Generalized efficient robust predictive control for networked interval type-2 T-S fuzzy system with adaptive
Xiaoming Tang1, Jialiang Wang1, Kun Zhao1
1The Key Laboratory of Industrial Internet of Things and Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing, China; The College of Automation and Advanced Scientific Research Institute, Chongqing University of Posts and Telecommunications, Chongqing, China.
ISA Transactions
|September 6, 2023
Summary
This study introduces a robust predictive control method for nonlinear systems, addressing network issues like packet loss and disturbances using an adaptive event-triggered scheme. The approach enhances control performance while reducing data transmission burdens.
Area of Science:
- Control Systems Engineering
- Fuzzy Logic Systems
- Networked Control Systems
Background:
- Nonlinear systems require advanced control strategies for stability and performance.
- Networked control systems face challenges from packet dropout and bounded disturbances.
- Adaptive event-triggered schemes (AETS) can optimize data transmission in control systems.
Purpose of the Study:
- To develop a Generalized Efficient Robust Predictive Control (GERPC) for interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy systems.
- To incorporate packet dropout and AETS into the control framework.
- To ensure system stability and improve control performance under disturbances.
Main Methods:
- Modeling packet dropout using a Bernoulli process and implementing an AETS for adaptive data transmission.
- Establishing a unified IT2 T-S fuzzy control model based on GERPC.
- Employing quadratic boundedness (QB) for stability analysis and convex Linear Matrix Inequality (LMI) reformulation for optimization.
Main Results:
- A GERPC strategy was designed to achieve a larger feasible region and reduced online computation.
- The proposed controller ensures closed-loop system stability despite bounded disturbances.
- Simulation experiments validated the effectiveness of the GERPC algorithm and the designed controller.
Conclusions:
- The developed GERPC strategy effectively manages nonlinear IT2 T-S fuzzy systems in networked environments.
- The integration of AETS significantly reduces network load while maintaining robust control.
- The controller offers a balance between computational efficiency and reliable control performance.
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