Feedback linearization of discrete-time systems
Systems & Control Letters
Multilayer feedforward networks are universal approximators
Neural Networks
An introduction to intelligent and autonomous control
An introduction to intelligent and autonomous control
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
A neural fuzzy control system with structure and parameter learning
Fuzzy Sets and Systems - Special issue on modern fuzzy control
Control of Electrical Drives
Fuzzy Control
Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximation Techniques
Stable indirect fuzzy adaptive control
Fuzzy Sets and Systems - Theme: Modeling and control
Adaptive Control Tutorial (Advances in Design and Control)
Adaptive Control Tutorial (Advances in Design and Control)
Stable auto-tuning of hybrid adaptive fuzzy/neural controllers for nonlinear systems
Engineering Applications of Artificial Intelligence
H∞ tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach
IEEE Transactions on Fuzzy Systems
Stable adaptive control using fuzzy systems and neural networks
IEEE Transactions on Fuzzy Systems
Adaptive fuzzy control: experiments and comparative analyses
IEEE Transactions on Fuzzy Systems
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
Identification and control of dynamic systems using recurrent fuzzy neural networks
IEEE Transactions on Fuzzy Systems
Hybrid adaptive fuzzy identification and control of nonlinear systems
IEEE Transactions on Fuzzy Systems
Adaptive neural/fuzzy control for interpolated nonlinear systems
IEEE Transactions on Fuzzy Systems
Stable auto-tuning of adaptive fuzzy/neural controllers for nonlinear discrete-time systems
IEEE Transactions on Fuzzy Systems
A new approach to fuzzy-neural system modeling
IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems
IEEE Transactions on Neural Networks
Robust adaptive control of nonaffine nonlinear plants with small input signal changes
IEEE Transactions on Neural Networks
Coordinated decentralized adaptive output feedback control of interconnected systems
IEEE Transactions on Neural Networks
Neural network adaptive control for nonlinear nonnegative dynamical systems
IEEE Transactions on Neural Networks
Direct adaptive controller for nonaffine nonlinear systems using self-structuring neural networks
IEEE Transactions on Neural Networks
Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems
IEEE Transactions on Neural Networks
Analysis of Artificial Neural Networks for Pattern-Based Adaptive Control
IEEE Transactions on Neural Networks
Semiglobal ISpS Disturbance Attenuation With Output Tracking via Direct Adaptive Design
IEEE Transactions on Neural Networks
Adaptive Control of a Class of Nonaffine Systems Using Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Neuro-adaptive force/position control with prescribed performance and guaranteed contact maintenance
IEEE Transactions on Neural Networks
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The indirect adaptive regulation of unknown nonlinear dynamical systems is considered in this paper. The method is based on a new neuro-fuzzy dynamical system (neuro-FDS) definition, which uses the concept of adaptive fuzzy systems (AFSs) operating in conjunction with high-order neural network functions (FHONNFs). Since the plant is considered unknown, we first propose its approximation by a special form of an FDS and then the fuzzy rules are approximated by appropriate HONNFs. Thus, the identification scheme leads up to a recurrent high-order neural network (RHONN), which however takes into account the fuzzy output partitions of the initial FDS. The proposed scheme does not require a priori experts' information on the number and type of input variable membership functions making it less vulnerable to initial design assumptions. Once the system is identified around an operation point, it is regulated to zero adaptively. Weight updating laws for the involved HONNFs are provided, which guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. The existence of the control signal is always assured by introducing a novel method of parameter hopping, which is incorporated in the weight updating law. Simulations illustrate the potency of the method and comparisons with conventional approaches on benchmarking systems are given. Also, the applicability of the method is tested on a direct current (dc) motor system where it is shown that by following the proposed procedure one can obtain asymptotic regulation.