Adaptive T–S fuzzy-neural modeling and control for general MIMO unknown nonaffine nonlinear systems using projection update laws

Multivariable adaptive control: A survey

“…Of those so-called universal approximators, neural networks and fuzzy functions have been widely employed for approximation-based adaptive control of some classes of uncertain nonlinear dynamical systems. Some references that focus on MIMO nonlinear systems are: (i) multivariable adaptive control using neural networks: Deng and Li (2005), Fu and Chai (2007), Ge, Zhang, and Lee (2004), Henriques and Dourado (1999), Jin, Nikiforuk, and Gupta (1994), Narendra and Mukhopadhyay (1994), Ordonez and Passino (1999), Salahshoor and Kamalabady (2010), Selmic and Lewis (2000), Trunov and Polycarpou (2000), Wang, Chien, Leu, and Lee (2010), Wang and Huang (2005), Yang, Yang, and Sun (2012); and (ii) multivariable adaptive control using fuzzy system models: Cheng and Chien (2006), Chiu (2006), Johansen (1994), Labiod and Guerra (2007), Liu, Tong, and Li (2011), Moustakidis, Rovithakis, and Theocharis (2008), Ordonez and Passino (1999), Rong, Sundararajan, Saratchandran, and Huang (2007), Tong, Li, and Shi (2012), Trebi-Ollennu and White (1997), and Zhang, Cai, and Bien (2000).…”

Adaptive Neural Control of Nonaffine Systems With Unknown Control Coefficient and Nonsmooth Actuator Nonlinearities

“…The assumption on the upper bound of control effectiveness terms (∂ f i (x i , x i+1 )/∂ x i+1 , ∂ f n (x n , u)/∂u) is eliminated in this brief as opposed to [17]- [19]. 2162-237X © 2014 IEEE.…”

Adaptive Stabilization Control for a Class of Complex Nonlinear Systems Based on T-S Fuzzy Bilinear Model

“…Compared with the adaptive control based on fuzzy logic system, the development of the adaptive control based on dynamic T-S fuzzy model is relatively slow; until recent years, the latter has become a focus issue in the control community. Novel research results have emerged in [10][11][12][13][14][15][16][17][18][19]. A novel direct T-S fuzzy neural online modelling and control method for a class of nonlinear systems with parametric uncertainties has been proposed, which utilized T-S fuzzy neural model to approximate the virtual linear system and designed the online identification algorithm and robust adaptive tracking controller in [10,11], respectively.…”

“…The adaptive compensation term was adopted to reduce the effect of the modelling. However, the consequence parts of the above T-S fuzzy models [10][11][12][13][14][15][16][17][18][19] were linear dynamic model or approximate linear dynamic model, so these methods have inevitable defects for some nonlinear systems.…”