Zhendong Shao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Zhendong Shao has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Xiaomiao Li
Yanshan University
2 shared publications - Zhiyong Bao
Yanshan University
2 shared publications - Kai Ma
Yanshan University
1 shared publication - Peng Qi
Department of Control Science and Engineering, College of Electronics and Information Engineering, and Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China
1 shared publication - Vladimir Terzija
Newcastle University
1 shared publication
Latest Publications
- 1 June 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Automation Science and Engineering
- Vol. 23 (15455955) , 10465-10477
Reinforcement Learning-Based Membership Function Optimization and Non-Fragile Control for IT2 Polynomial Fuzzy Switched Positive Systems
This paper proposes a reinforcement learning-based membership function optimization and embedding (RL-MFOE) control scheme for interval type-2 (IT2) polynomial fuzzy switched positive systems. A non-fragile static output feedback controller is designed to handle parameter uncertainties and external disturbances, guaranteeing a prescribed L1-gain performance. A matrix factorization technique is introduced to resolve the non-convex coupling between unknown positive vectors and controller gains. In the proposed scheme, a reinforcement learning algorithm tunes the parameters of the IT2 fuzzy membership functions (MFs) in a data-driven manner, where the decay rate serves as the reward signal guiding the optimization. The optimized MFs are expressed in polynomial form and incorporated into sum-of-squares (SOS) stability conditions via relaxation matrices, which reduces the conservatism in stability analysis, facilitates higher decay rates, and in turn allows stricter mode-dependent average dwell time (MDADT) bounds. By iteratively updating the MF parameters, the scheme satisfies the prescribed L1-gain performance and supports higher switching frequencies while preserving system positivity and stability. Two simulation examples validate the effectiveness of the proposed methodology.
- 1 June 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Automation Science and Engineering
- Vol. 23 (15455955) , 10465-10477
Reinforcement Learning-Based Membership Function Optimization and Non-Fragile Control for IT2 Polynomial Fuzzy Switched Positive Systems
This paper proposes a reinforcement learning-based membership function optimization and embedding (RL-MFOE) control scheme for interval type-2 (IT2) polynomial fuzzy switched positive systems. A non-fragile static output feedback controller is designed to handle parameter uncertainties and external disturbances, guaranteeing a prescribed L1-gain performance. A matrix factorization technique is introduced to resolve the non-convex coupling between unknown positive vectors and controller gains. In the proposed scheme, a reinforcement learning algorithm tunes the parameters of the IT2 fuzzy membership functions (MFs) in a data-driven manner, where the decay rate serves as the reward signal guiding the optimization. The optimized MFs are expressed in polynomial form and incorporated into sum-of-squares (SOS) stability conditions via relaxation matrices, which reduces the conservatism in stability analysis, facilitates higher decay rates, and in turn allows stricter mode-dependent average dwell time (MDADT) bounds. By iteratively updating the MF parameters, the scheme satisfies the prescribed L1-gain performance and supports higher switching frequencies while preserving system positivity and stability. Two simulation examples validate the effectiveness of the proposed methodology.