By handling the travel cost function artfully, the authors formulate the transportation mixed network design problem （MNDP） as a mixed-integer, nonlinear bilevel programming problem, in which the lower-level problem...By handling the travel cost function artfully, the authors formulate the transportation mixed network design problem （MNDP） as a mixed-integer, nonlinear bilevel programming problem, in which the lower-level problem, comparing with that of conventional bilevel DNDP models, is not a side constrained user equilibrium assignment problem, but a standard user equilibrium assignment problem. Then, the bilevel programming model for MNDP is reformulated as a continuous version of bilevel programming problem by the continuation method. By virtue of the optimal-value function, the lower-level assignment problem can be expressed as a nonlinear equality constraint. Therefore, the bilevel programming model for MNDP can be transformed into an equivalent single-level optimization problem. By exploring the inherent nature of the MNDP, the optimal-value function for the lower- level equilibrium assignment problem is proved to be continuously differentiable and its functional value and gradient can be obtained efficiently. Thus, a continuously differentiable but still nonconvex optimization formulation of the MNDP is created, and then a locally convergent algorithm is proposed by applying penalty function method. The inner loop of solving the subproblem is mainly to implement an Ml-or-nothing assignment. Finally, a small-scale transportation network and a large-scale network are presented to verify the proposed model and algorithm.展开更多
In this paper, we study optimal value functions of generalized semi-infinite min-max programming problems on a noncompact set. Directional derivatives and subdifferential characterizations of optimal value functions a...In this paper, we study optimal value functions of generalized semi-infinite min-max programming problems on a noncompact set. Directional derivatives and subdifferential characterizations of optimal value functions are given. Using these properties,we establish first order optimality conditions for unconstrained generalized semi-infinite programming problems.展开更多
Because of system constraints caused by the external environment and grid faults,the conventional maximum power point tracking(MPPT)and inverter control methods of a PV power generation system cannot achieve optimal p...Because of system constraints caused by the external environment and grid faults,the conventional maximum power point tracking(MPPT)and inverter control methods of a PV power generation system cannot achieve optimal power output.They can also lead to misjudgments and poor dynamic performance.To address these issues,this paper proposes a new MPPT method of PV modules based on model predictive control(MPC)and a finite control set model predictive current control(FCS-MPCC)of an inverter.Using the identification model of PV arrays,the module-based MPC controller is designed,and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature.An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors,the optimal voltage vector is selected according to the optimal value function,and the corresponding optimal switching state is applied to power semiconductor devices of the inverter.The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified,and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink.The results show that MPC has better tracking performance under constraints,and the system has faster and more accurate dynamic response and flexibility than conventional PI control.展开更多
Applications of robots in tasks where the robot's end-effector bears loads, such as manipulating or assembling an object, picking-and-placing loads, grinding or drilling, demand precision. One aspect that improve...Applications of robots in tasks where the robot's end-effector bears loads, such as manipulating or assembling an object, picking-and-placing loads, grinding or drilling, demand precision. One aspect that improves precision is the limitation, if not elimination, of manipulator compliance. This paper presents a manipulator compliance optimization approach for determining an optimal manipulator configuration for a given position in the robot's task space. A numerical solution for minimal compliance, a nonlinear constrained optimization problem, is presented for an arbitrary position and illustrated by an example, using a model developed on ADAMS software and using MATLAB optimization tools. Also, this paper investigates the optimal value function for robot tasks in which the tool-point is subjected to applied force as it generates an important trajectory such as in grinding processes. The optimal value function is needed for optimal configuration control.展开更多
This study focuses on investigating the optimal investment strategy for an optimization problem with delay using the uncertainty theory. The financial market is composed of a risk-free asset and a risk asset with an u...This study focuses on investigating the optimal investment strategy for an optimization problem with delay using the uncertainty theory. The financial market is composed of a risk-free asset and a risk asset with an uncertain price process described by an uncertain differential equation. An optimization problem is assumed that its objective is a nonlinear function of decision variable. By deriving the equation of optimality, an analytical solution is obtained for the optimal delay investment strategy, and the optimal delay value function. Finally, an economic analysis and numerical sensitivity analysis are conducted to evaluate the research results.展开更多
基金supported by the National Basic Research Program of China under Grant No. 2006CB705500the National Natural Science Foundation of China under Grant No. 0631001+1 种基金the Program for Changjiang Scholars and Innovative Research Team in University Volvo Research and Educational Foundations
文摘By handling the travel cost function artfully, the authors formulate the transportation mixed network design problem （MNDP） as a mixed-integer, nonlinear bilevel programming problem, in which the lower-level problem, comparing with that of conventional bilevel DNDP models, is not a side constrained user equilibrium assignment problem, but a standard user equilibrium assignment problem. Then, the bilevel programming model for MNDP is reformulated as a continuous version of bilevel programming problem by the continuation method. By virtue of the optimal-value function, the lower-level assignment problem can be expressed as a nonlinear equality constraint. Therefore, the bilevel programming model for MNDP can be transformed into an equivalent single-level optimization problem. By exploring the inherent nature of the MNDP, the optimal-value function for the lower- level equilibrium assignment problem is proved to be continuously differentiable and its functional value and gradient can be obtained efficiently. Thus, a continuously differentiable but still nonconvex optimization formulation of the MNDP is created, and then a locally convergent algorithm is proposed by applying penalty function method. The inner loop of solving the subproblem is mainly to implement an Ml-or-nothing assignment. Finally, a small-scale transportation network and a large-scale network are presented to verify the proposed model and algorithm.
基金The authors thank Prof.J.Y.Han,Dr.Lian Shujun and the referees for their valuable suggestion.This work was parially supported by the National Natural Science Foundation of China(Grants No.10171055 and 10171118)the Excellent Young Teachers Program of M0E,P.R.C.the Research Committee of the Hong Kong Polytechnic University.
文摘In this paper, we study optimal value functions of generalized semi-infinite min-max programming problems on a noncompact set. Directional derivatives and subdifferential characterizations of optimal value functions are given. Using these properties,we establish first order optimality conditions for unconstrained generalized semi-infinite programming problems.
基金supported by National Science Foundation of China(61563032,61963025)Project supported by Gansu Basic Research Innovation Group(18JR3RA133)+1 种基金Industrial Support and Guidance Project for Higher Education Institutions of Gansu Province(2019C-05)Open Fund Project of Key Laboratory of Industrial Process Advanced Control of Gansu Province(2019KFJJ02).
文摘Because of system constraints caused by the external environment and grid faults,the conventional maximum power point tracking(MPPT)and inverter control methods of a PV power generation system cannot achieve optimal power output.They can also lead to misjudgments and poor dynamic performance.To address these issues,this paper proposes a new MPPT method of PV modules based on model predictive control(MPC)and a finite control set model predictive current control(FCS-MPCC)of an inverter.Using the identification model of PV arrays,the module-based MPC controller is designed,and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature.An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors,the optimal voltage vector is selected according to the optimal value function,and the corresponding optimal switching state is applied to power semiconductor devices of the inverter.The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified,and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink.The results show that MPC has better tracking performance under constraints,and the system has faster and more accurate dynamic response and flexibility than conventional PI control.
文摘Applications of robots in tasks where the robot's end-effector bears loads, such as manipulating or assembling an object, picking-and-placing loads, grinding or drilling, demand precision. One aspect that improves precision is the limitation, if not elimination, of manipulator compliance. This paper presents a manipulator compliance optimization approach for determining an optimal manipulator configuration for a given position in the robot's task space. A numerical solution for minimal compliance, a nonlinear constrained optimization problem, is presented for an arbitrary position and illustrated by an example, using a model developed on ADAMS software and using MATLAB optimization tools. Also, this paper investigates the optimal value function for robot tasks in which the tool-point is subjected to applied force as it generates an important trajectory such as in grinding processes. The optimal value function is needed for optimal configuration control.
文摘This study focuses on investigating the optimal investment strategy for an optimization problem with delay using the uncertainty theory. The financial market is composed of a risk-free asset and a risk asset with an uncertain price process described by an uncertain differential equation. An optimization problem is assumed that its objective is a nonlinear function of decision variable. By deriving the equation of optimality, an analytical solution is obtained for the optimal delay investment strategy, and the optimal delay value function. Finally, an economic analysis and numerical sensitivity analysis are conducted to evaluate the research results.
