Search results for "Velocity control"
showing 3 items of 3 documents
Large eddy simulation of inertial particles dispersion in a turbulent gas-particle channel flow bounded by rough walls
2020
The purpose of this paper is to understand the capability and consistency of large eddy simulation (LES) in Eulerian–Lagrangian studies aimed at predicting inertial particle dispersion in turbulent wall-bounded flows, in the absence of ad hoc closure models in the Lagrangian equations of particle motion. The degree of improvement granted by LES models is object of debate, in terms of both accurate prediction of particle accumulation and local particle segregation; therefore, we assessed the accuracy in the prediction of the particle velocity statistics by comparison against direct numerical simulation (DNS) of a finer computational mesh, under both one-way and two-way coupling regimes. We p…
Predictive Intelligent Fuzzy Control for Cooperative Motion of Two Nonholonomic Wheeled Cars
2007
In this paper a problem of intelligent cooperative motion control of two wheeled nonholonomic cars (target and follower) is considered. Once a target car converges to a fixed state (position and orientation), a follower car coming from different position and orientation, converges to the state above, without excessive delay between the known arrival time of the target car and the arrival time of the follower. In this sense we present a new predictive fuzzy control system. A Kalman's filter and an odometric model are used to predict the future position and orientation of the target car. The prediction above is employed to plane a circular nonholonomic reference motion for the follower car. A…
Optimization of Delayed-State Kalman-Filter-based Algorithm via Differential Evolution for Sensorless Control of Induction Motors
2010
This paper proposes the employment of the differential evolution (DE) to offline optimize the covariance matrices of a new reduced delayed-state Kalman-filter (DSKF)-based algorithm which estimates the stator-flux linkage components, in the stationary reference frame, to realize sensorless control of induction motors (IMs). The DSKF-based algorithm uses the derivatives of the stator-flux components as mathematical model and the stator-voltage equations as observation model so that only a vector of four variables has to be offline optimized. Numerical results, carried out using a low-speed training test, show that the proposed DE-based approach is very promising and clearly outperforms a cla…