title: Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems.
abstract: In this paper, an observer-based direct adaptive fuzzy-neural control scheme is presented for nonaffine nonlinear systems in the presence of unknown structure of nonlinearities. A direct adaptive fuzzy-neural controller and a class of generalized nonlinear systems, which are called nonaffine nonlinear systems, are instead of the indirect one and affine nonlinear systems given by Leu et al. By using implicit function theorem and Taylor series expansion, the observer-based control law and the weight update law of the fuzzy-neural controller are derived for the nonaffine nonlinear systems. Based on strictly-positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be verified. Moreover, the overall adaptive scheme guarantees that all signals involved are bounded and the output of the closed-loop system will asymptotically track the desired output trajectory. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper.
authors.affiliation: Department of Electronic Engineering, Hwa Hsia Institute of Technology, Chung-Ho City, Taipei 23560, Taiwan, ROC. leuyk@cc.hwh.edu.tw
authors.forename: Yih-Guang
authors.initials: YG
authors.lastname: Leu
authors.forename: Wei-Yen
authors.initials: WY
authors.lastname: Wang
authors.forename: Tsu-Tian
authors.initials: TT
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003205:Computing Methodologies; D003661:Decision Support Techniques; D005246:Feedback; D017143:Fuzzy Logic; D008954:Models, Biological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D013269:Stochastic Processes