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Quasi-Infinite Adaptive Horizon Nonlinear Model Predictive Control

Devin W. Griffith, Sachin C. Patwardhan, Lorenz T. Biegler

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Abstract

In this work we present a new method for calculating terminal conditions for nonlinear model predictive control (NMPC) that is non-conservative and scalable via the quasi-infinite horizon methodology. Then, we introduce adaptive-horizon NMPC, a new method for updating prediction horizon lengths online via nonlinear programming sensitivity calculations. Finally, we show how these methods work together to provide an adaptive horizon NMPC implementation for a quad-tank simulation example.

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What this paper is about

In this work we present a new method for calculating terminal conditions for nonlinear model predictive control (NMPC) that is non-conservative and scalable via the quasi-infinite horizon methodology. Then, we introduce adaptive-horizon NMPC, a new method for updating prediction horizon lengths online via nonlinear programming sensitivity calculations. Finally, we show how these methods work together to provide an adaptive horizon NMPC implementation for a quad-tank simulation example.

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Available abstract

In this work we present a new method for calculating terminal conditions for nonlinear model predictive control (NMPC) that is non-conservative and scalable via the quasi-infinite horizon methodology. Then, we introduce adaptive-horizon NMPC, a new method for updating prediction horizon lengths online via nonlinear programming sensitivity calculations. Finally, we show how these methods work together to provide an adaptive horizon NMPC implementation for a quad-tank simulation example.

Key concepts: Model predictive control, Horizon, Nonlinear system, Control theory (sociology), Computer science, Nonlinear model, Scalability, Time horizon

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