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GRAMPC

swMATH ID: 30732
Software Authors: Englert, Tobias; Völz, Andreas; Mesmer, Felix; Rhein, Sönke; Graichen, Knut
Description: A software framework for embedded nonlinear model predictive control using a gradient-based augmented Lagrangian approach (GRAMPC). A nonlinear MPC framework is presented that is suitable for dynamical systems with sampling times in the (sub)millisecond range and that allows for an efficient implementation on embedded hardware. The algorithm is based on an augmented Lagrangian formulation with a tailored gradient method for the inner minimization problem. The algorithm is implemented in the software framework GRAMPC and is a fundamental revision of an earlier version. Detailed performance results are presented for a test set of benchmark problems and in comparison to other nonlinear MPC packages. In addition, runtime results and memory requirements for GRAMPC on ECU level demonstrate its applicability on embedded hardware.
Homepage: https://sourceforge.net/projects/grampc/
Keywords: nonlinear model predictive control; moving horizon estimation; augmented Lagrangian method; gradient method; embedded optimization; real-time implementation
Related Software: VIATOC; ACADO; qpOASES; GitHub; acados; FORCES NLP; qpDUNES; fast_mpc; FiOrdOs; MATMPC; HPIPM; OSQP; BLASFEO; DuQuad; CVXGEN; Ipopt; OOQP; ALADIN; DISROPT; pybind11
Cited in: 8 Documents

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