Practical Methods for Optimal Control and Estimation Using Nonlinear Programming: Second EditionThe book describes how sparse optimization methods can be combined with discretization techniques for differential-algebraic equations and used to solve optimal control and estimation problems. The interaction between optimization and integration is emphasized throughout the book. |
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Practical Methods for Optimal Control and Estimation Using Nonlinear ... John T. Betts Limited preview - 2010 |
Common terms and phrases
active set adjoint variables algebraic variables altitude angle approach approximation B-spline barrier behavior boundary conditions coefficients compute construct control variables convergence defined denoted derivative differential equations discretization error dynamics estimate example final finite difference formulation function evaluations given gradient grid points Hermite-Simpson Hessian matrix index sets indirect method inequality constraints initial conditions initial guess integration interpolation interval Jacobian Jacobian matrix Lagrange multipliers Lagrangian linear merit function mesh minimize multiple shooting Newton Newton's method NLP algorithm NLP problem NLP variables nonlinear Nonlinear Programming normal matrix objective function optimal control problem orbit parameter path constraint perturbation phase positive definite QP subproblem quadratic quasi-Newton refinement right-hand-side search direction shooting method SOCS solution solve sparsity Specifically step stepsize strategy summarizes swingby t₁ Table technique trajectory trapezoidal update vector velocity XX XX