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The Koopman operator was recently shown to be a useful method for nonlinear system identification and controller design. However, the scalability of current datadriven approaches is limited by the selection of feature maps. In this paper, we present a new data-driven framework for learning feature maps of the Koopman operator by introducing a novel separation method. The approach provides a flexible interface between diverse machine learning algorithms and well-developed linear subspace identification methods, as well as demonstrating a connection between the Koopman operator and observability. The proposed data-driven approach is tested by learning stable nonlinear dynamics generating handwritten characters, as well as a bilinear DC motor model.

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In many applications, resource-aware devices are connected through a network, such as in the Internet of Things and energy hubs. These devices require proper coordination to achieve a high performance without violation of their resource limits. In this paper, we propose an asynchronous resourceaware multi-agent model predictive control to cooperatively coordinate agents to conduct a common task, whose resource concern is handled by a self-triggered control scheme. The consistency and recursive feasibility of the proposed MPC scheme are investigated. A reliable numerical implementation is introduced to deal with non-constant sampling times among agents, which is subsequently validated by a numerical example.