Regularized - and DHOBE: An Adaptive Feedforward for a Solenoid Valve


To permit a stable and fast acting hydraulic pressure control on a continuously variable transmission (CVT) for road vehicles, an adaptive feedforward strategy is used. The Dasgupta-Huang outer bounding ellipsoid (DHOBE) and recursive least squares (RLS) with exponential forgetting factor$(rm RLS$-$lambda)$ adaptation algorithms are compared to the non-adaptive feedforward. The experiments show a transparent advantage for the adaptive over the non-adaptive version by compensating for the slow drift of the valve pressure gain during the warm-up amount of the transmission. As a result of of highly correlated input data, the difference algorithms supply deceiving performances with oscillating identified parameters. A regularization procedure is added to each adaptation algorithms, giving the $rrm RLS$-$lambda$ and $rDHOBE$. The regularized algorithms supply considerably better performances and stability than their non-regularized counterparts. As a result of of its implicit parametric uncertainty calculation whereas keeping an equivalent convergence rate, and a lower range of updates, the rDHOBE algorithm is thought to be the best answer for the application. By adapting a easy linear model, the rDHOBE adaptive feedforward succeeds in responding to an abrupt modification of the external pressure setpoint with no added actuation delay whereas keeping the pressure error underneath zero.5 bar.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE :Large-Dimensional Behavior of Regularized Maronna's M-Estimators of Covariance Matrices - 2018ABSTRACT:Robust estimators of large covariance matrices are thought-about, comprising regularized (linear shrinkage)
PROJECT TITLE :Reversion Correction and Regularized Random Walk Ranking for Saliency Detection - 2018ABSTRACT:In recent saliency detection research, several graph-based mostly algorithms have applied boundary priors as background
PROJECT TITLE :Ber Analysis Of Regularized Least Squares For Bpsk Recovery - 2017ABSTRACT:This paper investigates the matter of recovering an n-dimensional BPSK signal x0 ? -1, 1n from m-dimensional measurement vector y = Ax+z,
PROJECT TITLE :Regularized Kernel Least Mean Square Algorithm with Multiple-delay FeedbackABSTRACT:In the design of adaptive filters, feedback will be used to improve the convergence rate and filtering accuracy. This letter introduces
PROJECT TITLE :Iterative Shrinkage Algorithm for Patch-Smoothness Regularized Medical Image RecoveryABSTRACT:We have a tendency to introduce a quick iterative shrinkage algorithm for patch-smoothness regularization of inverse

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry