This paper reviews recent advances in the field of optimization under uncertainty via a
modern data lens, highlights key research challenges and promise of data-driven …

Model predictive control (MPC) has demonstrated exceptional success for the high-
performance control of complex systems. The conceptual simplicity of MPC as well as its …

摘要30 多年来, 模型预测控制(Model predictive control, MPC) 的理论和技术得到了长足的发展,
但面对经济社会迅速发展对约束优化控制提出的不断增长的要求, 现有的模型预测控制理论和 …

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This paper investigates the problem of distributed fusion over sensor networks with
probabilistic constraints and stochastic perturbations. In order to save the bandwidth …

In the past ten years many Stochastic Model Predictive Control (SMPC) algorithms have
been developed for systems subject to stochastic disturbances and model uncertainties …

The building sector is the largest energy consumer in the world. Therefore, it is
economically, socially, and environmentally significant to reduce the energy consumption of …

Many practical applications in control require that constraints on the inputs and states of the
system are respected, while some performance criterion is optimized. In the presence of …

Constraint tightening to non-conservatively guarantee recursive feasibility and stability in
Stochastic Model Predictive Control is addressed. Stability and feasibility requirements are …

Stochastic model predictive control (MPC) strategies can provide guarantees of stability and
constraint satisfaction, but their online computation can be formidable. This difficulty is …