TY - GEN
T1 - Adaptive fuzzy Internal Model Control design with bias term compensator
AU - Kumbasar, Tufan
AU - Eksin, Ibrahim
AU - Guzelkaya, Mujde
AU - Yesil, Engin
PY - 2011
Y1 - 2011
N2 - Internal Model Control (IMC) is a powerful method for control system design. However, the classic EMC scheme will not be sufficient in the case of modelling mismatches that might generally occur in the design of nonlinear control systems. Fuzzy modelling is a powerful tool in representing nonlinear systems. Thus, a new fuzzy model based EMC structure is proposed to provide an effective and robust control performance. In this structure, a fuzzy proportional controller is introduced into the feedback loop of the IMC structure to stabilize system and obtain an offset free response while the inverse fuzzy model controller is used for set point tracking. The timing formula of the fuzzy proportional controller in the feedback loop is obtained by instantaneous linearization of the fuzzy model at a certain operating point via simple calculations. In order to increase the robustness and disturbance rejection performance of the modified IMC scheme, an adaptation mechanism is introduced on the fuzzy model. A simulation study has been performed on the nonlinear mass-spring-damper system to show the effectiveness of the proposed scheme.
AB - Internal Model Control (IMC) is a powerful method for control system design. However, the classic EMC scheme will not be sufficient in the case of modelling mismatches that might generally occur in the design of nonlinear control systems. Fuzzy modelling is a powerful tool in representing nonlinear systems. Thus, a new fuzzy model based EMC structure is proposed to provide an effective and robust control performance. In this structure, a fuzzy proportional controller is introduced into the feedback loop of the IMC structure to stabilize system and obtain an offset free response while the inverse fuzzy model controller is used for set point tracking. The timing formula of the fuzzy proportional controller in the feedback loop is obtained by instantaneous linearization of the fuzzy model at a certain operating point via simple calculations. In order to increase the robustness and disturbance rejection performance of the modified IMC scheme, an adaptation mechanism is introduced on the fuzzy model. A simulation study has been performed on the nonlinear mass-spring-damper system to show the effectiveness of the proposed scheme.
KW - Fuzzy Sets
KW - IMC
KW - Inverse Fuzzy Control
KW - Mass-Spring- Damper
KW - Takagi-Sugeno Fuzzy Model
UR - http://www.scopus.com/inward/record.url?scp=80052184495&partnerID=8YFLogxK
U2 - 10.1109/ICMECH.2011.5971302
DO - 10.1109/ICMECH.2011.5971302
M3 - Conference contribution
AN - SCOPUS:80052184495
SN - 9781612849836
T3 - 2011 IEEE International Conference on Mechatronics, ICM 2011 - Proceedings
SP - 312
EP - 317
BT - 2011 IEEE International Conference on Mechatronics, ICM 2011 - Proceedings
T2 - 2011 IEEE International Conference on Mechatronics, ICM 2011
Y2 - 13 April 2011 through 15 April 2011
ER -