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To overcome the difficulties of time-varying disturbance, model mismatch, and frequent operation in the rudder/fin joint control system, an interference model predictive control (I-MPC) rudder/fin joint control system with sliding mode observer is proposed. Considering that the model mismatch problem occurs when the ship is sailing, the model mismatch and external disturbance are regarded as the total disturbance. A discrete 3-degree-of-freedom ship disturbance mathematical model is established. The rudder angle and fin angle are selected as the system inputs, then a sliding mode observer is designed to observe the time-varying disturbance and system output in real time. Different from traditional MPC and feedforward compensation, I-MPC will predict the output based on the disturbance observation value, and the control law is solved under rudder/fin angle and angular velocity constraints. Simulation results show that the proposed method improves the tracking performance and anti-disturbance performance of the rudder/fin system. The observer has high observation accuracy for constant, sinusoidal, and time-varying disturbances. Mechanism wear and energy loss caused by frequent operation are avoided.

 

Atmospheric chemical species play critical roles in ecosystem functioning and climate, but spatially resolving near‐surface concentrations has been challenging. In this regard, hovering unmanned aerial vehicles (UAVs) represent an emerging technology. The study herein provides guidance for optimized atmospheric sampling by hovering copter‐type UAVs. Large‐eddy simulations are conducted for species having chemical lifetimes ranging from reactive (i.e., 10²s) to long‐lived (i.e., 10⁸s). The case study of fair‐weather conditions over an equatorial tropical forest is used because of previous UAV deployments in this region. A framework is developed of influence length and horizontal shift of upwind surface emissions. The framework quantifies the length scale of the contribution of upwind forest emissions to species concentrations sampled by the downwind hovering UAV. Main findings include the following: (1) sampling within an altitude that is no more than 200 m above the canopy is recommended for both high‐ and intermediate‐reactivity species because of the strong decrease in species concentration even in a highly turbulent atmosphere; (2) sampling durations of at least 5 and 10 min are recommended for intermediate‐ and high‐reactivity species, respectively, because of the effects of atmospheric turbulence; and (3) in the case of heterogeneity of emissions across the underlying landscape, maximum recommended altitudes are presented for horizontal sampling strategies that can resolve the variability in the landscape emissions. The coupled effects of emission rate, wind speed, species lifetime, turbulence, and UAV sampling duration on influence length must all be considered for optimized and representative sampling over forests.

 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.