A New Type of Adaptive Neural Network Fuzzy Controller in the Double Inverted Pendulum System

Zhang, Suying; An, Ran; Shao, Shu-Man

doi:10.1007/978-3-642-23887-1_19

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…Numerous control schemes have been developed, e.g. Bang-Bang Control [119,167,168], Fuzzy Logic [149,[169][170][171], energy based [163], state feedback based [172], Sliding Mode [173], Backstepping, PID adaptive [53], Time Optimal [174], Switching [175], Neural Network [176], Prediction [177], etc. The issues of trajectory planning and optimized adaptive control was investigated in [84] for a class of WIP vehicle models.…”

Section: Classificationmentioning

confidence: 99%

A survey on underactuated robotic systems: Bio-inspiration, trajectory planning and control

et al. 2020

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Section: Classificationmentioning

confidence: 99%

A survey on underactuated robotic systems: Bio-inspiration, trajectory planning and control

et al. 2020

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“…In the literature, feedforward controller (Graichen et al, 2007), sliding mode fault-tolerant control system (Hmidi et al, 2021), neural network tracking control strategy (Ping et al, 2021a), optimal adaptive hybrid controller (Mahmoodabadi and Haghbayan, 2020), friction compensation method (Ping et al, 2021b), deep reinforcement learning control system (Manrique Escobar et al, 2020), and passivity cascade technique (Rahmani et al, 2021) have been proposed for the swing-up problem. Also, fractional-order proportional–integral–derivative (PID) (Mishra and Chandra, 2014), optimal (Chen et al, 2018; Zhang and Zhang, 2012), adaptive (Zhang et al, 2011), sliding mode (Chen et al, 2018; Nasir et al, 2010), adaptive sliding mode (Jmel et al, 2020), and robust control (Hasseni and Abdou, 2020) are the widely used control algorithms for stabilization of the IPS.…”

Section: Introductionmentioning

confidence: 99%

Design of optimized interval type-2 fuzzy logic controller based on the continuity, monotonicity, and smoothness properties for a cart-pole inverted pendulum system

Kelekçi

Yaren

Kizir

2022

Transactions of the Institute of Measurement and Control

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In this study, an interval type-2 fuzzy logic controller design method is proposed and validated on the real-time under-actuated nonlinear inverted pendulum system for swing-up and stabilization control problems. The swing-up algorithm is designed to give a faster response and the stabilization controller is designed based on continuity, monotonicity, and smoothness theorems for worst and best cases using the Mamdani fuzzy inference system in order to show the effectiveness of the proposed method. Outstanding type reduction methods are analyzed for the stabilization controller based on the design approach to determine the best type reduction algorithm for the effective and robust control performance. Real-time experiments are conducted to investigate the capability of the proposed controller in terms of adaptation performance and robustness ability. The controller also is able to handle structured and unstructured uncertainties such as measurement noise, external payload, undesirable internal/external disturbances, and parameter uncertainties. The results show that the proposed method clearly improves the control performance of the system in six experimental tasks conducted.

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“…The controller was designed to solve local minimum problem which is one of the disadvantage of Back-propagation (BP) neural networks. Zhang, An & Shao (2011) proposed an ANFIS controller based on fusion function for stability and control of DIP. The proposed method reduced dimension of the system by decreasing number of input variables thereby solving the problem of fuzzy rule explosion.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study for Position Regulation and Anti-Swing Control of Highly Non-Linear Double Inverted Pendulum (DIP) System Using Different Soft Com

Patil

Kharola

2017

International Journal of Fuzzy System Applications

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This paper presents a comparative analysis for stabilization and control of highly non-linear, complex and multi-variable Double Inverted Pendulum on cart. A Matlab-Simulink model of DIP has been built using governing mathematical equations. The objective is to control both the pendulums at vertical position while cart is free to move in horizontal direction. The control of DIP was achieved using three different soft-computing techniques namely Fuzzy logic reasoning, Neural networks (NN's) and Adaptive neuro fuzzy inference system (ANFIS). The results show that the ANFIS controller is more effective as compared to other two controllers in terms of settling time (sec), maximum overshoot (degree) and steady state error. The regression (R) and mean square error (MSE) values obtained after training of Neural network were adequate and the training error obtained in ANFIS was also optimum. All the three controllers were able to stabilize the DIP system but ANFIS control provides better results as illustrated with the help of graphs and tables.

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A New Type of Adaptive Neural Network Fuzzy Controller in the Double Inverted Pendulum System

Cited by 7 publications

References 4 publications

A survey on underactuated robotic systems: Bio-inspiration, trajectory planning and control

A survey on underactuated robotic systems: Bio-inspiration, trajectory planning and control

Design of optimized interval type-2 fuzzy logic controller based on the continuity, monotonicity, and smoothness properties for a cart-pole inverted pendulum system

A Comparative Study for Position Regulation and Anti-Swing Control of Highly Non-Linear Double Inverted Pendulum (DIP) System Using Different Soft Com

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