A New Type-2 Fuzzy Sliding Mode Control for Longitudinal Aerodynamic Parameters of a Commercial Aircraft

Abstract

Although sliding mode control has many advantages such as stability and robustness but there are two important disadvantages as follow: Chattering phenomenon and mathematical nonlinear dynamic equivalent controller part. So, this paper presents a new method of adaptive sliding mode control based on general type-2 fuzzy logic to overcome on the mentioned problems. First, the longitudinal motion equations of a commercial aircraft and the upper limits of the unknown functions are introduced, which include the driving errors and uncertain parameters of the model. Then, a general type-2 fuzzy neural network (GT2FNNs), with adaptive rules, estimates these limits. Estimating the limits can reduce the computational load with less rules and weight than the dynamic matrix. The Boeing 747 is being studied and an attempt has been made to use a model very close to this aircraft. The stability of the control system has been proven. The simulation results show that by applying three models of faults to the aircraft system, the proposed type-2 fuzzy-based sliding mode control has excellent performance, especially in controlling the Aileron and Rudder angles.

Modeling Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System for Power Prediction

“…Layer 7: The output is:ŷ =ŷ l +ŷ r 2 (15) In this article, structure learning is realized by exploiting T2F clustering. As one knows, an efficacious method is suggested to procreate fuzzy rules in real-time and decrease computations in antecedent part in structure optimization [62].…”

“…A novel method was suggested for general T2F clustering by Doostparast et al [13]. Some other applications of T2F sets can be found in textile engineering [14] and aerospace engineering [15]. Fuzzy c-means clustering and high order cognitive map were exerted by Lu in order to model and predict time series by T1FS [16].…”

“…Furthermore, the convergence issue in the learning algorithm is not taken into consideration and conversed even theoretically. Juang et al [15] put forth another contributive recurrent T2F neural network to model dynamical systems. There is not any rule pruning, which leads to extremely overlapped fuzzy sets.…”

Modeling of Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System (NCPRT2FS) for Power Prediction

“…A novel method was suggested for general T2F clustering by Doostparast et al [13]. Some other applications of T2F sets can be find in textile engineering [14] and aerospace engineering [15]. Fuzzy cmeans clustering and high order cognitive map were exerted by Lu in order to model and predict time series on the basis of type-1 fuzzy sets [16].…”

“…Furthermore, the convergence issue in the learning algorithm is not taken into consideration and conversed even theoretically. Juang et al [15] put forth another contributive recurrent T2F neural network to model dynamical systems. That there is not any rule pruning, is the major defect with their work.…”