A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a receding horizon implementation. Given a feasible solution to the finite-horizon optimal control problem at an initial time, resolvability implies the ability to solve the optimal control problem at subsequent times.
Originally developed for the control of spacecraft in the proximity of small celestial bodies, the algorithm can also be applied to other systems (such as industrial and automotive systems) for which robust feedback control may be required. The algorithm consists of a feedforward and a feedback component. The feedforward part is computed by the on-line solution of the finite-horizon optimal control problem with the nominal system dynamics, with a relaxation of the initial state constraint at each computation.
Originally developed for the control of spacecraft in the proximity of small celestial bodies, the algorithm can also be applied to other systems (such as industrial and automotive systems) for which robust feedback control may be required. The algorithm consists of a feedforward and a feedback component. The feedforward part is computed by the on-line solution of the finite-horizon optimal control problem with the nominal system dynamics, with a relaxation of the initial state constraint at each computation.
This explicit characterization of the robustness to the uncertainties (which can easily be extended to external disturbances) is particularly desirable in a real-time autonomous control application. Furthermore, the ability to solve for an open-loop trajectory during a maneuver enables model updates (possibly based on real-time information) into the control problem to reduce model uncertainty and improve optimality for the open-loop trajectory. The algorithm has been shown to be robustly stabilizing under state and control constraints with a region of attraction composed of initial states for which solution of the finite-horizon optimal control problem is feasible.
This work was done by A. Behçet Açkmeçe and John M. Carson III of Caltech for NASA's Jet Propulsion Laboratory. For more information, download the Technical Support Package (free white paper) at www.techbriefs.com/tsp under the Information Sciences category.
The software used in this innovation is available for commercial licensing. Please contact Karina Edmonds of the California Institute of Technology at (626) 395-2322. Refer to NPO-42754.
This Brief includes a Technical Support Package (TSP).
A Robustly Stabilizing Model Predictive Control Algorithm
(reference NPO-42754) is currently available for download from the TSP library.
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Overview
The document is a Technical Support Package from NASA's Jet Propulsion Laboratory (JPL) concerning a technology identified as "A Robustly Stabilizing Model Predictive Control Algorithm," designated with NTR Number 42754. This technology is part of NASA Tech Briefs, which aim to disseminate aerospace-related developments that have broader technological, scientific, or commercial applications.
The primary focus of the document is on a robust model predictive controller (MPC), which is a type of control algorithm used in various engineering applications, particularly in aerospace systems. Model predictive control is known for its ability to handle multi-variable control problems and constraints, making it suitable for complex systems where traditional control methods may struggle.
The document emphasizes the algorithm's robustness, which refers to its ability to maintain performance in the presence of uncertainties and disturbances in the system. This characteristic is crucial for aerospace applications, where conditions can be unpredictable and safety is paramount. The robust MPC aims to ensure stability and performance even when faced with variations in system dynamics or external influences.
Additionally, the Technical Support Package provides contact information for further inquiries, specifically directing interested parties to the Innovative Technology Assets Management at JPL. This section indicates that the technology is part of NASA's Commercial Technology Program, which seeks to promote the commercialization of innovative technologies developed through government research.
The document also includes a disclaimer stating that the U.S. Government and its representatives do not assume liability for the use of the information contained within, nor do they guarantee that such use will be free from privately owned rights. This is a standard precaution in technical documentation to clarify the limitations of liability and endorsement.
In summary, the Technical Support Package outlines a significant advancement in control algorithms through the robust model predictive controller, highlighting its potential applications in aerospace and other fields. It serves as a resource for those interested in the latest developments in control technology and offers a pathway for further engagement with NASA's innovative research initiatives.
