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- Title
Self-Organizing Interval Type-2 Fuzzy Neural Network Compensation Control Based on Real-Time Data Information Entropy and Its Application in n-DOF Manipulator.
- Authors
Sun, Youbo; Zhao, Tao; Liu, Nian
- Abstract
In order to solve the high-precision motion control problem of the n-degree-of-freedom (n-DOF) manipulator driven by large amount of real-time data, a motion control algorithm based on self-organizing interval type-2 fuzzy neural network error compensation (SOT2-FNNEC) is proposed. The proposed control framework can effectively suppress various types of interference such as base jitter, signal interference, time delay, etc., during the movement of the manipulator. The fuzzy neural network structure and self-organization method are used to realize the online self-organization of fuzzy rules based on control data. The stability of the closed-loop control systems are proved by Lyapunov stability theory. Simulations show that the algorithm is superior to a self-organizing fuzzy error compensation network and conventional sliding mode variable structure control methods in control performance.
- Subjects
ENTROPY (Information theory); REAL-time control; CLOSED loop systems; STABILITY theory; LYAPUNOV stability; FUZZY neural networks; GAS separation membranes
- Publication
Entropy, 2023, Vol 25, Issue 5, p789
- ISSN
1099-4300
- Publication type
Article
- DOI
10.3390/e25050789