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A system model was created by performing system identification experiments, and a controller was designed by a frequency shaping method based on the H∞ control.
A multi-input, multi-output positive position feedback controller was designed by taking the natural vibration characteristics into account and was then implemented by using a digital controller.
A switching plasma shape multivariable controller was designed by the H∞ robust loop-shaping approach for magnetic field at X-point and poloidal fluxes on plasma separatrix (isoflux control).
The controller was designed by using neural networks (NN), which were trained with the optimal power flux distribution between a fuel cell system and a battery system that minimizes the overall equivalent energy consumption.
A shear-impact-type atomized dust controller was designed by combining the rotary spray blades and axial flow fan, and the droplet atomizing process included two steps: shear-atomizing and impact-atomizing.
Considered both the situation with unknown control function matrices and the situation with linear unmodeled input dynamics, adaptive neural robust controller was designed by using adaptive backstepping method for a class of multi-input to multi-output nonlinear systems which could be turned to "standard block control type".
Similar(53)
The system is represented through its port controlled Hamiltonian model, and the controller is designed by interconnection and damping assignment.
Moreover, an active controller is designed by the computed torque control method for trajectory tracking control.
The tracking controller is designed by modifying dynamic surface control, which replaces the traditional first-order filter with a high-gain tracking differentiator (HGTD).
Moreover, the decentralized coordinated controller is designed by using H∞ robust control method so as to deal with the uncertainties of system.
Then, the optimal controller is designed by using the classical optimal control theory.
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