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Model predictive control (MPC) has drawn a considerable amount of attention in automotive applications during the last decade, partially due to its systematic capacity of treating system constraints. Even though having received broad acknowledgements, there still exist two intrinsic shortcomings on this optimization-based control strategy, namely the extensive online calculation burden and the complex tuning process, which hinder MPC from being applied to a wider extent. To tackle these two drawbacks, different methods were proposed. Nevertheless, the majority of these approaches treat these two issues independently. However, parameter tuning in fact has double-sided effects on both the controller performance and the real-time computational burden. Due to the lack of theoretical tools for globally analyzing the complex conflicts among MPC parameter tuning, controller performance optimization, and computational burden easement, a look-up table-based online parameter selection method is proposed in this paper to help a vehicle track its reference path under both the stability and computational capacity constraints. matlab-carsim conjoint simulations show the effectiveness of the proposed strategy. 

DNA–nanoparticle conjugates have found widespread use in sensing, imaging, and as components of devices. However, their synthesis remains relatively complicated and empirically based, often requiring specialized protocols for conjugates of different size, valence, and elemental composition. Here we report a novel, bottom-up approach for the synthesis of DNA–nanoparticle conjugates, based on ring-opening metathesis polymerization (ROMP), intramolecular crosslinking, and template synthesis. Using size, valence, and elemental composition as three independent synthetic parameters, various conjugates can be obtained using a facile and universal procedure. Examples are given to show the usefulness of these conjugates as sensing probes, building blocks for self-assembly, and as model particles for structure–property relationship studies.