A self-tuning control method for Wiener nonlinear systems and its application to process control problems

2017 ◽  
Vol 25 (2) ◽  
pp. 193-201 ◽  
Author(s):  
Ping Yuan ◽  
Bi Zhang ◽  
Zhizhong Mao
Keyword(s):  
Nonlinear Systems ◽  
Process Control ◽  
Control Method ◽  
Control Problems ◽  
Self Tuning

scholarly journals Neuro-PID Control for Electric Vehicle

2011 ◽  
Vol 15 (7) ◽  
pp. 846-853 ◽  
Author(s):  
Shigeru Omatu ◽  
◽  
Michifumi Yoshioka ◽  
Toru Fujinaka ◽  
◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Pid Control ◽  
Control Method ◽  
Torque Control ◽  
Learning Ability ◽  
Control Problems ◽  
Process Industries ◽  
The Neural Network ◽  
Control Scheme ◽  
Self Tuning

In this paper we consider the neuro-control method and its application to control problems of an electric vehicle. The neuro-control methods adopted here is based on Proportional-plus-Integral-plus-Derivative (PID) control, which has been adopted to solve process control or intelligent control problems. In Japan about eighty four percent of the process industries have used the PID control. After deriving the self-tuning PID control scheme (neuro-PID) using the learning ability of the neural network, we will show the control results by using the speed and torque control of an electric vehicle.

scholarly journals Estimating the Time to Benefit for Preventive Drugs with the Statistical Process Control Method: An Example with Alendronate

Drugs & Aging ◽  
2016 ◽  
Vol 33 (5) ◽  
pp. 347-353 ◽  
Author(s):  
Esther M. M. van de Glind ◽  
Hanna C. Willems ◽  
Saeid Eslami ◽  
Ameen Abu-Hanna ◽  
Willem F. Lems ◽  
...  
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Method ◽  
Statistical Process

scholarly journals The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Xiaoyan Qin
Keyword(s):  
Nonlinear Systems ◽  
Neural Control ◽  
Closed Loop ◽  
Control Method ◽  
High Order ◽  
Stochastic Nonlinear Systems ◽  
Closed Loop System ◽  
Adaptive Neural Control ◽  
Control Scheme ◽  
Approximation Capability

This paper studies the problem of the adaptive neural control for a class of high-order uncertain stochastic nonlinear systems. By using some techniques such as the backstepping recursive technique, Young’s inequality, and approximation capability, a novel adaptive neural control scheme is constructed. The proposed control method can guarantee that the signals of the closed-loop system are bounded in probability, and only one parameter needs to be updated online. One example is given to show the effectiveness of the proposed control method.

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