Adaptive Takagi-Sugeno fuzzy model and model predictive control of pneumatic artificial muscles

Xia, Xiuze; Cheng, Long

doi:10.1007/s11431-021-1887-6

Cited by 23 publications

(6 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, DMPC reduces the complexity of optimization problems and improves work efficiency. In addition, model predictive control can not only deal with multiple constraints, but also calculate the control input sequence through online solution [ 22 , 23 ]. The prediction and evaluation results are mainly nonlinear models based on machine learning algorithms.…”

Section: Related Workmentioning

confidence: 99%

Distributed Intelligent Learning and Decision Model Based on Logic Predictive Control

Zhou

Zhang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

By the method of documentation and logical analysis, based on the data, based on logic and based on the knowledge of three kinds of artificial intelligence in the sports education, the intelligent learning system feedback delay are studied, combined with mobile communication which led to the artificial intelligence online sports games teaching, pattern recognition, and virtual technology combined with innovative teaching interaction and experience. Promoting the development of green PE teaching machine learning can identify the types of PE activities and realize efficient PE learning diagnosis. Intelligent decision support system can identify sports talents and improve the effect of personalized PE teaching evaluation. From the perspective of psychological development and education, the key problems to be solved in the integration of artificial intelligence and physical education are examined. Then, the consistent model predictive control for feedback delay of nonlinear sports learning multiagent system with network induced delay and random communication protocol is studied. Under the communication waiting mechanism designed, each agent has a certain tolerance of delay, and this tolerance can be determined by ensuring the stability of the system. At the same time, a random communication protocol is designed to ensure the ordered communication of the multiagent system. Finally, the effectiveness of the proposed algorithm is verified by numerical simulation. To solve the channel competition access problem of the sports intelligent learning system with special structure feedback delay model predictive control, a dual channel awareness scheduling strategy under the model predictive control framework was proposed, and the distributed threshold strategy of sensors and the priority threshold strategy of controllers were designed. It is proved that the sensor will eventually work at Nash equilibrium point under the policy updating mechanism, and the priority threshold strategy of the controller is better than the traditional independent and identically distributed access strategy. By avoiding the data transmission when the channel status is poor, the channel access of the system is efficient and saves energy.

show abstract

Section: Related Workmentioning

confidence: 99%

Distributed Intelligent Learning and Decision Model Based on Logic Predictive Control

Zhou

Zhang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…One particularly promising and widely studied control methodology for addressing the challenges posed by the inverted pendulum system is the use of Takagi–Sugeno (T–S) fuzzy control 14 , 15 . The T–S fuzzy control approach 16 – 18 combines the flexibility of fuzzy logic with the power of local linear models to provide a versatile and adaptive control framework. By dividing the system into multiple local linear models associated with fuzzy if-then rules, T–S fuzzy control offers a systematic means of capturing the nonlinearity and uncertainty present in the inverted pendulum dynamics.…”

Section: Introductionmentioning

confidence: 99%

Enhancing stability control of inverted pendulum using Takagi–Sugeno fuzzy model with disturbance rejection and input–output constraints

Nguyen,

Dong,

BUI

2023

Sci Rep

View full text Add to dashboard Cite

The Takagi–Sugeno (T–S) fuzzy model is a versatile approach widely used in system control, often in combination with other strategies. This paper addresses key control challenges linked to the T–S system and presents important considerations to ensure its successful application using the Lyapunov theorem. One crucial aspect is determining the optimal number of premise variables and selecting accurate fuzzy rules for the T–S model. Additionally, the theorem based on Linear Matrix Inequality (LMI) is developed to enable effective disturbance rejection. To enhance stability control, constraints are imposed on the output angle and control input of a rotary inverted pendulum (RIP). By integrating T–S fuzzy control, disturbance rejection, and input/output constraints, robust stability in controlling the RIP is achieved. Extensive simulations are performed to showcase the efficiency of the suggested method, and the simulation results are thoroughly discussed and analyzed to verify the efficacy of the control method.

show abstract

“… 32 proposed a robust iterative learning control algorithm to address a PAM system’s uncertainties and state constraints. Fuzzy control in combination with fractional PID control 25 , with sliding mode control 33 , and with model predict control 34 are proposed for control of the PAM system. In these articles, fuzzy logic plays a role in adjusting the control parameters.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy sliding mode control of an actuator powered by two opposing pneumatic artificial muscles

Duong

Pham

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Pneumatic artificial muscle (PAM) is a potential actuator in human–robot interaction systems, especially rehabilitation systems. However, PAM is a nonlinear actuator with uncertainty and a considerable delay in characteristics, making control challenging. This study presents a discrete-time sliding mode control approach combined with the adaptive fuzzy algorithm (AFSMC) to deal with the unknown disturbance of the PAM-based actuator. The developed fuzzy logic system has parameter vectors of the component rules that are automatically updated by an adaptive law. Consequently, the developed fuzzy logic system can reasonably approximate the system disturbance. When operating the PAM-based system in multi-scenario studies, experimental results confirm the efficiency of the proposed strategy.

show abstract

Adaptive Takagi-Sugeno fuzzy model and model predictive control of pneumatic artificial muscles

Cited by 23 publications

References 41 publications

Distributed Intelligent Learning and Decision Model Based on Logic Predictive Control

Distributed Intelligent Learning and Decision Model Based on Logic Predictive Control

Enhancing stability control of inverted pendulum using Takagi–Sugeno fuzzy model with disturbance rejection and input–output constraints

Adaptive fuzzy sliding mode control of an actuator powered by two opposing pneumatic artificial muscles

Contact Info

Product

Resources

About