Cooperative distributed MPC via decentralized real-time optimization: Implementation results for robot formations

Stomberg, Gösta; Ebel, Henrik; Faulwasser, Timm; Eberhard, Peter

doi:10.1016/j.conengprac.2023.105579

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…, this process results in the same expression as (6). Next, we examine (23) and substitute the previous result obtained.…”

Section: Proof Of Theoremmentioning

confidence: 94%

“…The resulting relative control inputs associated with each edge can then be mapped back to the physical control inputs in a distributed manner by employing a distributed steepest descent iteration between agents over two sampling instants. Such intersample iteration is frequently used in distributed MPC [6]. However, unlike in distributed MPC, the approach proposed in [5] does not require solving an optimization problem at each iteration step but only involves performing a matrix-vector multiplication.…”

Section: Introductionmentioning

confidence: 99%

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A Distributed Linear-Quadratic Discrete-Time Game Approach to Multi-Agent Consensus

Aditya

Werner

2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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Formation control problems can be expressed as linear quadratic discrete-time games (LQDTG) for which Nash equilibrium solutions are sought. However, solving such problems requires solving coupled Riccati equations, which cannot be done in a distributed manner. A recent study showed that a distributed implementation is possible for a consensus problem when fictitious agents are associated with edges in the network graph rather than nodes. This paper proposes an extension of this approach to formation control with collision avoidance, where collision is precluded by including appropriate penalty terms on the edges. To address the problem, a statedependent Riccati equation needs to be solved since the collision avoidance term in the cost function leads to a state-dependent weight matrix. This solution provides relative control inputs associated with the edges of the network graph. These relative inputs then need to be mapped to the physical control inputs applied at the nodes; this can be done in a distributed manner by iterating over a gradient descent search between neighbors in each sampling interval. Unlike inter-sample iteration frequently used in distributed MPC, only a matrix-vector multiplication is needed for each iteration step here, instead of an optimization problem to be solved. This approach can be implemented in a receding horizon manner, this is demonstrated through a numerical example.

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“…, this process results in the same expression as (6). Next, we examine (23) and substitute the previous result obtained.…”

Section: Proof Of Theoremmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

A Distributed Linear-Quadratic Discrete-Time Game Approach to Multi-Agent Consensus

Aditya

Werner

2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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Iterative distributed model predictive control for heterogeneous systems with non-convex coupled constraints

Wu,

Dai,

Xia

2024

Automatica

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A direct yaw moment control frame through model predictive control considering vehicle trajectory tracking performance and handling stability for autonomous driving

Jin,

Zhou,

Xie

et al. 2024

Control Engineering Practice

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Cooperative distributed MPC via decentralized real-time optimization: Implementation results for robot formations

Cited by 7 publications

References 29 publications

A Distributed Linear-Quadratic Discrete-Time Game Approach to Multi-Agent Consensus

A Distributed Linear-Quadratic Discrete-Time Game Approach to Multi-Agent Consensus

Iterative distributed model predictive control for heterogeneous systems with non-convex coupled constraints

A direct yaw moment control frame through model predictive control considering vehicle trajectory tracking performance and handling stability for autonomous driving

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