High-speed moving horizon estimation based on automatic code generation

Recent theoretical and algorithmic advances have led to efficient algorithms that allow for real-time optimisation of processes with fast nonlinear dynamics. This paper addresses the efficient implementation of algorithms for moving horizon estimation (MHE) for obtaining real-time estimates of process states or parameters that are not measured directly. To this end, we propose to combine the previously proposed concepts of real-time iteration schemes and automatic code generation to obtain highly efficient source code of MHE algorithms. This has led to major extensions of the ACADO Code Generation tool that automatically generates customised plain C code for both model predictive control (MPC) and MHE applications. As a proof of concept, we present numerical results of controlling a nonlinear ODE model by means of combined exported MHE and MPC algorithms in a closed-loop manner. These exported algorithms turn out to be significantly faster than their generically implemented counterparts.

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