This paper proposes an ADMM-based multi-agent Bayesian optimization algorithm to learn the parameters of distributed model predictive controllers under model mismatch, and claims convergence and optimality guarantees.
Exact mul tiple-step predictions in gaussian process-based model predictive co ntrol: Ob- servations, possibilities, and challenges,
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Distributed Model Predictive Control Design for Multi-agent Systems via Bayesian Optimization
This paper proposes an ADMM-based multi-agent Bayesian optimization algorithm to learn the parameters of distributed model predictive controllers under model mismatch, and claims convergence and optimality guarantees.