scholarly journals Computationally Efficient Distributed Predictive Controller for Cascaded Multilevel Impedance Source Inverter With LVRT Capability

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 35731-35742 ◽  
Author(s):  
Mitchell Easley ◽  
Sarthak Jain ◽  
Mohammad B. Shadmand ◽  
Haitham Abu-Rub
Keyword(s):  
Computationally Efficient ◽  
Predictive Controller

A Computationally Efficient Predictive Controller for Lane Keeping of Semi-Autonomous Vehicles

2014 ◽  
Author(s):  
Changchun Liu ◽  
Chankyu Lee ◽  
Andreas Hansen ◽  
J. Karl Hedrick ◽  
Jieyun Ding
Keyword(s):  
Autonomous Vehicles ◽  
Piecewise Linear ◽  
Computational Cost ◽  
Parametric Programming ◽  
Linear Feedback ◽  
Loop Model ◽  
Computationally Efficient ◽  
Feedback Function ◽  
Predictive Controller ◽  
Lane Keeping

Model predictive control (MPC) is a popular technique for the development of active safety systems. However, its high computational cost prevents it from being implemented on lower-cost hardware. This paper presents a computationally efficient predictive controller for lane keeping assistance systems. The controller shares control with the driver, and applies a correction steering when there is a potential lane departure. Using the explicit feedback MPC, a multi-parametric nonlinear programming problem with a human-in-the-loop model and safety constraints is formulated. The cost function is chosen as the difference between the linear state feedback function to be determined and the resultant optimal control sequence of the MPC problem solved off-line given the current state. The piecewise linear feedback function is obtained by solving the parametric programming with an approximation approach. The effectiveness of the controller is evaluated through numerical simulations.

Computationally Efficient Urea-Dosing Controllers for Urea-SCR

2020 ◽  
Author(s):  
Rajakumar Ganne ◽  
Kaushal K. Jain ◽  
Peter H. Meckl ◽  
Harshil Angre ◽  
Jagdish R. Hiremath
Keyword(s):  
Computational Cost ◽  
Computationally Efficient ◽  
Trade Off ◽  
Model Based ◽  
Control Objective ◽  
Predictive Controller ◽  
Simulation Results ◽  
Scr System

Abstract This paper presents two non-model-based reference-shaping and a model-based predictive urea-dosing controller for the Urea-SCR system. An ideal urea-dosing controller would minimize both tailpipe NOx and NH3 slip. However, this is not possible because of the trade-off between deNOx and NH3 slip. This trade-off is used to clearly define a control objective in terms of NH3 slip. Three controllers are then developed to meet this control objective such that they are all computationally inexpensive. The three controllers are then tested for three very different drive cycles. Simulation results show that the performance of the non-model based reference-shaping controllers is subjected to manual tuning of their variables. In contrast, the predictive controller, which is the highlight of this paper, can adapt to various drive cycles without compromising on the computational cost.

Computationally efficient model of the implanted knee for time-sensitive applications

2020 ◽  
Author(s):  
E Bori ◽  
A Navacchia ◽  
L Wang ◽  
L Duxbury ◽  
S McGuan ◽  
...  
Keyword(s):  
Computationally Efficient

Cloud-Based Multimedia Content Protection System

2020 ◽  
pp. 164-169
Author(s):  
B. Aparna ◽  
S. Madhavi ◽  
G. Mounika ◽  
P. Avinash ◽  
S. Chakravarthi
Keyword(s):  
Large Scale ◽  
Protection System ◽  
Multimedia Content ◽  
Computationally Efficient ◽  
Content Protection ◽  
Protection Systems ◽  
Multimedia Content Protection ◽  
High Scalability ◽  
Cloud Infrastructures ◽  
Rapid Deployment

We propose a new design for large-scale multimedia content protection systems. Our design leverages cloud infrastructures to provide cost efficiency, rapid deployment, scalability, and elasticity to accommodate varying workloads. The proposed system can be used to protect different multimedia content types, including videos, images, audio clips, songs, and music clips. The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of videos, and (ii) distributed matching engine for multimedia objects. The signature method creates robust and representative signatures of videos that capture the depth signals in these videos and it is computationally efficient to compute and compare as well as it requires small storage. The distributed matching engine achieves high scalability and it is designed to support different multimedia objects. We implemented the proposed system and deployed it on two clouds: Amazon cloud and our private cloud. Our experiments with more than 11,000 videos and 1 million images show the high accuracy and scalability of the proposed system. In addition, we compared our system to the protection system used by YouTube and our results show that the YouTube protection system fails to detect most copies of videos, while our system detects more than 98% of them.

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