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The IMEP estimation algorithm and torque balance controller were embedded and validated in a real-time engine management system.
By comparing the simulation results of two learning stabilizers and the model-based balance controller, it is demonstrated that the designed stabilizer can achieve approximate success rate of the ideal model-based balance controller and exert all the driving ability of the robot under the large disturbance condition of ±30° inclination of the platform.
The combination of optimality, passivity and detectability notions leads to an estimator-based material balance controller that, for an ample range of load compositions maximizes the profit and yields the prespecified product purity.
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Training samples are extracted from the state transition of the stability training process using balance controllers based on the robot dynamic model.
Based on this model, a balancing controller is designed using robust predictive control techniques.
In this work, we show how a congested communication network can have a dramatic impact on the control performance of such a power balancing controller.
We will present a swing up controller as well as a nonlinear balancing controller with the focus on the implementation on a laboratory model.
Invariant set theory is used to accurately characterise the region of the state space in which the balancing controller is effective.
Moreover, the presentation of the fuzzy balancing controller is not considerably affected by changes in the environmental parameters, which demonstrates the effectiveness of the fuzzy controller in minimizing the seesaw tilt angle in the time domain, although the system is caused by unpredicted loading variation.
The key idea of our method is to control robots with a hybrid controller that combines the functions of a communication behavior controller and body balancing controllers.
Invariant sets calculated for the balancing controller can be exploited to systematically determine the switching condition between swing up and balancing controllers.
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