A Modular Framework for Distributed Model Predictive Control of Nonlinear Continuous-Time Systems (GRAMPC-D)
The modular open-source framework GRAMPC-D for model predictive control of distributed systems is presented in this paper. The modular concept allows to solve optimal control problems (OCP) in a centralized and distributed fashion using the same problem description. It is tailored to computational efficiency with the focus on embedded hardware. The distributed solution is based on the Alternating Direction Method of Multipliers (ADMM) and uses the concept of neighbor approximation to enhance convergence speed. The presented framework can be accessed through Cpp and Python and also supports plug-and-play and data exchange between agents over a network.
Code (0)
등록된 구현이 없습니다.
Tasks
Computational EfficiencyModel Predictive ControlSimilar Papers 제목 키워드 기반
Distributed nonlinear model predictive control of an autonomous tractor-trailer system
This paper addresses the trajectory tracking problem of an autonomous tractor-trailer system by using a fast distributed nonlinear model predictive control algorithm in combination with nonlinear moving horizon estimatio…
Model Predictive Controlparameter estimationDistributed Predictive Control Barrier Functions: Towards Scalable Safety Certification in Modular Multi-Agent Systems
We consider safety-critical multi-agent systems with distributed control architectures and potentially varying network topologies. While learning-based distributed control enables scalability and high performance, a lack…
Adaptive Distributed Observer-based Model Predictive Control for Multi-agent Formation with Resilience to Communication Link Faults
In order to address the nonlinear multi-agent formation tracking control problem with input constraints and unknown communication faults, a novel adaptive distributed observer-based distributed model predictive control m…
Model Predictive ControlBenchmarking Model Predictive Control Algorithms in Building Optimization Testing Framework (BOPTEST)
We present a data-driven modeling and control framework for physics-based building emulators. Our approach consists of: (a) Offline training of differentiable surrogate models that accelerate model evaluations, provide c…
BenchmarkingModel Predictive ControlModelPredictiveControl.jl: advanced process control made easy in Julia
Proprietary closed-source software is still the norm in advanced process control. Transparency and reproducibility are key aspects of scientific research. Free and open-source toolkit can contribute to the development, s…