| Co-Investigator(Kenkyū-buntansha) |
YAMAGUCHI Teruo FACULTY OF ENGINEERING, KUMAMOTO UNIVERSITY, LECTURER, 工学部, 講師 (50230363)
HARADA Hiroshi FACULTY OF ENGINEERING, KUMAMOTO UNIVERSITY, ASSOCIATE PROFESSOR, 工学部, 助教授 (90145285)
TORIGOE Ippei FACULTY OF ENGINEERING, KUMAMOTO UNIVERSITY, ASSOCIATE PROFESSOR, 工学部, 助教授 (40134663)
SAKATA Masato FACULTY OF ENGINEERING, KUMAMOTO UNIVERSITY, RESEARCH ASSISTANT, 工学部, 助手 (20040652)
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| Research Abstract |
Identification of nonlinear control system is an important task for designing controller to obtain high performance of the actual processes. Although almost all control systems are strictly speaking nonlinear, the identification of nonlinear system is so far not yet well investigated. The authors have recently developed a new method of obtaining Volterra kernels of nonlinear system by use of pseudorandom M-sequence. Computer simulation showed that this identification method is effective with wide applications.
Under this Grant-in-Aid, the authors investigated the possibility of applying the author's method to two actual processes. One is the application to the identification of nonlinear mechatro-servo system; robot manipulator. We applied M-sequence to driving torque of a robot manipulator and the movement angle is measured, Taking the crosscorrelation between them, we obtain Volterra kernels of up to 3rd order. Comparison of the estimated output calculated from the obtained Volterra k
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ernels and the actual output shows a good agreement between them. This result is published in Proc. IMEKO-XV held in Osaka, Japan, in 1999 (Ref. 10). Identification of nonlinear system having backlash type nonlinearity is also investigated and the results are published in Proc. '99KACC held in Seoul, Korea, in 1999 (Ref.13).
The other application of our nonlinear identification is to the identification of nonlinear chemical processes. This time we adopted Continuous Stirred Tank Reactor (CSTR), which is known as bilinear system, as the nonlinear process to be identified. The result shows that our method of nonlinear identification is very effective for nonlinear chemical processes. This result is published in Proc. ASCC which will be held in Shanghai, China, 2000(Ref. 14). The authors also applied our method to Model Predictive Control (MPC) of nonlinear chemical process, since it is thought that when we use more accurate model for MPC of nonlinear chemical processes, the control performance would be much better compared with using inaccurate model, Simulation result shows that our method on nonlinear process identification is also effective for MPC of chemical processes. This result is published in Proc. KACC'98 held in Pusan, Korea, in l998 (Ref. 7). Less
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