Adaptive inverse multilayer fuzzy control for uncertain nonlinear system optimizing with differential evolution algorith
- PDF / 4,248,291 Bytes
- 22 Pages / 595.276 x 790.866 pts Page_size
- 1 Downloads / 207 Views
Adaptive inverse multilayer fuzzy control for uncertain nonlinear system optimizing with differential evolution algorithm Cao Van Kien 1 & Ho Pham Huy Anh 2,3
&
Nguyen Ngoc Son 1
# Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This paper introduces a novel adaptive inverse multilayer T-S fuzzy controller (AIMFC) optimally identified with an optimization soft computing algorithm available for a class of robust control applied in uncertain nonlinear SISO systems. The parameters of multilayer T-S fuzzy model are optimally identified by the differential evolution (DE) algorithm to create offline the inverse nonlinear plant with uncertain coefficients. Then, the adaptive fuzzy-based sliding mode surface is applied to ensure that the closed-loop system is asymptotically stable in which the stability is satisfied using Lyapunov stability concept. The control quality of the proposed AIMFC algorithm is compared with the three recent advanced control algorithms applied in the SpringMass-Damper (SMD) benchmark system. Simulation and experiment results with different control parameters show that the proposed algorithm is better than the inverse fuzzy controller and the conventional adaptive fuzzy controller comparatively applied in both SMD system and the coupled-liquid tank system with the performance index using the least mean squares (LMS) error, which is investigated to demonstrate the efficiency and the robustness of the proposed AIMFC control approach. Keywords Adaptive inverse multilayer T-S fuzzy controller (AIMFC) . Uncertain nonlinear system . Hybrid adaptive optimal control . Differential evolution (DE) algorithm . Lyapunov stability principle . Spring-mass-damper (SMD) benchmark system . Coupled-liquid tank system
1 Introduction Fuzzy logic was first proposed in 1965 by Zadeh [1]. There have been numerous studies developed based on this fuzzy-based domain, such as Fuzzy type-2, Fuzzy type-n, neural fuzzy, hierarchical fuzzy model, applied to model and control nonlinear system [2, 3]. Nowadays, Takagi–Sugeno (T–S) fuzzy set has proved its capability in providing an efficient modeling structure * Ho Pham Huy Anh [email protected] Cao Van Kien [email protected] Nguyen Ngoc Son [email protected] 1
Faculty of Electronics Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Viet Nam
2
Faculty of Electrical and Electronics Engineering (FEEE), Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Viet Nam
3
Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Viet Nam
for nonlinear plants. The benefits of T–S fuzzy models relate to the key feature that they permit us to apply a set of local linear fuzzy sets with an appropriate number of membership functions in order to successfully denote uncertain nonlinear plants. Then the T-S fuzzy model has been increasingly considered as an effective modeling approach applied in numerous applications,
Data Loading...
