The computation and real-time implementation of controls in nonlinear systems remains one of the great challenges for applying optimal control theory in demanding aerospace and industrial systems. Often, linearization around a set point is the only practical approach, and many controllers implemented in hardware systems are simple linear feedback mechanisms. From proportional guidance in missiles to PID controllers for UAV flight controls to linear integrators in optical tracking, linear controls dominate much of current implementation. Output feedback is of course one important consideration: optimal controls determined from Pontryagins principle are generally open-loop. Computation is a second difficulty: use of Pontryagins principle, dynamic programming, or direct optimization methods using conventional computational designs in high dimensional nonlinear systems has been considered largely unrealistic. In this Phase I effort, we will develop real-time control algorithms that integrate the optimality of pseudospectral methods with robust state estimation for real-time closed loop optimal control.
Benefit: Aimed primary at DoD applications, this work will provide the capability for real-time high performance control in demanding nonlinear dynamical systems such as UAV flight controls and missile guidance.
Keywords: model predictive control, model predictive control, pseudospectral control, Real-time Feedback