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Implementing linear model predictive controllers in embedded systems with limited computational resources is still challenging. Recently, several code generation tools have been developed that produce highly efficient library-free optimization algorithms. We present a tool that focuses on controller performance and hardware with low computational resources. The underlying optimization algorithm has been explicitly developed for real-time embedded applications, and is based on an augmented Lagrangian method together with Nesterov's gradient method. The tool provides offline methods that allow the generation of online controllers that have low computational requirements and quickly reach good performance. We demonstrate the capabilities of the software, and the performance of the generated controllers with two examples.
Zometa et al. (Sat,) studied this question.