An iterative learning control algorithm based on shifted Legendre orthogonal polynomials is proposed to address the terminal control problem of linear time-varying systems. First, the method parameterizes a linear tim...An iterative learning control algorithm based on shifted Legendre orthogonal polynomials is proposed to address the terminal control problem of linear time-varying systems. First, the method parameterizes a linear time-varying system by using shifted Legendre polynomials approximation. Then, an approximated model for the linear time-varying system is deduced by employing the orthogonality relations and boundary values of shifted Legendre polynomials. Based on the model, the shifted Legendre polynomials coefficients of control function are iteratively adjusted by an optimal iterative learning law derived. The algorithm presented can avoid solving the state transfer matrix of linear time-varying systems. Simulation results illustrate the effectiveness of the proposed method.展开更多
为设计高效稳定的演化算法,将方程求根的不动点迭代思想引入到优化领域,通过将演化算法的寻优过程看作为在迭代框架下方程不动点的逐步显示化过程,设计出一种基于数学模型的演化新算法,即不动点演化算法(fixed point evolution algorith...为设计高效稳定的演化算法,将方程求根的不动点迭代思想引入到优化领域,通过将演化算法的寻优过程看作为在迭代框架下方程不动点的逐步显示化过程,设计出一种基于数学模型的演化新算法,即不动点演化算法(fixed point evolution algorithm,FPEA).该算法的繁殖算子是由Aitken加速的不动点迭代模型导出的二次多项式,其整体框架继承传统演化算法(如差分演化算法)基于种群的迭代模式.试验结果表明:在基准函数集CEC2014、CEC2019上,本文算法的最优值平均排名在所有比较算法中排名第1;在4个工程约束设计问题上,FPEA与CSA、GPE等多个算法相比,能以较少的计算开销获得最高的求解精度.展开更多
In the paper, the problem of H∞ decentralized state feedback control for largescale systems is described. An algorithm is proposed which uses the method of a feasible direction matrix. The algorithm only requires the...In the paper, the problem of H∞ decentralized state feedback control for largescale systems is described. An algorithm is proposed which uses the method of a feasible direction matrix. The algorithm only requires the solution of an algebraic Riccati equation (ARE) and makes the H∞norm of the closedloop transfer function matrix from disturbance inputs to controlled outputs less than a given constant which ensure the stability of the overall controlled system at each iteration. The given example shows that the convergence of the algorithm is satisfactory.展开更多
This paper is devoted to study the multiobjective system programming under the assumption that some of the problem parameters are random variables. A method called Interactive Reference Goal Satisfied Degree and Feasi...This paper is devoted to study the multiobjective system programming under the assumption that some of the problem parameters are random variables. A method called Interactive Reference Goal Satisfied Degree and Feasible Degree (IRGSD-FD) is developed to solve stochastic multiobjective problems. It is an interactive method providing a so-called `dialogue' between the user and the model, the decision maker having the option conducting the search process for the (α, β)-efficient solutions by modifying the initial conditions according to the partial results obtained. During the iterations, the decision maker can improve upon the reference goal or called aspiration level already attained by one objective function as well as upon the probability of reaching the corresponding objective or called satisfied degree (or both), or/and the probability of satisfying the constraint or called feasible degree already attained by the constraint. Finally, the application of IRGSD-FD method in the resource allocation problem is discussed with a case study for project investment management.展开更多
In this paper, the matrix algebraic equations involved in the optimal control problem of time-invariant linear Ito stochastic systems, named Riccati- Ito equations in the paper, are investigated. The necessary and suf...In this paper, the matrix algebraic equations involved in the optimal control problem of time-invariant linear Ito stochastic systems, named Riccati- Ito equations in the paper, are investigated. The necessary and sufficient condition for the existence of positive definite solutions of the Riccati- Ito equations is obtained and an iterative solution to the Riccati- Ito equations is also given in the paper thus a complete solution to the basic problem of optimal control of time-invariant linear Ito stochastic systems is then obtained. An example is given at the end of the paper to illustrate the application of the result of the paper.展开更多
基金Supported by National Natural Science Foundation of P. R. China (60474049)
文摘An iterative learning control algorithm based on shifted Legendre orthogonal polynomials is proposed to address the terminal control problem of linear time-varying systems. First, the method parameterizes a linear time-varying system by using shifted Legendre polynomials approximation. Then, an approximated model for the linear time-varying system is deduced by employing the orthogonality relations and boundary values of shifted Legendre polynomials. Based on the model, the shifted Legendre polynomials coefficients of control function are iteratively adjusted by an optimal iterative learning law derived. The algorithm presented can avoid solving the state transfer matrix of linear time-varying systems. Simulation results illustrate the effectiveness of the proposed method.
文摘为设计高效稳定的演化算法,将方程求根的不动点迭代思想引入到优化领域,通过将演化算法的寻优过程看作为在迭代框架下方程不动点的逐步显示化过程,设计出一种基于数学模型的演化新算法,即不动点演化算法(fixed point evolution algorithm,FPEA).该算法的繁殖算子是由Aitken加速的不动点迭代模型导出的二次多项式,其整体框架继承传统演化算法(如差分演化算法)基于种群的迭代模式.试验结果表明:在基准函数集CEC2014、CEC2019上,本文算法的最优值平均排名在所有比较算法中排名第1;在4个工程约束设计问题上,FPEA与CSA、GPE等多个算法相比,能以较少的计算开销获得最高的求解精度.
基金theNational+4 种基金 Natural Science Foundation of China
文摘In the paper, the problem of H∞ decentralized state feedback control for largescale systems is described. An algorithm is proposed which uses the method of a feasible direction matrix. The algorithm only requires the solution of an algebraic Riccati equation (ARE) and makes the H∞norm of the closedloop transfer function matrix from disturbance inputs to controlled outputs less than a given constant which ensure the stability of the overall controlled system at each iteration. The given example shows that the convergence of the algorithm is satisfactory.
文摘This paper is devoted to study the multiobjective system programming under the assumption that some of the problem parameters are random variables. A method called Interactive Reference Goal Satisfied Degree and Feasible Degree (IRGSD-FD) is developed to solve stochastic multiobjective problems. It is an interactive method providing a so-called `dialogue' between the user and the model, the decision maker having the option conducting the search process for the (α, β)-efficient solutions by modifying the initial conditions according to the partial results obtained. During the iterations, the decision maker can improve upon the reference goal or called aspiration level already attained by one objective function as well as upon the probability of reaching the corresponding objective or called satisfied degree (or both), or/and the probability of satisfying the constraint or called feasible degree already attained by the constraint. Finally, the application of IRGSD-FD method in the resource allocation problem is discussed with a case study for project investment management.
文摘In this paper, the matrix algebraic equations involved in the optimal control problem of time-invariant linear Ito stochastic systems, named Riccati- Ito equations in the paper, are investigated. The necessary and sufficient condition for the existence of positive definite solutions of the Riccati- Ito equations is obtained and an iterative solution to the Riccati- Ito equations is also given in the paper thus a complete solution to the basic problem of optimal control of time-invariant linear Ito stochastic systems is then obtained. An example is given at the end of the paper to illustrate the application of the result of the paper.
