TY - JOUR
T1 - TS-based sampled-data model predictive controller for continuous-time nonlinear systems
AU - Vafamand, Navid
AU - Naghavi, Seyed Vahid
AU - Safavi, Ali Akbar
AU - Khayatiyan, Alireza
AU - Khooban, Mohammad Hassan
AU - Dragicevic, Tomislav
PY - 2018/12/10
Y1 - 2018/12/10
N2 - This paper proposes a new fuzzy model predictive control approach for continuous-time nonlinear systems in terms of linear matrix inequalities (LMIs). The proposed approach is based on the Takagi–Sugeno fuzzy modeling, a quadratic Lyapunov function, and a sampled-data parallel distributed compensation controller with constant sampling time. The goal is designing the sampled-data controller such that at each sampling time, the stability of the closed-loop system is guaranteed and an infinite horizon cost function is minimised. The main advantage of the proposed approach is to eliminate the approximations induced from discretizing the original system and cost function upper bound minimisation. Consequently, a lower bound of the cost function is obtained and the performance of the proposed model predictive controller is improved compared to the recently published papers in the same field of interest. In addition, the Euclidean norm constraint of the control input vector is derived in terms of LMIs. To illustrate the merits of the proposed approach, the proposed technique is applied to a continuous stirred tank reactor system.
AB - This paper proposes a new fuzzy model predictive control approach for continuous-time nonlinear systems in terms of linear matrix inequalities (LMIs). The proposed approach is based on the Takagi–Sugeno fuzzy modeling, a quadratic Lyapunov function, and a sampled-data parallel distributed compensation controller with constant sampling time. The goal is designing the sampled-data controller such that at each sampling time, the stability of the closed-loop system is guaranteed and an infinite horizon cost function is minimised. The main advantage of the proposed approach is to eliminate the approximations induced from discretizing the original system and cost function upper bound minimisation. Consequently, a lower bound of the cost function is obtained and the performance of the proposed model predictive controller is improved compared to the recently published papers in the same field of interest. In addition, the Euclidean norm constraint of the control input vector is derived in terms of LMIs. To illustrate the merits of the proposed approach, the proposed technique is applied to a continuous stirred tank reactor system.
KW - Fuzzy model predictive control
KW - sampled-data parallel distributed compensation (SDPDC)
KW - Takagi-Sugeno (TS) fuzzy model
KW - linear matrix inequality (LMI)
KW - online optimization
KW - continuous stirred tank reactor (CSTR)
UR - http://www.scopus.com/inward/record.url?scp=85055412141&partnerID=8YFLogxK
U2 - 10.1080/00207721.2018.1536234
DO - 10.1080/00207721.2018.1536234
M3 - Journal article
SN - 0020-7721
VL - 49
SP - 3284
EP - 3295
JO - International Journal of Systems Science
JF - International Journal of Systems Science
IS - 16
ER -