Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
A course in fuzzy systems and control
A course in fuzzy systems and control
Real-time control of robot manipulators by neural networks
Integrated Computer-Aided Engineering - Special issue: real-time intelligent control systems
Neuro-fuzzy based approach for hybrid force/position robot control
Integrated Computer-Aided Engineering
Adaptive fuzzy control for a class of uncertain nonaffine nonlinear systems
Information Sciences: an International Journal
Multivariable Neurofuzzy Control of an Autonomous Underwater Vehicle
Integrated Computer-Aided Engineering
Active control of electro-rheological fluid embedded pneumatic vibration isolator
Integrated Computer-Aided Engineering
Intelligent adaptive control for MIMO uncertain nonlinear systems
Expert Systems with Applications: An International Journal
Observer-based fuzzy adaptive control for strict-feedback nonlinear systems
Fuzzy Sets and Systems
Dynamic structure adaptive neural fuzzy control for MIMO uncertain nonlinear systems
Information Sciences: an International Journal
Adaptive control of robot manipulators using fuzzy logic systems under actuator constraints
Fuzzy Sets and Systems
Fuzzy adaptive output feedback control for MIMO nonlinear systems
Fuzzy Sets and Systems
IEEE Transactions on Fuzzy Systems
Extended Kalman and Particle Filtering for sensor fusion in motion control of mobile robots
Mathematics and Computers in Simulation
On necessary and sufficient conditions for differential flatness
Applicable Algebra in Engineering, Communication and Computing
Diagnosing multiple faults in oil rig motor pumps using support vector machine classifier ensembles
Integrated Computer-Aided Engineering
Adaptive fuzzy control for field-oriented induction motor drives
Neural Computing and Applications
Adaptive fuzzy decentralized control fora class of large-scale nonlinear systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid adaptive fuzzy control for a class of nonlinear MIMO systems
IEEE Transactions on Fuzzy Systems
Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems
IEEE Transactions on Fuzzy Systems
Mixed Feedforward/Feedback Based Adaptive Fuzzy Control for a Class of MIMO Nonlinear Systems
IEEE Transactions on Fuzzy Systems
Adaptive Fuzzy Output Tracking Control of MIMO Nonlinear Uncertain Systems
IEEE Transactions on Fuzzy Systems
Brief Robustness analysis of exact feedforward linearization based on differential flatness
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
Robust Self-Organizing Neural-Fuzzy Control With Uncertainty Observer for MIMO Nonlinear Systems
IEEE Transactions on Fuzzy Systems
Teleoperation of multi-agent systems with nonuniform control input delays
Integrated Computer-Aided Engineering
Modelling and simulation of double-link scenario in a two-wheeled wheelchair
Integrated Computer-Aided Engineering
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The paper proposes flatness-based adaptive fuzzy control for uncertain MIMO nonlinear dynamical systems. The considered control scheme based on differential flatness theory extends the class of systems in which indirect adaptive fuzzy control can be applied. To conclude if a dynamical system is differentially flat, the following should be examined: i the existence of the flat output, which is a variable that can be written as a function of the system's state variables ii the system's state variables and the input can be written as functions of the flat output and its derivatives. Nonlinear systems satisfying the differential flatness property can be written in the Brunovsky canonical form via a transformation of their state variables and control inputs. The resulting control signal is shown to contain nonlinear elements, which in case of unknown system parameters can be calculated using neuro-fuzzy approximators. Using Lyapunov stability analysis it is shown that one can compute an adaptation law for the neuro-fuzzy approximators which assures stability of the closed loop. The performance of the proposed flatness-based adaptive fuzzy control scheme is tested through simulation experiments on benchmark nonlinear multi-input multi-output dynamical systems, such as robotic manipulators.