Experimental studies in nonlinear discrete-time adaptive prediction and control

2006 ◽  
Vol 14 (2) ◽  
pp. 275-286 ◽  
Author(s):  
R. Ordonez ◽  
J.T. Spooner ◽  
K.M. Passino
Keyword(s):  
Discrete Time ◽  
Experimental Studies ◽  
Adaptive Prediction ◽  
Prediction And Control ◽  
And Control

Discrete-Time H2-Optimal Control for Hydraulic Position Control Systems

2007 ◽  
Author(s):  
Yang Lin ◽  
Yang Shi ◽  
Richard Burton
Keyword(s):  
Optimal Control ◽  
Control Systems ◽  
Discrete Time ◽  
Ad Hoc ◽  
Position Control ◽  
Experimental Studies ◽  
Design Procedure ◽  
Design Parameters ◽  
Practical Applications ◽  
And Control

Hydraulic position control systems play an important role in industrial automation. This paper explores the application of discrete-time H2-optimal control for a hydraulic position control system (HPCS). By minimizing the H2-norm of the system, the discrete-time robust H2-optimal control both stabilizes the plant and minimizes the root-mean-square of the servo position error simultaneously. The intuitive nature of this advanced approach helps to manage the selection of design parameters, whereas, classical methods provide less insight into strategies for parameter selection and control design. Additionally, the powerful ability to address disturbances and uncertainty in the robust H2-optimal design offers a more direct alternative to the ad hoc and iterative nature of classical methods for the hydraulic servo position system. Computer simulations illustrate the design procedure and the effectiveness of the proposed method. Experimental studies which employ the H2-optimal control on a hydraulic positioning system are also conducted and the results show that the method is suitable for practical applications.

Robust Adaptive Prediction and Control by Musmar Techniques

1982 ◽  
Vol 15 (4) ◽  
pp. 795-800
Author(s):  
E. Mosca ◽  
G. Zappa ◽  
C. Manfredi
Keyword(s):  
Adaptive Prediction ◽  
Prediction And Control ◽  
Robust Adaptive ◽  
And Control

An insight into Case Management and Challenges in Veteran Suicide Prevention

2020 ◽  
pp. 14-25
Author(s):  
Tamara Green
Keyword(s):  
Case Management ◽  
Suicide Prevention ◽  
Suicide Risk ◽  
Digital Transformation ◽  
Prevention Science ◽  
Historical Records ◽  
Military Members ◽  
Prediction And Control ◽  
And Control ◽  
Insight Into

Much of the literature, policies, programs, and investment has been made on mental health, case management, and suicide prevention of veterans. The Australian “veteran community is facing a suicide epidemic for the reasons that are extremely complex and beyond the scope of those currently dealing with them.” (Menz, D: 2019). Only limited work has considered the digital transformation of loosely and manual-based historical records and no enablement of Artificial Intelligence (A.I) and machine learning to suicide risk prediction and control for serving military members and veterans to date. This paper presents issues and challenges in suicide prevention and management of veterans, from the standing of policymakers to stakeholders, campaigners of veteran suicide prevention, science and big data, and an opportunity for the digital transformation of case management.

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