Neural net—fuzzy logic rules mapping for dynamic of fuzzy sets boundaries
CIE '96 Proceedings of the 19th international conference on Computers and industrial engineering
A PI-type controller with self-tuning scaling factors
Fuzzy Sets and Systems
A note on fuzzy PI-type controllers with resetting action
Fuzzy Sets and Systems - Special issue on formal methods for fuzzy modeling and control
Extract intelligible and concise fuzzy rules from neural networks
Fuzzy Sets and Systems - Fuzzy systems
Proceedings of the 3rd International Conference on Genetic Algorithms
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
Fuzzy Sets and Systems - Modeling and control
Adaptive fuzzy control of a class of SISO nonaffine nonlinear systems
Fuzzy Sets and Systems
Automatica (Journal of IFAC)
Genetic learning of fuzzy rules based on low quality data
Fuzzy Sets and Systems
Model-free adaptive control design using evolutionary-neural compensator
Expert Systems with Applications: An International Journal
Multiobjective evolution based fuzzy PI controller design for nonlinear systems
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Adaptive Sliding-Mode Control for NonlinearSystems With Uncertain Parameters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Analytical structures and analysis of the simplest fuzzy PI controllers
Automatica (Journal of IFAC)
Brief Swinging up a pendulum by energy control
Automatica (Journal of IFAC)
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A simple model-free controller is presented for solving the nonlinear dynamic control problems. As an example of the problem, a planetary gear-type inverted pendulum (PIP) is discussed. To control the inherently unstable system which requires real-time control responses, the design of a smart and simple controller is made necessary. The model-free controller proposed includes a swing-up controller part and a stabilization controller part; neither controller has any information about the PIP. Since the input/output scaling parameters of the fuzzy controller are highly sensitive, we use genetic algorithm (GA) to obtain the optimal control parameters. The experimental results show the effectiveness and robustness of the present controller.