Numerical Methods for Optimal Control Problems with State ConstraintsWhile optimality conditions for optimal control problems with state constraints have been extensively investigated in the literature the results pertaining to numerical methods are relatively scarce. This book fills the gap by providing a family of new methods. Among others, a novel convergence analysis of optimal control algorithms is introduced. The analysis refers to the topology of relaxed controls only to a limited degree and makes little use of Lagrange multipliers corresponding to state constraints. This approach enables the author to provide global convergence analysis of first order and superlinearly convergent second order methods. Further, the implementation aspects of the methods developed in the book are presented and discussed. The results concerning ordinary differential equations are then extended to control problems described by differential-algebraic equations in a comprehensive way for the first time in the literature. |
Contents
| 1 | |
Estimates on Solutions to Differential Equations and Their | 13 |
First Order Method | 27 |
Implementation | 55 |
Second Order Method 81 | 80 |
XII | 107 |
RungeKutta Based Procedure for Optimal Control of Dif | 129 |
A A Primal RangeSpace Method for PiecewiseLinear Quadra | 169 |
| 197 | |
List of Symbols 209 | 208 |
Other editions - View all
Numerical Methods for Optimal Control Problems with State Constraints Radoslaw Pytlak Limited preview - 1999 |
Numerical Methods for Optimal Control Problems with State Constraints Radoslaw Pytlak No preview available - 2014 |
Common terms and phrases
active set adjoint equations algorithm applied approximation Assume bounded brachistochrone problem calculated calculus of variations Chapter computational control functions convergence analysis corresponding defined described differential differential-algebraic equations direction finding subproblem discretization equality constraints evaluated Example exists FD Algorithm finite number Fo(u follows formula gradient algorithms Hessian Hessian matrix implementation inequality constraints integration procedure Jacobians Lagrange multipliers Lemma linear linearly independent LSSOL matrix nonlinear nonlinear programming number of constraints number of iterations objective function optimal control problems order method penalty parameter PLQP PNTSOL problem PN proof Proposition Re,u reduced gradients relaxed controls Runge-Kutta methods satisfies second order sequence solution solved SQP algorithm stationary point stepsize stiff equations superlinearly convergent system equations Theorem tions trajectory Uk+1 updated variables vectors windshear xu(t yu,d ακ นี้


