Fuzzy Controller Design for Discrete-time T-S Fuzzy Systems with Partially Unknown Membership Functions

Guo-yi, Liu; Zhou, Juan

doi:10.1007/s12555-021-0900-8

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Other1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Adaptive sliding mode control of robotic manipulator based on reinforcement learning

Ren,

Chen,

Miao

et al. 2024

Asian Journal of Control

View full text Add to dashboard Cite

Robotic manipulators usually exhibit time‐varying, nonlinear, and coupled dynamics due to the parameter perturbations, disturbances, and other uncertainties. Traditional control algorithms typically do not possess parameters' self‐adaptive learning ability, limiting the tracking performance of the robot. In order to address these issues, an adaptive sliding mode control method based on reinforcement learning (ASMRL) is proposed in this paper, where a proportional–integral sliding mode (PISM) controller is used to address the nonlinear problem in the system, and deep deterministic policy gradient (DDPG) agent is adopted to conduct the parameters' learning of the PISM controller using its adaptive characteristics and the autonomous learning ability. The simulation results illustrate that the proposed method can effectively achieve better tracking performance compared with two other control methods, demonstrating the effectiveness of the proposed approach.

show abstract