A Comprehensive Optimization for the Trade-off of Energy Saving and System Performance in Controller Design

2016 ◽  
pp. 201-209
Author(s):  
Yijie Zhang ◽  
Min Zheng ◽  
Ke Zhang
Keyword(s):  
Energy Saving ◽  
System Performance ◽  
Controller Design ◽  
Trade Off

scholarly journals A Trade-off Between Energy Saving and Cycle Time Reduction by Pareto Optimal Corner Smoothing in Industrial Feed Drive Systems

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 23579-23594
Author(s):  
Enock William Nshama ◽  
Mathew Renny Msukwa ◽  
Naoki Uchiyama
Keyword(s):  
Energy Saving ◽  
Cycle Time ◽  
Pareto Optimal ◽  
Trade Off ◽  
Feed Drive ◽  
Cycle Time Reduction ◽  
Time Reduction ◽  
Drive Systems

scholarly journals LOW POWER SYSTEM DESIGN BY COMBINING SOFTWARE PREFETCHING AND DYNAMIC VOLTAGE SCALING

2007 ◽  
Vol 16 (05) ◽  
pp. 745-767
Author(s):  
SUMITKUMAR N. PAMNANI ◽  
DEEPAK N. AGARWAL ◽  
GANG QU ◽  
DONALD YEUNG
Keyword(s):  
Energy Consumption ◽  
Low Power ◽  
Energy Saving ◽  
System Performance ◽  
Performance Enhancement ◽  
Voltage Scaling ◽  
Dynamic Voltage Scaling ◽  
Dynamic Voltage ◽  
Software Prefetching ◽  
Online Profiling

Performance-enhancement techniques improve CPU speed at the cost of other valuable system resources such as power and energy. Software prefetching is one such technique, tolerating memory latency for high performance. In this article, we quantitatively study this technique's impact on system performance and power/energy consumption. First, we demonstrate that software prefetching achieves an average of 36% performance improvement with 8% additional energy consumption and 69% higher power consumption on six memory-intensive benchmarks. Then we combine software prefetching with a (unrealistic) static voltage scaling technique to show that this performance gain can be converted to an average of 48% energy saving. This suggests that it is promising to build low power systems with techniques traditionally known for performance enhancement. We thus propose a practical online profiling based dynamic voltage scaling (DVS) algorithm. The algorithm monitors system's performance and adapts the voltage level accordingly to save energy while maintaining the observed system performance. Our proposed online profiling DVS algorithm achieves 38% energy saving without any significant performance loss.

Computer-Aided Learning of Controller Design: Focus on Fuzzy Logic Control

2002 ◽  
Vol 39 (4) ◽  
pp. 358-370 ◽  
Author(s):  
C. H. Lo ◽  
Y. K. Wong ◽  
A. B. Rad
Keyword(s):  
Fuzzy Logic ◽  
System Performance ◽  
Fuzzy Logic Control ◽  
Controller Design ◽  
Logic Control ◽  
Simulated Environment ◽  
Process Plant ◽  
Computer Aided ◽  
On Line ◽  
Computer Aided Learning

A computer-aided controller design package is developed in this paper. The package provides a simulated environment for simulating the action of a controller under different parameter settings in order to achieve optimal system performance. The designed controller is then applied to a process plant for on-line control.

scholarly journals Novel m-PSO-SVM based Interface Controller Design for Haptic System

2020 ◽  
Vol 16 (2) ◽  
pp. 188-196
Author(s):  
Naveen Kumar ◽  
Jyoti Ohri
Keyword(s):  
Controller Design ◽  
Optimal Solution ◽  
Complex Problem ◽  
Haptic Interface ◽  
Hybrid Technique ◽  
Trade Off ◽  
Haptic System ◽  
Free Parameters ◽  
Modified Pso ◽  
Selection Of

The haptic system has two key performance issues: stability and transparency. A haptic interface controller (HIC) is designed to address these issues. Addressing these issues becomes a complex problem as both are complementary to each other. Here, when transparency of the system is increased, its stability degrades and vice-versa. To overcome this problem, intelligent optimized solutions are used in this paper to design a HIC controller for the haptic system. SVM and NN techniques have been employed to identify the performance of the controller, ensuring stability and transparency both. The disadvantages of NN in terms of the number of neurons and hidden layers are overcome by SVM. Further, the performance of SVM is highly dependent upon the selection of free parameters. So, further, a modified PSO technique is employed for the optimal selection of these parameters to enhance the performance of SVM. Hence, this novel proposed hybrid technique of m-PSO optimized SVM is applied for the optimal design of the HIC to find out an optimal solution between trade-off the transparency and stability of the haptic device simultaneously. To appreciate the efficacy of the proposed technique, the result obtained with this is compared with HIC design using neural network and conventional ZN method also. This designed controller ensures stability as well as transparency, even under the presence of uncertainty, delay, and quantization error.

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