Fuzzy identification and control of a liquid level rig
Fuzzy Sets and Systems
Structure identification of fuzzy model
Fuzzy Sets and Systems
Fuzzy adaptive control of a first-order process
Fuzzy Sets and Systems
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
An introduction to fuzzy control
An introduction to fuzzy control
Nested design of fuzzy controllers with partial fuzzy rule base
Fuzzy Sets and Systems
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Analysis of direct action fuzzy PID controller structures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Two-level tuning of fuzzy PID controllers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Analytical structures and analysis of the simplest fuzzy PDcontrollers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A robust self-tuning scheme for PI- and PD-type fuzzy controllers
IEEE Transactions on Fuzzy Systems
Deadzone compensation in discrete time using adaptive fuzzy logic
IEEE Transactions on Fuzzy Systems
Brief Swinging up a pendulum by energy control
Automatica (Journal of IFAC)
Adaptive control using neural networks and approximate models
IEEE Transactions on Neural Networks
Two-stage rule-based precision positioning control of a piezoelectrically actuated table
International Journal of Systems Science
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In this article, a systematic two-stage design method for adaptive fuzzy controllers is presented. The proposed control scheme has low computational complexity. Moreover, the exact mathematical model of the plant to be controlled is not required. The fuzzy controller under consideration is based on the proportional-derivative fuzzy control scheme and triangular membership functions. In the design procedure, the domain intervals of the input and output variables are selected with a heuristic approach to minimize a cost function under the constraint of tolerable overshoots in the response curve. A learning scheme is then proposed to automatically adjust the parameters in the fuzzy controller to reduce the error of the system. It can also be used adaptively to improve the system performance of a time-varying system. Simulations and comparisons are included to demonstrate the effectiveness of the proposed method.