Adaptive Control for The Electrostatic Microactuators with Stochastic Disturbance

2019 ◽  
Author(s):  
Chao Dai ◽  
Xin Yu
Keyword(s):  
Adaptive Control ◽  
Stochastic Disturbance

Grinding Process Fuzzy Control on CNC Tool Grinder

2007 ◽  
Vol 359-360 ◽  
pp. 528-532 ◽  
Author(s):  
Jing Kang ◽  
Chang Jian Feng ◽  
Hong Ying Hu ◽  
Qiang Shao
Keyword(s):  
Adaptive Control ◽  
Adaptive Controller ◽  
Grinding Force ◽  
Force Characteristic ◽  
Grinding Process ◽  
Constant Force ◽  
Loop Control ◽  
Stochastic Disturbance ◽  
Evaluation Indexes ◽  
Loop Control System

In order to improve form cutter accuracy in grinding process, a closed-loop control system was designed to accomplish adaptive control of constant force in grinding process. Since it is a complicated dynamic process with severe nonlinear and much stochastic disturbance, fuzzy adaptive controller was used which can adjust parameters online. By measuring grinding force, characteristic information of grinding process was acquired. Regulation factor of feed rate is determined by grinding force ratio, and force deviation and its change rate are used as evaluation indexes. Thus, adaptive control of constant force in grinding process is accomplished. Simulation and tool grinding test indicate that the system has high precision and stability, and reduces cutter error efficiently.

The Effect of Computational Delay on Performance of Adaptive Control Systems

2006 ◽  
Author(s):  
Pawel Konrad Orzechowski ◽  
Tsu-Chin Tsao ◽  
James Steve Gibson
Keyword(s):  
Adaptive Control ◽  
Least Squares ◽  
Adaptive Filters ◽  
Adaptive Controller ◽  
Recursive Least Squares ◽  
Variable Order ◽  
Stochastic Disturbance ◽  
Disturbance Dynamics ◽  
Fractional Delay ◽  
Computationally Intensive

In many adaptive control applications, especially where the recursive-least-squares (RLS) algorithms are used, the real-time implementation of high order adaptive filters for estimating the disturbance dynamics is computationally intensive. The delay associated with the computational burden is usually either underestimated as no delay or overestimated as one sample delay in the control system design and analysis. For a stochastic disturbance dynamics, the H2 optimal control performance for the case of one-step delay is worse than that of no delay due to the nonminimum phase plant zero introduced by the delay. The optimal performance for a fractional delay is bounded between these two extremes. The paper investigates the effect of the fractional computational delay on a variable order adaptive controller based on a recursive least-squares adaptive lattice filter. The trade-off between the adaptive filter order and the computational delay is analyzed and evaluated by an example.

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