Stabilization of the quadruple inverted pendulum by variable universe adaptive fuzzy controller based on variable gain H ∞ regulator

2012 ◽  
Vol 25 (5) ◽  
pp. 856-872 ◽  
Author(s):  
Yongli Zhang ◽  
Jiayin Wang ◽  
Hongxing Li
Keyword(s):  
Inverted Pendulum ◽  
Fuzzy Controller ◽  
Adaptive Fuzzy Controller ◽  
Variable Gain ◽  
Adaptive Fuzzy ◽  
Variable Universe

Design of Parameter Adaptive Fuzzy Controller for the Planar Double Inverted Pendulum

2013 ◽  
Vol 273 ◽  
pp. 759-763
Author(s):  
Wei Zhang ◽  
Jiao Long Zhang
Keyword(s):  
Inverted Pendulum ◽  
Fuzzy Controller ◽  
Good Control ◽  
Fast Response ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
Control Precision ◽  
Input Variables ◽  
The Impact ◽  
Parameter Adaptive

In this paper, The mathematical model is established using analytical dynamics method for planar double inverted pendulum, based on optimal control theory, parameter adaptive fuzzy controller is designed by use of variable fusion technology, thereby it can reduce the dimension of the input variables of the fuzzy controller, greatly decrease the number of the fuzzy rules, and the impact of the quantization factor on the control effectiveness is studied. The designed controller for system is applied in simulation experiments, the results show that the fuzzy controller can guarantee good control precision, fast response, control planar inverted pendulum stability.

scholarly journals Heuristic Global Optimization of an Adaptive Fuzzy Controller for the Inverted Pendulum System: Experimental Comparison

2020 ◽  
Vol 10 (18) ◽  
pp. 6158
Author(s):  
Miguel Llama ◽  
Alejandro Flores ◽  
Ramon Garcia-Hernandez ◽  
Victor Santibañez
Keyword(s):  
Real Time ◽  
Inverted Pendulum ◽  
Fuzzy Controller ◽  
Parameter Tuning ◽  
Lyapunov Theory ◽  
Experimental Comparison ◽  
Pendulum System ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
Inverted Pendulum System

In this paper an adaptive fuzzy controller is proposed to solve the trajectory tracking problem of the inverted pendulum on a cart system. The designed algorithm is featured by not using any knowledge of the dynamic model and incorporating a full-state feedback. The stability of the closed-loop system is proven via the Lyapunov theory, and boundedness of the solutions is guaranteed. The proposed controller is heuristically tuned and its performance is tested via simulation and real-time experimentation. For this reason, a tuning method is investigated via evolutionary algorithms: particle swarm optimization, firefly algorithm and differential evolution in order to optimize the performance and verify which technique produces better results. First, a model-based simulation is carried out to improve the parameter tuning of the fuzzy systems, and then the results are transferred to real-time experiments. The optimization procedure is presented as well as the experimental results, which are also discussed.

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