Nitroaromatics are tremendously valuable organic compounds
with
a long history of being used as pharmaceuticals, agrochemicals, and
explosives as well as vital intermediates to a wide variety of chemicals.
Consequently, the exploration of aromatic nitration has become an
important endeavor in both academia and industry. Herein, we report
the identification of a powerful nitrating reagent, 5-methyl-1,3-dinitro-1
H
-pyrazole, from the
N
-nitro-type reagent
library constructed using a practical N–H nitration method.
This nitrating reagent behaves as a controllable source of the nitronium
ion, enabling mild and scalable nitration of a broad range of (hetero)arenes
with good functional group tolerance. Of note, our nitration method
could be controlled by manipulating the reaction conditions to furnish
mononitrated or dinitrated product selectively. The value of this
method in medicinal chemistry has been well established by its efficient
late-stage C–H nitration of complex biorelevant molecules.
Density functional theory (DFT) calculations and preliminary mechanistic
studies reveal that the powerfulness and versatility of this nitrating
reagent are due to the synergistic “nitro effect” and
“methyl effect”.
Aiming at the problems of control stability of the intelligent vehicle lateral control method, single test conditions, etc., a lateral control method with feedforward + predictive LQR is proposed, which can better adapt to the problem of intelligent vehicle lateral tracking control under complex working conditions. Firstly, the vehicle dynamics tracking error model is built by using the two degree of freedom vehicle dynamics model, then the feedforward controller, predictive controller and LQR controller are designed separately based on the path tracking error model, and the lateral control system is built. Secondly, based on the YOLO-v3 algorithm, the environment perception system under the urban roads is established, and the road information is collected, the path equation is fitted and sent to the control system. Finally, the joint simulation is carried out based on CarSim software and a Matlab/Simulink control model, and tested combined with hardware in the loop test platform. The results of simulation and hardware-in-loop test show that the transverse controller with feedforward + predictive LQR can effectively improve the accuracy of distance error control and course error control compared with the transverse controller with feedforward + LQR control, LQR controller and MPC controller on the premise that the vehicle can track the path in real time.
Since the zero initial conditions of the boost converter are far from the target equilibrium point, the overshoot of the input current and the output voltage will cause energy loss during the start-up process when the converter adopts the commonly used small-signal model design control method. This paper presents a sliding mode control strategy that combines two switching surfaces. One switching surface based on the large-signal model is employed for the start-up to minimize inrush current and voltage overshoot. The stability of this strategy is verified by Lyapunov theory and simulation. Once the converter reaches the steady-state, the other switching surface with PI compensation of voltage error is employed to improve the robustness. The latter switching surface, which is adopted to regulate the voltage, can not only suppress the perturbation of input voltage and load, but also achieve a better dynamic process and a zero steady-state error. Furthermore, the discrete sliding mode controller is implemented by digital signal processor (DSP). Finally, the results of simulation, experiment and theoretical analysis are consistent.
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