| SEQUENTIAL QUADRATIC PROGRAMMING METHODS FOR OPTIMAL CONTROL PROBLEMS WITH STATE CONSTRAINTS |
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| 引用本文： | 徐成贤 JongdeJ.L.. SEQUENTIAL QUADRATIC PROGRAMMING METHODS FOR OPTIMAL CONTROL PROBLEMS WITH STATE CONSTRAINTS[J]. 高校应用数学学报(英文版), 1993, 8(2): 163-174. DOI: 10.1007/BF02662000 |
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| 作者姓名： | 徐成贤 JongdeJ.L. |
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| 作者单位： | [2]TechnologyUniversityofEindhoven,Holland [3]DepartmentofMathematicsXi＇anJiaotongUniversity,710049 |
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| 摘 要： | A Kind of direct methods is presented for the solution of optimal control problems with state constraints.These methods are sequential quadratic programming methods.At every iteration a quadratic programming which is obtained by quadratic approximation to Lagrangian function and Linear approximations to constraints is solved to get a search direction for a merit function.The merit function is formulated by augmenting the Lagrangian funetion with a penalty term.A line search is carried out along the search direction to determine a step length such that the merit function is decreased.The methods presented in this paper include continuous sequential quadratic programming methods and discreate sequential quadrade programming methods.
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| 关 键 词： | 最优控制 状态约束 二次程序序列 拉格朗日函数 |
| 收稿时间： | 1992-05-08 |
Sequential quadratic programming methods for optimal control problems with state constraints |
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| Xu Chengxian,J. L. de Jong. Sequential quadratic programming methods for optimal control problems with state constraints[J]. Applied Mathematics A Journal of Chinese Universities, 1993, 8(2): 163-174. DOI: 10.1007/BF02662000 |
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| Authors: | Xu Chengxian J. L. de Jong |
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| Affiliation: | (1) Department of Mathematics, Xi’an Jiaotong University, 710049 Xi’an, China;(2) Technology University of Eindhoven, Holland |
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| Abstract: | A kind of direct methods is presented for the solution of optimal control problems with state constraints. These methods are sequential quadratic programming methods. At every iteration a quadratic programming which is obtained by quadratic approximation to Lagrangian function and linear approximations to constraints is solved to get a search direction for a merit function. The merit function is formulated by augmenting the Lagrangian function with a penalty term. A line search is carried out along the search direction to determine a step length such that the merit function is decreased. The methods presented in this paper include continuous sequential quadratic programming methods and discreate sequential quadratic programming methods. |
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| Keywords: | Optimal Control Problems with State Constraints Sequential Quadratic Programming Lagrangian Function. Merit Function Line Search. |
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