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Adaptive neural-fuzzy control of triple inverted pendulum

GAO Jun-wei,CAI Guo-qiang,JI Zhi-jian,QIN Yong,JIA Li-min,

控制理论与应用 , 2010,
Abstract: In the triple inverted pendulum(TIP) system, adaptive neural-fuzzy inference system(ANFIS) approach is atilized to combine fuzzy logic with Neural-Network, according to the input/output data, so that ANFIS automatically adjusts fuzzy rules and membership functions based on state synthesis to fit sampling data. The simulation results show that the designed ANFIS controller is feasible. Compared with LQR control, triple inverted pendulum based on ANFIS control has better dynamics performance and anti-interference capability.
The design of improved variable universe fuzzy controller of triple inverted pendulum

CHEN Fu-guo,DENG Guan-nan,TAN Yan-hua,

控制理论与应用 , 2010,
Abstract: In traditional variable universe fuzzy control system, the universe changes with the values of input variables;this damages the response rapidity. What's more, it is difficulty to select the function models for the expansion-contraction factors and their parameters. To deal with the above problems, a fuzzy controller of triple inverted pendulum based on a new variable universe algorithm is designed. First, the idea of comparatively variable fuzzy control is presented. Second, the expansion-contraction factor is constructed based on the fuzzy logic inference rule list, and the output tuning factor is designed based on the closed-loop response curve. Finally, pole assignment method is employed to integrate the states into two variables to avoid the rules explosion problem. The simulation results show, compared with the traditional variable universe fuzzy control, the proposed controller improve the response speed and control precision of the inverted pendulum system.
Stabilization of an inverted pendulum system via an SIRM neuro-fuzzy controller
Khwanon, S.,Kulworawanichpong, T.,Sujitjorn, S.
Songklanakarin Journal of Science and Technology , 2005,
Abstract: This article presents a new neuro-fuzzy controller to stabilize an inverted pendulum system. The proposed controller consists of the Single Input Rule Modules (SIRMs), the artificial neural network (ANN) and the dynamic importance degrees (DIDs). It simultaneously controls both the angle of the pendulum and the position of the cart. The learning of the ANN results in the DIDs. The proposed controller has a simple structure that can decrease the number of fuzzy rules. The simulation results show that the proposed neurofuzzy controller has an ability to stabilize a wide range of the inverted pendulum system within a short periodof time. Moreover, the comparisons of the simulation results between the proposed neuro-fuzzy controller and the SIRMs fuzzy controller are revealed in this article.
Fuzzy weighted hybrid control for inverted pendulum

ZHAO Wen-jie,ZHANG Li-juan,LIU Yan-quan,LIU Yu-yan,

控制理论与应用 , 2004,
Abstract: A fuzzy-weighted control method,which combines sliding mode control and linear state feedback,is proposed for inverted pendulum.Sliding mode control was used to regulate the pendulum angle to neighborhood of zero.In the neighborhood of zero,the linear model was obtained by approximating the nonlinear model of inverted pendulum and the linear state feedback controller based on pole assignment was designed to stabilize the state of the system.A weight-sum of the controller outputs was then used to supply the control action to inverted pendulum.The results of simulation demonstrated the effectiveness of the method.
The swing-up and stabilization of the triple inverted pendulum

ZHANG Yong-li,CHENG Hui-feng,LI Hong-xing,

控制理论与应用 , 2011,
Abstract: The swing-up of the triple inverted pendulum is achieved by inversion-based trajectory control. The variablegain linear quadratic regulator is designed to stabilize the triple inverted pendulum in the upward position. The transient process of swing-up of the triple inverted pendulum is treated as a nonlinear two-point boundary value problem. The normal trajectories of swing-up are obtained by solving a two-point boundary value problem, and a feedforward control is designed by using the inverse system method. A gain-scheduled feedback is used to stabilize the system during the swing-up. In addition, the triple inverted pendulum in the upward position is stabilized by utilizing the variable-gain linear quadratic regulator. Simulation results demonstrate the effectiveness of the proposed control scheme.
Comparative Performance Analysis between Fuzzy Logic Controller (FLC) and PID Controller for an Inverted Pendulum System
Aleem Ahmed Khan,Kashan Hussain
International Journal of Electrical, Electronics and Computer Systems (IJEECS) , 2012,
Abstract: The idea of this paper is to compare the time-response performance characteristics between two controllers having different strategy for an inverted pendulum system. The main objective is to determine which control strategy brings the better results in comparative analysis with regard to pendulum’s angle and cart’s position of the system. The inverted pendulum system in fact a critical and challenging control problem, which continually moves away from a stable state. Two Control strategies for an Inverted pendulum system model are presented for stabilized controlling such as Proportional-Integral-Derivatives (PID) and Fuzzy Logic Controller (FLC) Controllers. Matlab Simulation has been performed on Simulink platform shows that both controllers successfully controls Multi-output Inverted pendulum system. However PID is more efficient and has a better time response characteristics than FLC control strategy.
Inverted Pendulum Design with Hardware Fuzzy Logic Controller
Eric Minnaert,Brian Hemmelman,Dan Dolan
Journal of Systemics, Cybernetics and Informatics , 2008,
Abstract: An inverted pendulum robot has been designed and built using a fuzzy logic controller implemented in a Field Programmable Gate Array (FPGA). The Mamdani fuzzy controller has been implemented using integer numbers to simplify its construction and improve system throughput. An accelerometer and rate gyroscope are used along with a complementary filter to monitor the state of the robot. Using angular velocity and angle error the fuzzy controller can successfully balance the inverted pendulum robot.
Adaptation of a fuzzy controller’s scaling gains using genetic algorithms for balancing an inverted pendulum  [PDF]
Duka Adrian-Vasile
Scientific Bulletin of the ''Petru Maior" University of T?rgu Mure? , 2011,
Abstract: This paper examines the development of a genetic adaptive fuzzy control system for the Inverted Pendulum. The inverted pendulum is a classical problem in Control Engineering, used for testing different control algorithms. The goal is to balance the inverted pendulum in the upright position by controlling the horizontal force applied to its cart. Because it is unstable and has a complicated nonlinear dynamics, the inverted pendulum is a good testbed for the development of nonconventional advanced control techniques. Fuzzy logic technique has been successfully applied to control this type of system, however most of the time the design of the fuzzy controller is done in an ad-hoc manner, and choosing certain parameters (controller gains, membership functions) proves difficult. This paper examines the implementation of an adaptive control method based on genetic algorithms (GA), which can be used on-line to produce the adaptation of the fuzzy controller’s gains in order to achieve the stabilization of the pendulum. The performances of the proposed control algorithms are evaluated and shown by means of digital simulation.
A Simple and Easy Fuzzy Control Method for Inverted Pendulum

ZHAO Hai-long,

控制理论与应用 , 2000,
Abstract: This paper applies simple and easy fuzzy control method to inverted pendulum to keep it stable and to realize one way movement and two way movement by controlling the balance position of inverted pendulum not in the verical direction.
International Journal of Electrical, Electronics and Data Communication , 2013,
Abstract: The inverted pendulum (IP) is among the most difficult systems to control in the field of control engineering. The process is non linear and unstable. The objective of this paper is to balance the pendulum vertically on a motor driven wagon by the use of hierarchical fuzzy PID controller. The hierarchical fuzzy PID controller divides the control system into multiple granular levels and adopts different control strategies in different granularities. We use different fuzzy controls forcoarse adjustment in the coarse granularity and adopt classical PID as fine control in the fine granularity. Then we design asupervisor which is a fuzzy controller to switch between the different fuzzy controllers and PID control smoothly
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