Online distributed optimization with strongly pseudoconvex-sum cost functions and coupled inequality constraints

Lu, Kaihong; Xu, Hang

doi:10.1016/j.automatica.2023.111203

Cited by 2 publications

(1 citation statement)

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“…[9] designs a discrete algorithm for multi-robot system to deal with the cooperative transportation by minimizing the total energy consumption. In [10], a class of online DOP considering coupled inequality constraints is investigated and an online primal-dual algorithm is developed. Noting that the above papers focus on the development of discrete-time algorithm for DOP, which means these algorithms are not suitable for the continuous-time dynamics.…”

Section: Introductionmentioning

confidence: 99%

Distributed Adaptive Optimization Algorithm for Fractional High-Order Multiagent Systems Based on Event-Triggered Strategy and Input Quantization

Yang,

Yuan,

Chen

et al. 2023

Fractal Fract

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This paper investigates the distributed optimization problem (DOP) for fractional high-order nonstrict-feedback multiagent systems (MASs) where each agent is multiple-input–multiple-output (MIMO) dynamic and contains uncertain dynamics. Based on the penalty-function method, the consensus constraint is eliminated and the global objective function is reconstructed. Different from the existing literatures, where the DOPs are addressed for linear MASs, this paper deals with the DOP through using radial basis function neural networks (RBFNNs) to approximate the unknown nonlinear functions for high-order MASs. To reduce transmitting and computational costs, event-triggered scheme and quantized control technology are combined to propose an adaptive backstepping neural network (NN) control protocol. By applying the Lyapunov stability theory, the optimal consensus error is proved to be bounded and all signals remain semi-global uniformly ultimately bounded. Simulation shows that all agents reach consensus and errors between agents’ outputs and the optimal solution is close to zero with low computational costs.

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Section: Introductionmentioning

confidence: 99%