scholarly journals Zero Bias Simple Adaptive Control on Low-Loss Homopolar AMB Flywheel : A MIMO Control with PID Adaptive Identification(Mechanical Systems)

2009 ◽  
Vol 75 (757) ◽  
pp. 2507-2514 ◽  
Author(s):  
Budi RACHMANTO ◽  
Kenzo NONAMI
Keyword(s):  
Adaptive Control ◽  
Mechanical Systems ◽  
Low Loss ◽  
Zero Bias ◽  
Adaptive Identification ◽  
Mimo Control

Adaptive control of MIMO mechanical systems with unknown actuator nonlinearities based on the Nussbaum gain approach

2016 ◽  
Vol 3 (1) ◽  
pp. 26-34 ◽  
Author(s):  
Ci Chen ◽  
Zhi Liu ◽  
Yun Zhang ◽  
C. L. Philip Chen ◽  
Shengli Xie
Keyword(s):  
Adaptive Control ◽  
Mechanical Systems ◽  
Actuator Nonlinearities

Adaptive Identification and Control for Small Modular Reactors

2011 ◽  
Author(s):  
Daniel G. Cole
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Adaptive System ◽  
Operating Conditions ◽  
System Stability ◽  
Peak Performance ◽  
Model Matching ◽  
Adaptive Identification ◽  
Control Approach ◽  
And Control

This paper discusses adaptive identification and control (AID&C) techniques to enable automated online identification and control of SMRs. Adaptive system ID allows engineers to rapidly measure system dynamics, calibrate sensors channels, determine loop processes, and quantify actuator authority for the various reactor control loops. Adaptive control can automatically tune these loops and adjust plant processes to optimize conditions for peak performance and power production. Another advantage of the adaptive ID and control approach is that these tools can be used during reactor operation to monitor active and passive components. Adaptive system ID techniques are used to measure loop-transfer characteristics. Presented is a practical approach that uses adaptive model-matching tools to identify the coprime factors of the local loops. This has the advantage over model based approaches since coprime factors can be identified on the real system using real data. Adaptive control enables auto-tuning of controller parameters to meet operational specifications. Using the coprime factors, all controllers that stabilize the plant can be parametrized by a free Q-parameter that can be changed to meet control system objectives and improve performance, and the tuning is performed using adaptive techniques. The controller architecture presented provides several desirable and necessary features: e.g., a default fail-safe mode of operation, stability in the presence of communications failures, guaranteed stability, and robustness. An advantage of the adaptive structure presented here is that control system stability can be guaranteed, even during adaptation by ensuring certain norm conditions on the Q-parameter and estimated plant uncertainty. More importantly, the Q-parameter can be monitored during operation, providing a real-time estimate of the changes in the plant resulting from changes in the reactor itself. This signal monitors the dynamics of each loop, providing information about the reactor from the perspective of the control process. Online monitoring using AID&C can be used to better track control system transients that result in reactor trip, thus avoiding undesirable reactor trips and diversion events. And, there is a potential that the system can better adapt to changing operating conditions during plant transients including load following procedures.

Adaptive Control of Uncertain Coupled Mechanical Systems

1993 ◽  
Vol 26 (2) ◽  
pp. 571-574 ◽  
Author(s):  
J. Rodellar Benedé ◽  
E.P. Ryan
Keyword(s):  
Adaptive Control ◽  
Mechanical Systems
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