Adaptive Sliding-Mode H∞ Control via Self-Evolving Function-Link Interval Type-2 Petri Fuzzy-Neural-Network for XY-Stage Nonlinear System

Purpose – Micro aerial vehicle is nonlinear plant; it is difficult to obtain stable control for MAV attitude due to uncertainties. The purpose of this paper is to propose one robust stable control strategy for MAV to accommodate system uncertainties, variations, and external disturbances. Design/methodology/approach – First, by employing interval type-II fuzzy neural network (ITIIFNN) to approximate the nonlinearity function and uncertainty functions in the attitude angle dynamic model of micro aircraft vehicle (MAV). Then, the Lyapunov stability theorem is used to testify the asymptotic stability of the closed-loop system, the parameters of the ITIIFNN and gain of sliding mode control can be tuned on-line by adaptive laws based on Lyapunov synthesis approach, and the Lyapunov stability theorem has been used to testify the asymptotic stability of the closed-loop system. Findings – The validity of the proposed control method has been verified through real-time experiments. The experimental results show that the performance of interval type-II fuzzy neural network based gain adaptive sliding mode controller (GASMC-ITIIFNN) is significantly improved compared with conventional adaptive sliding mode controller (CASMC), type-I fuzzy neural network based sliding mode controller (GASMC-TIFNN). Practical implications – This approach has been used in one MAV, the controller works well, and which could guarantee the MAV control system with good performances under uncertainties, variations, and external disturbances. Originality/value – The main original contributions of this paper are: the proposed control scheme makes full use of the nominal model of the MAV attitude control model; the overall closed-loop control system is globally stable demonstrated by Lyapunov stable theory; the tracking error can be asymptotically attenuated to a desired small level around zero by appropriate chosen parameters and learning rates; and the MAV attitude control system based on GASMC-ITIIFNN controller can achieve favourable tracking performance than GASMC-TIFNN and CASMC.

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A functional-link based interval type-2 compensatory fuzzy neural network for nonlinear system modeling

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011) ◽

10.1109/fuzzy.2011.6007477 ◽

2011 ◽

Cited By ~ 8

Author(s):

Jyh-Yeong Chang ◽

Yang-Yin Lin ◽

Ming-Feng Han ◽

Chin-Teng Lin

Keyword(s):

Neural Network ◽

Nonlinear System ◽

Fuzzy Neural Network ◽

System Modeling ◽

Functional Link ◽

Nonlinear System Modeling ◽

Fuzzy Neural ◽

Interval Type

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System Identification Based on Dynamical Training for Recurrent Interval Type-2 Fuzzy Neural Network

International Journal of Fuzzy System Applications ◽

10.4018/ijfsa.2011070105 ◽

2011 ◽

Vol 1 (3) ◽

pp. 66-85 ◽

Cited By ~ 5

Author(s):

Tsung-Chih Lin ◽

Yi-Ming Chang ◽

Tun-Yuan Lee

Keyword(s):

Neural Network ◽

Neural Networks ◽

Nonlinear System ◽

Fuzzy Neural Network ◽

Fuzzy Neural Networks ◽

Training Data ◽

Fuzzy Neural ◽

Interval Type ◽

Recurrent Interval

This paper proposes a novel fuzzy modeling approach for identification of dynamic systems. A fuzzy model, recurrent interval type-2 fuzzy neural network (RIT2FNN), is constructed by using a recurrent neural network which recurrent weights, mean and standard deviation of the membership functions are updated. The complete back propagation (BP) algorithm tuning equations used to tune the antecedent and consequent parameters for the interval type-2 fuzzy neural networks (IT2FNNs) are developed to handle the training data corrupted by noise or rule uncertainties for nonlinear system identification involving external disturbances. Only by using the current inputs and most recent outputs of the input layers, the system can be completely identified based on RIT2FNNs. In order to show that the interval IT2FNNs can handle the measurement uncertainties, training data are corrupted by white Gaussian noise with signal-to-noise ratio (SNR) 20 dB. Simulation results are obtained for the identification of nonlinear system, which yield more improved performance than those using recurrent type-1 fuzzy neural networks (RT1FNNs).

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