A direct control parameter tuning method using generalized minimum variance evaluation

2014 ◽  
Author(s):  
Kazuma Ando ◽  
Shiro Masuda
Keyword(s):  
Control Parameter ◽  
Parameter Tuning ◽  
Minimum Variance ◽  
Direct Control ◽  
Tuning Method

Constant-Value Control of Joystick Operating Force Using Acoustic System Having Feedback Control System

2008 ◽  
Vol 20 (4) ◽  
pp. 578-584
Author(s):  
Taichi Sato ◽  
◽  
Yuta Murayama ◽  
Hiroshi Igarashi ◽  
◽  
...  
Keyword(s):  
Control System ◽  
Control Parameter ◽  
Control Experiment ◽  
Parameter Tuning ◽  
Test Subject ◽  
Control Parameters ◽  
Tuning Method ◽  
Acoustic Control ◽  
Tuning Methods ◽  
Time Waveform

We have conducted experiments of operating a force operating joystick to the sound. Test subjects listen to white noise with square envelope in a semi anechoic room and joystick operating force is measured. We have seen that time waveform of operating force varies among experiments. Variation in operating force depends on day, test subject, etc. And, we have established an acoustic control system that feedbacks operating force for realizing target value of joystick operating force. We conducted a constant-value control experiment using PI control in which each control parameters’ effects are studied. As a result, among three existing control parameter tuning methods, Cohen-Coon’s control parameter tuning method is the most appropriate for our system.

scholarly journals An MEA-Tuning Method for Design of the PID Controller

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yongli Zhang ◽  
Lijun Zhang ◽  
Zhiliang Dong
Keyword(s):  
Pid Controller ◽  
Industrial Process ◽  
Optimal Solution ◽  
Design Feature ◽  
Parameter Tuning ◽  
Parallel Structure ◽  
Evolutionary Information ◽  
Tuning Method ◽  
Efficiency And Effectiveness ◽  
Global Optimal

The optimization and tuning of parameters is very important for the performance of the PID controller. In this paper, a novel parameter tuning method based on the mind evolutionary algorithm (MEA) was presented. The MEA firstly transformed the problem solutions into the population individuals embodied by code and then divided the population into superior subpopulations and temporary subpopulations and used the similar taxis and dissimilation operations for searching the global optimal solution. In order to verify the control performance of the MEA, three classical functions and five typical industrial process control models were adopted for testing experiments. Experimental results indicated that the proposed approach was feasible and valid: the MEA with the superior design feature and parallel structure could memorize more evolutionary information, generate superior genes, and enhance the efficiency and effectiveness for searching global optimal parameters. In addition, the MEA-tuning method can be easily applied to real industrial practices and provides a novel and convenient solution for the optimization and tuning of the PID controller.

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