Control of Cutting Force for End Milling Processes Using an Extended Model Reference Adaptive Control Scheme

1996 ◽  
Vol 118 (3) ◽  
pp. 339-347 ◽  
Author(s):  
S. J. Rober ◽  
Y. C. Shin

In this work an extended Model Reference Adaptive Control (MRAC) technique is used to control the cutting force of an end milling process. The technique incorporates Zero Phase Error Tracking Control (ZPETC) into the MRAC system. The extended MRAC controller remains stable even in the presence of marginally stable and nonminimum phase process zeros. A modified recursive least-squares estimation algorithm is used for on-line parameter identification. Simulation results are presente to compare the extended MRAC controller to the standard MRAC controller. A microprocessor system is used to implement adaptive force control of a single-input single-output milling process where the microprocessor monitors the system cutting forces and controls the desired feedrate. A constant cutting force is maintained in the presence of time-varying plant gains and a high random component of the output force. Experimental results are presented for standard MRAC and extended MRAC controllers for comparison.

Author(s):  
Somasundar Kannan ◽  
Christophe Giraud-Audine ◽  
Etienne Patoor

In this paper, adaptive predictive control is proposed to control a Shape Memory Alloy (SMA) linear displacement actuator. A Black-Box (BB) model based on a Laguerre filter is used to identify the SMA actuator and the controller in closed loop. This identification is performed online using a recursive least squares (RLS) algorithm, thus providing a linear model which approximate the behaviour of whole system around a working point. Based on this model, a predictor is built and a simple control law is derived. This structure can be cast in the Model Reference Adaptive Control (MRAC) frame and understood as a modified Series-Parallel Model Reference Adaptive control (MRAC) scheme. Experimental results prove that the proposed method combined with an efficient online identification strategy is able to robustly handle both nonlinearities and input constraints.


AIChE Journal ◽  
1967 ◽  
Vol 13 (3) ◽  
pp. 485-491 ◽  
Author(s):  
Robert M. Casciano ◽  
H. Kenneth Staffin

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