Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems

Wang, Chi-Hsu; Lin, Tzu Chen; Lee, Tsu‐Tian; Liu, Han-Leih

doi:10.1109/tsmcb.2002.1033178

Cited by 124 publications

References 24 publications

(25 reference statements)

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Adaptive TS‐FNN control for a class of uncertain multi‐time‐delay systems: The exponentially stable sliding mode‐based approach

Chiang

Chiu

Liu

2008

Adaptive Control & Signal

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This paper presents an adaptive Takagi-Sugeno fuzzy neural network (TS-FNN) control for a class of multiple time-delay uncertain nonlinear systems. First, we develop a sliding surface guaranteed to achieve exponential stability while considering mismatched uncertainty and unknown delays. This exponential stability result based on a novel Lyapunov-Krasovskii method is an improvement when compared with traditional schemes where only asymptotic stability is achieved. The stability analysis is transformed into a linear matrix inequalities problem independent of time delays. Then, a sliding mode control-based TS-FNN control scheme is proposed to achieve asymptotic stability for the controlled system. Since the TS-FNN combines TS fuzzy rules and a neural network structure, fewer numbers of fuzzy rules and tuning parameters are used compared with the traditional pure TS fuzzy approach. Moreover, all the fuzzy membership functions are tuned on-line even in the presence of input uncertainty. Finally, simulation results show the control performance of the proposed scheme.

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Adaptive TS‐FNN control for a class of uncertain multi‐time‐delay systems: The exponentially stable sliding mode‐based approach

Chiang

Chiu

Liu

2008

Adaptive Control & Signal

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Motion Planning for Climbing Robot Based on Hybrid Navigation

Jiang

Wang

Fang

et al. 2006

Advances in Machine Learning and Cybernetics

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In this paper, a motion planning method for autonomous control of a bipedal climbing robot based on hybrid navigation is presented. The algorithm of hybrid navigation blends the optimality of the trajectory planning with the capabilities in expressing knowledge and learning of the fuzzy neural network. The real task environment of the climbing robot is both known and dynamic. Therefore the trajectory planning is used to search roughly for the optimal trajectories which will lead towards the goal according to prior data. Meanwhile, by the process of the multi-sensor data fusion, the fuzzy neural network is employed in dealing efficiently with the uncertain and dynamic situations. The properties of motion planning based on the hybrid navigation are verified by the computer simulations and experiments.

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