Predictive terminal guidance with tuning of prediction horizon & constrained control

Continuous time-predictive control approach is employed to formulate an output tracking nonlinear, optimal, terminal guidance law for re-entry vehicles. The notable features of this formulation are that the system equations are not linearised and the evaluation of the guidance equations does not need the information of vehicle parameters, such as drag and mass. The formulation allows to impose the physical constrains on the control inputs, i.e. on the demanded lateral accelerations through a saturation mapping and the controls are obtained using a fixed point iteration algorithm which converges typically in a few iterations. Further, a simple method of tuning the prediction horizon needed in the guidance equations is presented. Numerical simulations show that the guidance law achieves almost zero terminal errors in all states despite large errors in initial conditions

Predictive control-based optimal nonlinear reentry guidance law

This paper discusses a new nominal riding reentry guidance law design based on a nonlinear optimal predictive control approach. In this method, the error between the actual trajectory and the nominal trajectory is predicted, and a quadratic cost function of these predicted errors is minimised, resulting in an optimal feedback guidance law design. The guidance law thus obtained does not require linearisation of the equations of motion. A nominal trajectory is selected which satisfies the vehicle constraints and mission objectives. This nominal data is used with actual data to evaluate the guidance law. Numerical simulations have been carried out for a planar trajectory for a variety of initial condition errors and also for off nominal conditions and the results are presented