Matlab has a high performance at engineering calculation.C# is good at interface development.Combining their advantages together,hybrid programming with Matlab and C # will help to improve the reliability analysis sof...Matlab has a high performance at engineering calculation.C# is good at interface development.Combining their advantages together,hybrid programming with Matlab and C # will help to improve the reliability analysis software efficiency and accuracy significantly.Procedures of hybrid programming with Matlab and C# in reliability analysis software are introduced in this paper.Finally a mathematical problem is tested to verify the feasibility of this programming method.展开更多
The distributed hybrid processing optimization problem of non-cooperative targets is an important research direction for future networked air-defense and anti-missile firepower systems. In this paper, the air-defense ...The distributed hybrid processing optimization problem of non-cooperative targets is an important research direction for future networked air-defense and anti-missile firepower systems. In this paper, the air-defense anti-missile targets defense problem is abstracted as a nonconvex constrained combinatorial optimization problem with the optimization objective of maximizing the degree of contribution of the processing scheme to non-cooperative targets, and the constraints mainly consider geographical conditions and anti-missile equipment resources. The grid discretization concept is used to partition the defense area into network nodes, and the overall defense strategy scheme is described as a nonlinear programming problem to solve the minimum defense cost within the maximum defense capability of the defense system network. In the solution of the minimum defense cost problem, the processing scheme, equipment coverage capability, constraints and node cost requirements are characterized, then a nonlinear mathematical model of the non-cooperative target distributed hybrid processing optimization problem is established, and a local optimal solution based on the sequential quadratic programming algorithm is constructed, and the optimal firepower processing scheme is given by using the sequential quadratic programming method containing non-convex quadratic equations and inequality constraints. Finally, the effectiveness of the proposed method is verified by simulation examples.展开更多
The goal of this work is to develop a hybrid electric vehicle model that is suitable for use in a dynamic programming algorithm that provides the benchmark for optimal control of the hybrid powertrain. The benchmark a...The goal of this work is to develop a hybrid electric vehicle model that is suitable for use in a dynamic programming algorithm that provides the benchmark for optimal control of the hybrid powertrain. The benchmark analysis employs dynamic programming by backward induction to determine the globally optimal solution by solving the energy management problem starting at the final timestep and proceeding backwards in time. This method requires the development of a backwards facing model that propagates the wheel speed of the vehicle for the given drive cycle through the driveline components to determine the operating points of the powertrain. Although dynamic programming only searches the solution space within the feasible regions of operation, the benchmarking model must be solved for every admissible state at every timestep leading to strict requirements for runtime and memory. The backward facing model employs the quasi-static assumption of powertrain operation to reduce the fidelity of the model to accommodate these requirements. Verification and validation testing of the dynamic programming algorithm is conducted to ensure successful operation of the algorithm and to assess the validity of the determined control policy against a high-fidelity forward-facing vehicle model with a percent difference of fuel consumption of 1.2%. The benchmark analysis is conducted over multiple drive cycles to determine the optimal control policy that provides a benchmark for real-time algorithm development and determines control trends that can be used to improve existing algorithms. The optimal combined charge sustaining fuel economy of the vehicle is determined by the dynamic programming algorithm to be 32.99 MPG, a 52.6% increase over the stock 3.6 L 2019 Chevrolet Blazer.展开更多
In this paper, a linear programming method is proposed to solve model predictive control for a class of hybrid systems. Firstly, using the （max, ＋） algebra, a typical subclass of hybrid systems called max-plus-line...In this paper, a linear programming method is proposed to solve model predictive control for a class of hybrid systems. Firstly, using the （max, ＋） algebra, a typical subclass of hybrid systems called max-plus-linear （MPL） systems is obtained. And then, model predictive control （MPC） framework is extended to MPL systems. In general, the nonlinear optimization approach or extended linear complementarity problem （ELCP） were applied to solve the MPL-MPC optimization problem. A new optimization method based on canonical forms for max-min-plus-scaling （MMPS） functions （using the operations maximization, minimization, addition and scalar multiplication） with linear constraints on the inputs is presented. The proposed approach consists in solving several linear programming problems and is more efficient than nonlinear optimization. The validity of the algorithm is illustrated by an example.展开更多
The present paper aims at validating a Model Predictive Control(MPC),based on the Mixed Logical Dynamical(MLD)model,for Hybrid Dynamic Systems(HDSs)that explicitly involve continuous dynamics and discrete events.The p...The present paper aims at validating a Model Predictive Control(MPC),based on the Mixed Logical Dynamical(MLD)model,for Hybrid Dynamic Systems(HDSs)that explicitly involve continuous dynamics and discrete events.The proposed benchmark system is a three-tank process,which is a typical case study of HDSs.The MLD-MPC controller is applied to the level control of the considered tank system.The study is initially focused on the MLD approach that allows consideration of the interacting continuous dynamics with discrete events and includes the operating constraints.This feature of MLD modeling is very advantageous when an MPC controller synthesis for the HDSs is designed.Once the MLD model of the system is well-posed,then the MPC law synthesis can be developed based on the Mixed Integer Programming(MIP)optimization problem.For solving this MIP problem,a Branch and Bound(B&B)algorithm is proposed to determine the optimal control inputs.Then,a comparative study is carried out to illustrate the effectiveness of the proposed hybrid controller for the HDSs compared to the standard MPC approach.Performances results show that the MLD-MPC approach outperforms the standardMPCone that doesn’t consider the hybrid aspect of the system.The paper also shows a behavioral test of the MLDMPC controller against disturbances deemed as liquid leaks from the system.The results are very satisfactory and show that the tracking error is minimal less than 0.1%in nominal conditions and less than 0.6%in the presence of disturbances.Such results confirm the success of the MLD-MPC approach for the control of the HDSs.展开更多
Remote and Hybrid work has been a common practice for many organizations in recent years. It has many advantages such as offering a better work-life balance but it might also negatively affect productivity and teamwor...Remote and Hybrid work has been a common practice for many organizations in recent years. It has many advantages such as offering a better work-life balance but it might also negatively affect productivity and teamwork. While an organization would like to satisfy the remote/hybrid preferences of its employees, it also must ensure that there are enough people working in the office to satisfy certain professional needs. Finding the right balance between in-office and remote work is not an easy task. We develop three optimization models to give solutions to the problem. The most comprehensive model allows employees to work remotely some days of the week and flexible hours for those weekdays when employees work in the office. Our computational results show that the models are very time-efficient in practice. The computational results also include a sensitivity analysis of the most comprehensive model.展开更多
Dynamic programming(DP)is frequently used to obtain the optimal solution to the hybrid electric vehicle(HEV)energy management.The trade-off between the accuracy and the computational effort is the biggest problem for ...Dynamic programming(DP)is frequently used to obtain the optimal solution to the hybrid electric vehicle(HEV)energy management.The trade-off between the accuracy and the computational effort is the biggest problem for the DP method.The closed-form solution to the DP is proposed to solve this problem.Firstly,the affine linear model of the engine fuel rate is obtained based on engine test data.The piecewise linear approximation of the motor power demand is obtained considering the different energy flows in the charging and discharging stages of the battery.Then,the second-order Taylor expansion for the cost matrix at each time and state grid point is introduced to get the closed-form solution of the optimal torque split.The results show that this method can greatly reduce the computing burden by 93%while ensuring near-optimal fuel economy compared with the conventional DP method.展开更多
文摘Matlab has a high performance at engineering calculation.C# is good at interface development.Combining their advantages together,hybrid programming with Matlab and C # will help to improve the reliability analysis software efficiency and accuracy significantly.Procedures of hybrid programming with Matlab and C# in reliability analysis software are introduced in this paper.Finally a mathematical problem is tested to verify the feasibility of this programming method.
基金supported by the National Natural Science Foundation of China (61903025)the Fundamental Research Funds for the Cent ral Universities (FRF-IDRY-20-013)。
文摘The distributed hybrid processing optimization problem of non-cooperative targets is an important research direction for future networked air-defense and anti-missile firepower systems. In this paper, the air-defense anti-missile targets defense problem is abstracted as a nonconvex constrained combinatorial optimization problem with the optimization objective of maximizing the degree of contribution of the processing scheme to non-cooperative targets, and the constraints mainly consider geographical conditions and anti-missile equipment resources. The grid discretization concept is used to partition the defense area into network nodes, and the overall defense strategy scheme is described as a nonlinear programming problem to solve the minimum defense cost within the maximum defense capability of the defense system network. In the solution of the minimum defense cost problem, the processing scheme, equipment coverage capability, constraints and node cost requirements are characterized, then a nonlinear mathematical model of the non-cooperative target distributed hybrid processing optimization problem is established, and a local optimal solution based on the sequential quadratic programming algorithm is constructed, and the optimal firepower processing scheme is given by using the sequential quadratic programming method containing non-convex quadratic equations and inequality constraints. Finally, the effectiveness of the proposed method is verified by simulation examples.
文摘The goal of this work is to develop a hybrid electric vehicle model that is suitable for use in a dynamic programming algorithm that provides the benchmark for optimal control of the hybrid powertrain. The benchmark analysis employs dynamic programming by backward induction to determine the globally optimal solution by solving the energy management problem starting at the final timestep and proceeding backwards in time. This method requires the development of a backwards facing model that propagates the wheel speed of the vehicle for the given drive cycle through the driveline components to determine the operating points of the powertrain. Although dynamic programming only searches the solution space within the feasible regions of operation, the benchmarking model must be solved for every admissible state at every timestep leading to strict requirements for runtime and memory. The backward facing model employs the quasi-static assumption of powertrain operation to reduce the fidelity of the model to accommodate these requirements. Verification and validation testing of the dynamic programming algorithm is conducted to ensure successful operation of the algorithm and to assess the validity of the determined control policy against a high-fidelity forward-facing vehicle model with a percent difference of fuel consumption of 1.2%. The benchmark analysis is conducted over multiple drive cycles to determine the optimal control policy that provides a benchmark for real-time algorithm development and determines control trends that can be used to improve existing algorithms. The optimal combined charge sustaining fuel economy of the vehicle is determined by the dynamic programming algorithm to be 32.99 MPG, a 52.6% increase over the stock 3.6 L 2019 Chevrolet Blazer.
基金This work was supported by the National Science Foundation of China (No. 60474051)the program for New Century Excellent Talents in University of China (NCET).
文摘In this paper, a linear programming method is proposed to solve model predictive control for a class of hybrid systems. Firstly, using the （max, ＋） algebra, a typical subclass of hybrid systems called max-plus-linear （MPL） systems is obtained. And then, model predictive control （MPC） framework is extended to MPL systems. In general, the nonlinear optimization approach or extended linear complementarity problem （ELCP） were applied to solve the MPL-MPC optimization problem. A new optimization method based on canonical forms for max-min-plus-scaling （MMPS） functions （using the operations maximization, minimization, addition and scalar multiplication） with linear constraints on the inputs is presented. The proposed approach consists in solving several linear programming problems and is more efficient than nonlinear optimization. The validity of the algorithm is illustrated by an example.
文摘The present paper aims at validating a Model Predictive Control(MPC),based on the Mixed Logical Dynamical(MLD)model,for Hybrid Dynamic Systems(HDSs)that explicitly involve continuous dynamics and discrete events.The proposed benchmark system is a three-tank process,which is a typical case study of HDSs.The MLD-MPC controller is applied to the level control of the considered tank system.The study is initially focused on the MLD approach that allows consideration of the interacting continuous dynamics with discrete events and includes the operating constraints.This feature of MLD modeling is very advantageous when an MPC controller synthesis for the HDSs is designed.Once the MLD model of the system is well-posed,then the MPC law synthesis can be developed based on the Mixed Integer Programming(MIP)optimization problem.For solving this MIP problem,a Branch and Bound(B&B)algorithm is proposed to determine the optimal control inputs.Then,a comparative study is carried out to illustrate the effectiveness of the proposed hybrid controller for the HDSs compared to the standard MPC approach.Performances results show that the MLD-MPC approach outperforms the standardMPCone that doesn’t consider the hybrid aspect of the system.The paper also shows a behavioral test of the MLDMPC controller against disturbances deemed as liquid leaks from the system.The results are very satisfactory and show that the tracking error is minimal less than 0.1%in nominal conditions and less than 0.6%in the presence of disturbances.Such results confirm the success of the MLD-MPC approach for the control of the HDSs.
文摘Remote and Hybrid work has been a common practice for many organizations in recent years. It has many advantages such as offering a better work-life balance but it might also negatively affect productivity and teamwork. While an organization would like to satisfy the remote/hybrid preferences of its employees, it also must ensure that there are enough people working in the office to satisfy certain professional needs. Finding the right balance between in-office and remote work is not an easy task. We develop three optimization models to give solutions to the problem. The most comprehensive model allows employees to work remotely some days of the week and flexible hours for those weekdays when employees work in the office. Our computational results show that the models are very time-efficient in practice. The computational results also include a sensitivity analysis of the most comprehensive model.
基金National Natural Science Foundation of China:[Grant Number 52077217].
文摘Dynamic programming(DP)is frequently used to obtain the optimal solution to the hybrid electric vehicle(HEV)energy management.The trade-off between the accuracy and the computational effort is the biggest problem for the DP method.The closed-form solution to the DP is proposed to solve this problem.Firstly,the affine linear model of the engine fuel rate is obtained based on engine test data.The piecewise linear approximation of the motor power demand is obtained considering the different energy flows in the charging and discharging stages of the battery.Then,the second-order Taylor expansion for the cost matrix at each time and state grid point is introduced to get the closed-form solution of the optimal torque split.The results show that this method can greatly reduce the computing burden by 93%while ensuring near-optimal fuel economy compared with the conventional DP method.
