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The state diagram of the overall control flow of the controller is shown in Fig. 6, and the internal architecture is shown in Fig. 7.
Using computer simulations, the controller is shown to provide proactive control actions to control the vehicle's sideslip angle.
The nonlinear controller is shown to outperform linear control set-point tracking measured by integral absolute error (IAE).
The detailed control strategy block diagram of the PV-VSG controller is shown in Fig. 2.
A hybrid controller consisting of a saturation controller and a position-feedback controller is shown to be robust and efficient in controlling both transient and steady-state vibrations.
The block diagram of the position controller is shown in Figure 7. Figure 6 Overall control architecture of the ankle-foot mechanism.
The development of the fuzzy control and its comparison to a conventional PID (velocity form) controller is shown.
The resulting model predictive controller is shown to consist of a piecewise linear feedback control and can be tuned so that fast engagement, small friction losses and smooth lock-up are achieved.
This new neural network controller is shown to exhibit very good performance as shown by the control simulation of a nonlinear continuous stirred tank reactor and a pH neutralization process.
Based on Lyapunov Krasovskii theory, T S fuzzy model, and delayed feedback control scheme, the FDOFS controller is designed and an analytic expression of the controller is shown.
In each sub-region, one controller is shown to produce increased stability and/or performance over other possible decentralised control structures.
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CEO of Professional Science Editing for Scientists @ prosciediting.com