Supervisory Recurrent Fuzzy Neural Network Control of Wing Rock for Slender Delta Wings

Abstract

Wing rock is a highly nonlinear phenomenon in which an aircraft undergoes limit cycle roll oscillations at high angles of attack. In this paper, a supervisory recurrent fuzzy neural network control (SRFNNC) system is developed to control the wing rock system. This SRFNNC system is comprised of a recurrent fuzzy neural network (RFNN) controller and a supervisory controller. The RFNN controller is investigated to mimic an ideal controller and the supervisory controller is designed to compensate for the approximation error between the RFNN controller and the ideal controller. The RFNN is inherently a recurrent multilayered neural network for realizing fuzzy inference using dynamic fuzzy rules. Moreover, an on-line parameter training methodology, using the gradient descent method and the Lyapunov stability theorem, is proposed to increase the learning capability. Finally, a comparison between the sliding-mode control, the fuzzy sliding control and the proposed SRFNNC of a wing rock system is presented to illustrate the effectiveness of the SRFNNC system. Simulation results demonstrate that the proposed design method can achieve favorable control performance for the wing rock system without the knowledge of system dynamic functions.

Qualitative Functional Decomposition Analysis of Evolved Neuromorphic Flight Controllers

“…In previous work [4,[7][8][9], the authors were able to demonstrate controllers could be "learned from scratch" by verifying the idea within a framework of neuromorphic evolvable hardware. Further, this previous work also demonstrated the feasibility of neuromorphic adaptive hardware implementations that provide computational advantage over existing adaptive control techniques using similar neural substrates [10,11]. The work mentioned in this paper focuses more intensely on that problem of explaining what those controllers do and how they do it.…”

Intelligent Robust Control for Uncertain Nonlinear Multivariable Systems using Recurrent Cerebellar Model Neural Networks

“…Neural networks (NNs) possess several advantages such as parallelism, fault tolerance, generalization and powerful approximation capabilities, so that NNs have been applied for system identifications and controls [3]- [6]. Some significant results indicate that the main property of NNs is adaptive learning so that it can uniformly approximate arbitrary input-output linear or nonlinear mappings on closed subsets.…”

“…On the neural network structure aspect, NNs can be classified as feedforward neural network (FNN [3], [5], [8], [9]) and recurrent neural network (RNN [4], [6], [7]). As known, FNN is a static mapping.…”

Design of Self-Constructing Recurrent-Neural-Network-Based Adaptive Control

“…Without aid of tapped delay, a feedforward neural network is unable to represent a dynamic mapping. The recurrent neural network (RNN) has superior capabilities as compared to feedforward neural networks, such as their dynamic response and their information storing ability (Lee & Teng, 2000;Lin & Hsu, 2004). Since an RNN has an internal feedback loop, it captures the dynamic response of a system with external feedback through delays.…”

“…Thus, an RNN is a dynamic mapping network. Due to its dynamic characteristic and relatively simple architecture, the recurrent neural network is a useful tool for most real-time applications (Lin & Chen, 2006;Lin & Hsu, 2004;Tian et al, 2004;. Although the neural-network-based adaptive control performances are acceptable in above literatures; however, the learning algorithm only includes the parameter learning, and they have not considered the structure learning of the neural network.…”