Design of general multivariable process controllers is an attractive and practical alternative to optimizing design by evolutionary algorithms (EAs) since it can be formulated as an optimization problem. A closed-loop...Design of general multivariable process controllers is an attractive and practical alternative to optimizing design by evolutionary algorithms (EAs) since it can be formulated as an optimization problem. A closed-loop particle swarm optimization (CLPSO) algorithm is proposed by mapping PSO elements into the closed-loop system based on control theories. At each time step, a proportional integral (PI) controller is used to calculate an updated inertia weight for each particle in swarms from its last fitness. With this modification, limitations caused by a uniform inertia weight for the whole population are avoided, and the particles have enough diversity. After the effectiveness, efficiency and robustness are tested by benchmark functions, CLPSO is applied to design a multivariable proportional-integral-derivative (PID) controller for a solvent dehydration tower in a chemical plant and has improved its performances.展开更多
This article expounds and proves the basic model of the inconsistency of the security supervision policy and makes an analysis in the method of game theory on the inconsistency of the security transaction-tax-rate pol...This article expounds and proves the basic model of the inconsistency of the security supervision policy and makes an analysis in the method of game theory on the inconsistency of the security transaction-tax-rate policy, concludes that the security supervision department is inclined to increase or decrease the security transaction tax rate, thus points out ways for supervision department to surmount this difficulty.展开更多
Based on control theory,adjoint system for the general problem of turbomachinery aerodynamic optimization was studied and developed in the present paper by using the variation technique in the grid node coordinates co...Based on control theory,adjoint system for the general problem of turbomachinery aerodynamic optimization was studied and developed in the present paper by using the variation technique in the grid node coordinates combined with Jacobian Matrics of flow fluxes.Then the adjoint system for aerodynamic design optimization of turbine cascade governed by compressible Navier-Stokes equations was derived in detail.With the purpose of saving computation resources,the mathematic method presented in this paper avoids the coordinate system transforming in the traditional derivation process of the adjoint system and makes the adjoint system much more sententious.Given the general expression of objective functions consisting of both boundary integral and field integral,the adjoint equations and their boundary conditions were derived,and the final expression of the objective function gradient including only boundary integrals was formulated to reduce the CPU cost,especially for the complex 3D configurations.The adjoint system was solved numerically by using the finite volume method with an explicit 5-step Runge-Kutta scheme and Riemann approximate solution of Roe's scheme combined with multi-grid technique and local time step to accelerate the convergence procedure.Finally,based on the aerodynamic optimization theory in the present work,2D and 3D inviscid and viscous inverse design programs of axial turbomachinery cascade for both pressure distribution and isentropic Mach number distribution on the blade wall were developed,and several design optimization cases were performed successfully to demonstrate the ability and economy of the present optimization system.展开更多
This paper considers optimization problems for a new kind of control systems based on non-equilibrium dynamic games.To be precise,the authors consider the infinitely repeated games between a human and a machine based ...This paper considers optimization problems for a new kind of control systems based on non-equilibrium dynamic games.To be precise,the authors consider the infinitely repeated games between a human and a machine based on the generic 2×2 game with fixed machine strategy of finite k-step memory.By introducing and analyzing the state transfer graphes(STG),it will be shown that the system state will become periodic after finite steps under the optimal strategy that maximizes the human’s averaged payoff,which helps us to ease the task of finding the optimal strategy considerably. Moreover,the question whether the optimizer will win or lose is investigated and some interesting phenomena are found,e.g.,for the standard Prisoner’s Dilemma game,the human will not lose to the machine while optimizing her own averaged payoff when k = 1;however,when k≥2,she may indeed lose if she focuses on optimizing her own payoff only The robustness of the optimal strategy and identification problem are also considered.It appears that both the framework and the results are beyond those in the classical control theory and the traditional game theory.展开更多
文摘Design of general multivariable process controllers is an attractive and practical alternative to optimizing design by evolutionary algorithms (EAs) since it can be formulated as an optimization problem. A closed-loop particle swarm optimization (CLPSO) algorithm is proposed by mapping PSO elements into the closed-loop system based on control theories. At each time step, a proportional integral (PI) controller is used to calculate an updated inertia weight for each particle in swarms from its last fitness. With this modification, limitations caused by a uniform inertia weight for the whole population are avoided, and the particles have enough diversity. After the effectiveness, efficiency and robustness are tested by benchmark functions, CLPSO is applied to design a multivariable proportional-integral-derivative (PID) controller for a solvent dehydration tower in a chemical plant and has improved its performances.
文摘This article expounds and proves the basic model of the inconsistency of the security supervision policy and makes an analysis in the method of game theory on the inconsistency of the security transaction-tax-rate policy, concludes that the security supervision department is inclined to increase or decrease the security transaction tax rate, thus points out ways for supervision department to surmount this difficulty.
基金supported by the National Natural Science Foundation of China (Grant No. 50776065)
文摘Based on control theory,adjoint system for the general problem of turbomachinery aerodynamic optimization was studied and developed in the present paper by using the variation technique in the grid node coordinates combined with Jacobian Matrics of flow fluxes.Then the adjoint system for aerodynamic design optimization of turbine cascade governed by compressible Navier-Stokes equations was derived in detail.With the purpose of saving computation resources,the mathematic method presented in this paper avoids the coordinate system transforming in the traditional derivation process of the adjoint system and makes the adjoint system much more sententious.Given the general expression of objective functions consisting of both boundary integral and field integral,the adjoint equations and their boundary conditions were derived,and the final expression of the objective function gradient including only boundary integrals was formulated to reduce the CPU cost,especially for the complex 3D configurations.The adjoint system was solved numerically by using the finite volume method with an explicit 5-step Runge-Kutta scheme and Riemann approximate solution of Roe's scheme combined with multi-grid technique and local time step to accelerate the convergence procedure.Finally,based on the aerodynamic optimization theory in the present work,2D and 3D inviscid and viscous inverse design programs of axial turbomachinery cascade for both pressure distribution and isentropic Mach number distribution on the blade wall were developed,and several design optimization cases were performed successfully to demonstrate the ability and economy of the present optimization system.
基金supported by the National Natural Science Foundation of China under Grant No.60821091 by the Knowledge Innovation Project of Chinese Academy of Sciences under Grant No.KJCX3-SYW-S01
文摘This paper considers optimization problems for a new kind of control systems based on non-equilibrium dynamic games.To be precise,the authors consider the infinitely repeated games between a human and a machine based on the generic 2×2 game with fixed machine strategy of finite k-step memory.By introducing and analyzing the state transfer graphes(STG),it will be shown that the system state will become periodic after finite steps under the optimal strategy that maximizes the human’s averaged payoff,which helps us to ease the task of finding the optimal strategy considerably. Moreover,the question whether the optimizer will win or lose is investigated and some interesting phenomena are found,e.g.,for the standard Prisoner’s Dilemma game,the human will not lose to the machine while optimizing her own averaged payoff when k = 1;however,when k≥2,she may indeed lose if she focuses on optimizing her own payoff only The robustness of the optimal strategy and identification problem are also considered.It appears that both the framework and the results are beyond those in the classical control theory and the traditional game theory.
