Authors:
Florian Weissel
;
Marco F. Huber
and
Uwe D. Hanebeck
Affiliation:
Intelligent Sensor-Actuator-Systems Laboratory, Universität Karlsruhe (TH), Germany
Keyword(s):
Nonlinear Model Predictive Control; Stochastic Systems; Nonlinear Estimation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Information-Based Models for Control
;
Intelligent Control Systems and Optimization
;
Nonlinear Signals and Systems
;
Planning and Scheduling
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
Simulation and Modeling
;
Symbolic Systems
;
Time Series and System Modeling
;
Vehicle Control Applications
Abstract:
In this paper, a framework for Nonlinear Model Predictive Control (NMPC) that explicitly incorporates the noise influence on systems with continuous state spaces is introduced. By the incorporation of noise, which results from uncertainties during model identification and the measurement process, the quality of control can be significantly increased. Since NMPC requires the prediction of system states over a certain horizon, an efficient state prediction technique for nonlinear noise-affected systems is required. This is achieved by using transition densities approximated by axis-aligned Gaussian mixtures together with methods to reduce the computational burden. A versatile cost function representation also employing Gaussian mixtures provides an increased freedom of modeling. Combining the prediction technique with this value function representation allows closed-form calculation of the necessary optimization problems arising from NMPC. The capabilities of the framework and especial
ly the benefits that can be gained by considering the noise in the controller are illustrated by the example of a mobile robot following a given path.
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