Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Fully tuned radial basis function neural networks for flight control
Fully tuned radial basis function neural networks for flight control
Fuzzy Predictive Functional Control in the State Space Domain
Journal of Intelligent and Robotic Systems
Model-Reference Fuzzy Adaptive Control as a Framework for Nonlinear System Control
Journal of Intelligent and Robotic Systems
PD Control of robot with velocity estimation and uncertainties compensation
International Journal of Robotics and Automation
A Decomposed-model Predictive Functional Control Approach to Air-vehicle Pitch-angle Control
Journal of Intelligent and Robotic Systems
SOFMLS: online self-organizing fuzzy modified least-squares network
IEEE Transactions on Fuzzy Systems
Handling drifts and shifts in on-line data streams with evolving fuzzy systems
Applied Soft Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Stable adaptive control using fuzzy systems and neural networks
IEEE Transactions on Fuzzy Systems
Adaptive control of a class of nonlinear systems with nonlinearly parameterized fuzzy approximators
IEEE Transactions on Fuzzy Systems
Supervisory recurrent fuzzy neural network control of wing rock for slender delta wings
IEEE Transactions on Fuzzy Systems
Stable adaptive fuzzy control of nonlinear systems
IEEE Transactions on Fuzzy Systems
Fuzzily Connected Multimodel Systems Evolving Autonomously From Data Streams
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In the paper, two adaptive fuzzy control schemes including indirect and direct frameworks are developed for suppressing the wing-rock motion that is a highly nonlinear aerodynamic phenomenon in which limit cycle roll oscillations are experienced by aircraft at high angles of attack. In the two control topologies, a dynamic fuzzy system called Extended Sequential Adaptive Fuzzy Inference System (ESAFIS) is constructed to represent the dynamics of the wing-rock system. ESAFIS is an online learning fuzzy system in which the rules are added or deleted based on the input data. In the indirect control scheme, the ESAFIS is used to estimate the nonlinear dynamic function and then a stable indirect fuzzy controller is designed based on the estimator. In the direct control scheme, the ESAFIS controller is directly designed to imitate an ideal stable control law without determining the model of the dynamic function. Different from the original ESAFIS, the adaptive tuning algorithms for the consequent parameters are established in the sense of Lyapunov theorem to ensure the stability of the overall control system. A sliding mode controller is also designed to compensate for the modelling errors of ESAFIS by augmenting the indirect/direct fuzzy controller. Finally, comparisons with a neuron control scheme using the RBF network and a fuzzy control scheme with Takagi-Sugeno (TS) system are presented to depict the effectiveness of the proposed control strategies. Simulation results show that the proposed fuzzy controllers achieve better tracking performance with dynamically allocating the rules online.