A sequential quadratic programming algorithm for discrete optimal control problems with control inequality constraints

2003 ◽  
Author(s):  
J.F.O. De O. Pantoja ◽  
D.Q. Mayne
Keyword(s):  
Optimal Control ◽  
Quadratic Programming ◽  
Sequential Quadratic Programming ◽  
Optimal Control Problems ◽  
Inequality Constraints ◽  
Programming Algorithm ◽  
Control Problems ◽  
Discrete Optimal Control ◽  
Sequential Quadratic Programming Algorithm

A Constraining Hyperplane Technique for State Variable Constrained Optimal Control Problems

1973 ◽  
Vol 95 (4) ◽  
pp. 380-389 ◽  
Author(s):  
K. Martensson
Keyword(s):  
Optimal Control ◽  
Optimal Control Problems ◽  
Control Variable ◽  
Dynamic Programming Algorithm ◽  
Inequality Constraints ◽  
Programming Algorithm ◽  
Control Problems ◽  
State Control ◽  
Constrained Optimal Control ◽  
State Variable

A new approach to the numerical solution of optimal control problems with state-variable inequality constraints is presented. It is shown that the concept of constraining hyperplanes may be used to approximate the original problem with a problem where the constraints are of a mixed state-control variable type. The efficiency and the accuracy of the combination of constraining hyperplanes and a second-order differential dynamic programming algorithm are investigated on problems of different complexity, and comparisons are made with the slack-variable and the penalty-function techniques.

scholarly journals An active set sequential quadratic programming algorithm for nonlinear optimisation

2006 ◽  
Vol 74 (1) ◽  
pp. 69-83
Author(s):  
Qing-Jie Hu ◽  
Yun-Hai Xiao ◽  
Y. Chen
Keyword(s):  
Quadratic Programming ◽  
Sequential Quadratic Programming ◽  
Optimization Problems ◽  
Inequality Constraints ◽  
Least Square ◽  
Programming Algorithm ◽  
Active Set ◽  
Nonlinear Inequality ◽  
Nonlinear Inequality Constraints ◽  
Sequential Quadratic Programming Algorithm

In this paper, we have proposed an active set feasible sequential quadratic programming algorithm for nonlinear inequality constraints optimization problems. At each iteration of the proposed algorithm, a feasible direction of descent is obtained by solving a reduced quadratic programming subproblem. To overcome the Maratos effect, a higher-order correction direction is obtained by solving a reduced least square problem. The algorithm is proved to be globally convergent and superlinearly convergent under some mild conditions without strict complementarity.

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