Guidance and control strategies for aerospace vehicles

A simplified method of matched asymptotic expansions was developed where the common part in composite solution is generated as a polynomial in stretched variable instead of actually evaluating the same from the outer solution. This methodology was applied to the solution of the exact equations for three dimensional atmospheric entry problems. Compared to previous works, the present simplified methodology yields explicit analytical expressions for various components of the composite solution without resorting to any type of transcendental equations to be solved only by numerical methods. The optimal control problem arising in the noncoplanar orbital transfer employing aeroassist was also addressed

Guidance and control strategies for aerospace vehicles

The optimal control problem arising in coplanar orbital transfer employing aeroassist technology and the fuel-optimal control problem arising in orbital transfer vehicles employing aeroassist technology are addressed

Fuel-optimal trajectories of aeroassisted orbital transfer with plane change

The problem of minimization of fuel consumption during the atmospheric portion of an aeroassisted, orbital transfer with plane change is addressed. The complete mission has required three characteristic velocities, a deorbit impulse at high earth orbit (HEO), a boost impulse at the atmospheric exit, and a reorbit impulse at low earth orbit (LEO). A performance index has been formulated as the sum of these three impulses. Application of optimal control principles has led to a nonlinear, two-point, boundary value problem which was solved by using a multiple shooting algorithm. The strategy for the atmospheric portion of the minimum-fuel transfer is to start initially with the maximum positive lift in order to recover from the downward plunge, and then to fly with a gradually decreasing lift such that the vehicle skips out of the atmosphere with a flight path angle near zero degrees

Necessary Conditions of Optimization for Partially Observed Controlled Diffusions

Necessary conditions are derived for stochastic partially observed control problems when the control enters the drift coefficient and correlation between signal and observation noise is allowed. The problem is formulated as one of complete information, but instead of considering directly the equation satisfied by the unnormalized conditional density of nonlinear filtering, measure-valued decompositions are used to decompose it into two processes. The minimum principle and the stochastic partial differential equation satisfied by the adjoint process are then derived, and the optimality conditions are shown to be the exact necessary conditions derived by Bensoussan [1, 2] when the correlation is zero. Key Words: Stochastic Control, Minimum Principle, Partially Observable Diffusions, Nonlinear Filtering, Measure-Valued Decompositions. AMS subject classification 93E20 1 Introduction The stochastic control problem under consideration is minfJ(u); u 2 U ad g; (1.1) J(u) = E u f Z T ..

Risk-Sensitive/Integral Control for Systems with Point Process Observations

This paper deals with necessary conditions for integral and exponential-of-integral cost functions, when the signal is a controlled diffusion process, and the observations consist of continuous and discontinuous processes. These problems are reformulated as infinite dimensional stochastic problems having full information, with state the Zakai equation for the integral cost, and the information state for the exponential-of-integral cost. The approach is the one considered in [1] for the case of integral cost with continuous observations. 1. Problem Formulation For control u taking values in a nonempty subset of R k , the diffusion process x(\Delta) under consideration satisfies the Ito equation dx t = f(x t ; u t )dt + oe(x t )dw t ; t 2 [0; T ] x(0) = x 0 (1) The observations are given by continuous and counting procesess y(\Delta) and N(\Delta), respectively dy t = h(x t )dt + db t ; dN t = t dt + dm t (2) Here, f : R n \Theta R k ! R n ; oe : R n ! L(R n ; R n ); ..