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.

Adjusting the errors of the GM(1, 2) grey model in the financial data series using an adaptive fuzzy controller

2016 ◽  
Vol 6 (3) ◽  
pp. 341-352 ◽  
Author(s):  
Marcel Bolos ◽  
Ioana Bradea ◽  
Camelia Delcea
Keyword(s):  
Fuzzy Controller ◽  
Financial Data ◽  
Data Series ◽  
Output Variable ◽  
Content Type ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
Grey Models ◽  
The Status ◽  
Input Variables

Purpose The purpose of this paper is to focus on the adjustment of the GM(1, 2) errors for financial data series that measures changes in the public sector financial indicators, taking into account that the errors in grey models remain a key problem in reconstructing the original data series. Design/methodology/approach Adjusting the errors in grey models must follow some rules that most often cannot be determined based on the chaotic trends they register in reconstructing data series. In order to ensure the adjustment of these errors, for improving the robustness of GM(1, 2), was constructed an adaptive fuzzy controller which is based on two input variables and one output variable. The input variables in the adaptive fuzzy controller are: the absolute error ε i 0 ( k ) [ % ] of GM(1, 2), and the distance between two values x i 0 ( k ) [ % ] , while the output variable is the error adjustment A ε i 0 ( k ) [ % ] determined with the help of the above-mentioned input variables. Findings The adaptive fuzzy controller has the advantage that sets the values for error adjustments by the intensity (size) of the errors, in this way being possible to determine the value adjustments for each element of the reconstructed financial data series. Originality/value To ensure a robust process of planning the financial resources, the available financial data are used for long periods of time, in order to notice the trend of the financial indicators that need to be planned. In this context, the financial data series could be reconstituted using grey models that are based on sequences of financial data that best describe the status of the analyzed indicators and the status of the relevant factors of influence. In this context, the present study proposes the construction of a fuzzy adaptive controller that with the help of the output variable will ensure the error’s adjustment in the reconstituted data series with GM(1, 2).

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.

Research on the Temperature Control Algorithm of the Poultry Farm

2014 ◽  
Vol 602-605 ◽  
pp. 1206-1209
Author(s):  
Xiang Jie Niu
Keyword(s):  
Temperature Control ◽  
Control Algorithm ◽  
Good Control ◽  
Fast Response ◽  
System Stability ◽  
Poultry Farm ◽  
Control Effect ◽  
Control Precision ◽  
High Control ◽  
The Impact

The environment in poultry farm is a complex system with time-varying, nonlinear and pure lag. The traditional temperature control algorithm is difficult to obtain good control effect. In order to solve the issues of larger overshoot, vibration, low control accuracy and so on, this paper presents a fuzzy-PID estimated control algorithm. Firstly, the system identification is used to get more accurate system model. Then, using the fuzzy control rules improves the system stability precision, reduces the impact of the delay on the system and improves the adaptability of the system. Simulation results show that the proposed control strategy has a fast response, small overshoot, robustness, high control precision and so on. It provides a reference for intelligent control design of poultry temperature.

scholarly journals Thyristor Controlled Series Capasitor Berbasis Adaptive Fuzzy Logic Controller Sebagai Percepatan Peredaman Osilasi Daya Pada Sistem Tenaga

2018 ◽  
Vol 2 (1) ◽  
pp. 37-41
Author(s):  
Dwi Agus Prabowo ◽  
Istiyo Winarno
Keyword(s):  
Power System ◽  
Fuzzy Logic Controller ◽  
Service Providers ◽  
Fuzzy Controller ◽  
System Stability ◽  
Main Concern ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
The Stability ◽  
The Impact

The current population growth is very fast, so also the number of settlements more evenly, with this demand fulfillment demand for electricity is increasingly widespread and more, therebr making electric power generation service providers continue to strive to provide uniform and stable electrical energy. On the other hand there is an impact due to the many loads on the network electricity that can not be estimated its use, rise and fall of the load, therefore the power system stability must be maintained, this makes the stability of the power system the main concern in a operating. Without good dampening the disturbance will be isolated in the system and out of the stability area, so it can lead to worse effects such as total blackout. Thyristor Controlled Series Capacitor (TCSC) is a device that can be used to regulate power inmadance of power system. TCSC has three main components such as inductor, capacitor, and thyristor. The way TCSC works is by setting the angle of ignition, here the adaptive fuzzy controller is used as the best alpha-viewer the system needs. From the comparison simulation, the difference of fuzzy controller with adaptive fuzzy with fuzzy controller can reduce oscillation at 0.68 second average time and with fuzzy oscillation adaptive controller that can be muffled at 0.56 seconds, with this adaptive fuzzy controller capable damping oscillations 0.12 seconds faster in comparison with fuzzy controllers. So with this oscillation damping can reduce the impact of isolated disturbances in the system.

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