Iterative Learning Control With Predictive Trial Information: Convergence, Robustness, and Experimental Verification


Iterative learning management (ILC) is a management style method for prime-performance trajectory tracking. Most existing results achieve this by learning from data collected over the past executions of the task (named trials). This transient proposes a unique ILC design framework that updates the management input by learning not only from the past trials but additionally from the anticipated future trials using information of the plant model. It's shown that by together with information from the predicted future trials, the designed ILC controller is less short sighted, and therefore better performance will be achieved. Analysis of the algorithm’s properties reveals doubtless substantial profit in terms of convergence speed; the proposed algorithm also possesses distinct robustness features with respect to model uncertainty. Each numerical simulations and experimental results using a nonminimum section take a look at facility are provided to demonstrate the effectiveness of the proposed methodology.

Did you like this research project?

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

PROJECT TITLE : Noise-Robust Iterative Back-Projection ABSTRACT: As a result of denoising, noisy image super-resolution (SR) is a substantial challenge. There is no clean reference image for iterative back-projection (IBP), which
PROJECT TITLE : Estimation, Control and Prediction of Voltage Level and Stability at Receiving Node ABSTRACT: Receiver voltage stability is addressed in this article. Voltage stability and level are intertwined concepts. Although
PROJECT TITLE :Iterative Receivers for Downlink MIMO-SCMA: Message Passing and Distributed Cooperative Detection - 2018ABSTRACT:The fast development of mobile communications requires even higher spectral potency. Non-orthogonal
PROJECT TITLE :Diagnosing and Minimizing Semantic Drift in Iterative Bootstrapping Extraction - 2018ABSTRACT:Semantic drift is a common problem in iterative information extraction. Previous approaches for minimizing semantic drift
PROJECT TITLE :Iterative Block Tensor Singular Value Thresholding For Extraction Of Low Rank Component Of Image Data - 2017ABSTRACT:Tensor principal component analysis (TPCA) is a multi-linear extension of principal component

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

Project Enquiry