight least-squares (MWLS) predictive control algorithm, compared with quadratic programming (QP) method, has the advantages of reducing the computer burden, quick calculation speed and dealing with the case in which the optimization is infeasible. But it can only deal with soft constraints. In order to deal with hard constraints and guarantee feasibility, an improved algorithm is proposed by recalculating the setpoint according to the hard constraints before calculating the manipulated variable and MWLS algorithm is used to satisfy the requirement of soft constraints for the system with the input constraints and output constraints. The algorithm can not only guarantee stability of the system and zero steady state error, but also satisfy the hard constraints of input and output variables. The simulation results show the improved algorithm is feasible and effective.
基金the funding from Anhui University of Science and Technology(No.2022yjrc15)the Key Project of National Natural Science Foundation of China(Nos.U21A20125 and U21A20122)+1 种基金the Key Research and Development Projects of Anhui Province(No.2022a05020043)the National Natural Science Foundation of China(Nos.51805410 and 51804007).
文摘In this work,a new method to solve the Reynolds equation including mass-conserving cavitation by using the physics informed neural networks(PINNs)is proposed.The complementarity relationship between the pressure and the void fraction is used.There are several difficulties in problem solving,and the solutions are provided.Firstly,the difficulty for considering the pressure inequality constraint by PINNs is solved by transferring it into one equality constraint without introducing error.While the void fraction inequality constraint is considered by using the hard constraint with the max-min function.Secondly,to avoid the fluctuation of the boundary value problems,the hard constraint method is also utilized to apply the boundary pressure values and the corresponding functions are provided.Lastly,for avoiding the trivial solution the limitation for the mean value of the void fraction is applied.The results are validated against existing data,and both the incompressible and compressible lubricant are considered.Good agreement can be found for both the domain and domain boundaries.
文摘We address the problem of optimizing a distributed monitoring system and the goal of the optimization is to reduce the cost of deployment of the monitoring infrastructure by identifying a minimum aggregating set subject to delay constraint on the aggregating path. We show that this problem is NP-hard and propose approximation algorithm proving the approximation ratio with lnm+1, where is the number of monitoring nodes. At last we extend our modal with more constraint of bounded delay variation. Key words network - distributed monitoring - delay constraint - NP-hard CLC number TP 393 Foundation item: Supported by the National Natural Science Foundation of China (60373023)Biography: LIU Xiang-hui(1973-), male, Ph. D. candidate, research direction: algorithm complexity analysis, QoS in Internet.
