Model predictive dynamic control allocation with actuator dynamics

Abstract

A model predictive, dynamic control allocation algorithm is developed in this paper for the inner loop of a re-entry vehicle guidance and control system. The purpose of the control allocation portion of the guidance and control architecture is to distribute control power among redundant control effectors to meet the desired control objectives under a set of constraints. Most existing algorithms neglect the actuator dynamics or deal with the actuator dynamics separately, thereby assuming a static relationship between actuator outputs (in our case, control surface deflections) and plant inputs (i.e., moments about the three body axis). We propose a dynamic control allocation scheme based on model-based predictive control (MPC) that directly takes into account actuators with noneligible dynamics and hard constraints. Model-based predictive control schemes compute the control inputs by optimizing an open-loop control objective over a future time interval at each control step. In our setup, the model-predictive control allocation problem is posed as a sequential quadratic programming problem with dynamic constraints, which can be cast into a linear complementary problem (LCP) and therefore solved by linear programming approaches in a finite number of iterations. The time-varying affine internal model used in the MPC design enhances the ability of the control loop to deal with unmodeled system nonlinearities. The approach can be easily extended to encompass a variety of linear actuator dynamics without the need to redesign the overall scheme. Results are based on the model of an experimental reusable launch vehicle, and compared with that of existing static control allocation schemes.