A modular framework for distributed model predictive control of nonlinear continuous-time systems (GRAMPC-D)

Burk, Daniel; Völz, Andreas; Graichen, Knut

doi:10.1007/s11081-021-09605-3

Cited by 13 publications

(16 citation statements)

References 22 publications

(22 reference statements)

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“…As can be seen from the bottom plot, ADMM produces input signals which deviate less than 2•10 −2 m/s from the optimal solution once an initialization phase of a few seconds has passed. This confirms previous results in the sense that a low number of ADMM iterations per control step can suffice in practice to obtain a reasonable control performance (Van Parys and Pipeleers, 2017;Burk et al, 2021b;Stomberg et al, 2022a).…”

Section: Formation Control Via Linear-quadratic Mpc and Admmsupporting

confidence: 90%

“…The experiments demonstrate the effectiveness of the controller design and of our implementation and the results show that control sampling intervals in the millisecond range can be and Pipeleers, 2017), where subsystem computations are a clear bottleneck, as well as to (Burk et al, 2021b), where communication is holding up. Warm-starting the optimization methods in DMPC allows to find a reasonable control input with only few optimizer iterations per MPC step.…”

Section: Discussionmentioning

confidence: 88%

“…For optimizer iterates, however, lost messages are resent by the control application to ensure a synchronous execution of ADMM. This is necessary since we rely on UDP and not on the transmission control protocol as (Burk et al, 2021b).…”

Section: Dmpc Implementationmentioning

confidence: 99%

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Cooperative Distributed MPC via Decentralized Real-Time Optimization: Implementation Results for Robot Formations

Stomberg¹,

Ebel²,

Faulwasser³

et al. 2023

Preprint

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Distributed model predictive control (DMPC) is a flexible and scalable feedback control method applicable to a wide range of systems. While stability analysis of DMPC is quite well understood, there exist only limited implementation results for realistic applications involving distributed computation and networked communication. This article approaches formation control of mobile robots via a cooperative DMPC scheme. We discuss the implementation via decentralized optimization algorithms. To this end, we combine the alternating direction method of multipliers with decentralized sequential quadratic programming to solve the underlying optimal control problem in a decentralized fashion. Our approach only requires coupled subsystems to communicate and does not rely on a central coordinator. Our experimental results showcase the efficacy of DMPC for formation control and they demonstrate the real-time feasibility of the considered algorithms.

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Section: Formation Control Via Linear-quadratic Mpc and Admmsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 88%

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Cooperative Distributed MPC via Decentralized Real-Time Optimization: Implementation Results for Robot Formations

Stomberg¹,

Ebel²,

Faulwasser³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…While the other control response for the MPC control indicates a peak time of 28 s, rise time of 10 s, and settling time of 55.45 s, the response graph reveals these, respectively. However, the new model is based on heuristic algorithms that have a precise upper bound and lower bound, both of which contribute to the robust stability that was previously lacking in the use of basic models by early predictive controllers [32]. The control horizon (Ch) (as shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

Spider Monkey Metaheuristic Tuning of Model Predictive Control with Perched Landing Stabilities for Novel Auxetic Landing Foot in Drones

Jawahar,

S.N.

et al. 2024

ELEKTRON ELEKTROTECH

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The study focuses on improving drone landing gear dynamics through an innovative auxetic foot design, leveraging Spider Monkey Optimization for Model Predictive Control adjustment, facilitated by an Arduino-MATLAB interface. The auxetic foot design incorporates materials with a negative Poisson ratio, which allows the foot to expand and enhance energy absorption during landings. This design improves stability and safety during the perched landing process. The SMO-MPC approach is used to optimise the control of the perched landing gear. SMO, inspired by spider monkey search behaviour, optimises auxetic foot control input sequences with the limits of rotational displacement (theta = 30 deg to -30 deg) on the prediction horizon to improve landing gear performance. The real-time implementation of SMO-MPC is achieved through an Arduino-MATLAB interface on quadcopter drone. A comparative analysis is conducted to evaluate the benefits of SMO-MPC compared to conventional MPC methods. The results show that the SMO-MPC approach with auxetic foot design surpasses conventional MPC methods in terms of landing performance with 14.6 % improvement in damping force control and control of aerodynamic stability with pitch of 34.16 %, yaw of 16.87 %, and roll of 31.74 %.

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“…Finally, we highlight that the higher computational load required by the DNMPC does not preclude its real-time implementation. Indeed, there exists an extensive research line devoted to this crucial aspect and different effective solutions have been found (see, e.g., [51] and references therein). Tracking performances with the more classic distributed diffusive control in (18).…”

Section: Dnmpc Vs Pure Diffusive Controllermentioning

confidence: 99%

Distributed Nonlinear Model Predictive Control for Connected Autonomous Electric Vehicles Platoon with Distance-Dependent Air Drag Formulation

et al. 2021

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This paper addresses the leader tracking problem for a platoon of heterogeneous autonomous connected fully electric vehicles where the selection of the inter-vehicle distance between adjacent vehicles plays a crucial role in energy consumption reduction. In this framework, we focused on the design of a cooperative driving control strategy able to let electric vehicles move as a convoy while keeping a variable energy-oriented inter-vehicle distance between adjacent vehicles which, depending on the driving situation, was reduced as much as possible to guarantee air-drag reduction, energy saving and collision avoidance. To this aim, by exploiting a distance-dependent air drag coefficient formulation, we propose a novel distributed nonlinear model predictive control (DNMPC) where the cost function was designed to ensure leader tracking performances, as well as to optimise the inter-vehicle distance with the aim of reducing energy consumption. Extensive simulation analyses, involving a comparative analysis with respect to the classical constant time headway (CTH) spacing policy, were performed to confirm the capability of the DNMPC in guaranteeing energy saving.

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A modular framework for distributed model predictive control of nonlinear continuous-time systems (GRAMPC-D)

Cited by 13 publications

References 22 publications

Cooperative Distributed MPC via Decentralized Real-Time Optimization: Implementation Results for Robot Formations

Cooperative Distributed MPC via Decentralized Real-Time Optimization: Implementation Results for Robot Formations

Spider Monkey Metaheuristic Tuning of Model Predictive Control with Perched Landing Stabilities for Novel Auxetic Landing Foot in Drones

Distributed Nonlinear Model Predictive Control for Connected Autonomous Electric Vehicles Platoon with Distance-Dependent Air Drag Formulation

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