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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Wenhai Qi

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Aug 2026

Reinforcement Learning-Based Optimization for Interval Type-2 Fuzzy Unknown Nonlinear System: An Zero-Sum Control Scheme.

A novel mode-free reinforcement learning (RL) algorithm is proposed for the optimal control of unknown nonlinear system presented by the interval type-2 fuzzy (IT2F) model. The optimal control is converted into a zero-sum game, where the control input and the external disturbance are the opposing competitors. Based on the RL method, a model-based policy iteration (PI) algorithm is constructed to solve the fuzzy stochastic coupled algebraic Riccati equations of nonlinear system. Considering that the dynamic parameter is difficult to obtain completely in real systems, a model-free fuzzy control algorithm is designed under the framework of the RL algorithm. An optimal control of IT2F system is realized without parameter information, only requiring the state and input information. Furthermore, the asymptotic stability with $H_{\infty } $ performance index is ensured by the Lyapunov function. Finally, the effectiveness is tested by the semi-car active suspension model (SCASM).

Runkun Li, Wen-Hai Qi, Guangdeng Zong et al. · 0 citations
Aug 2026

SMC for Stirred Tank Reactor Model: Hybrid Systems With Bi-Boundary Sojourn Time.

This work investigates sliding mode control (SMC) for stirred tank reactor (STR) under semi-Markov switching with bi-boundary sojourn time (ST). Compared with traditional discrete hybrid systems, both the upper and lower bounds of the ST are considered for each mode, providing a more accurate characterization of the system than the upper bound. Based on the statistical properties of the semi-Markov kernel (SMK), the SMK is assumed to be partly known. Owing to the limited research on SMC for discrete hybrid systems with semi-Markov switching and bi-boundary ST, the main contribution of this work is the development of the SMC law that guarantees the reachability of the quasi-sliding mode (QSM), together with a linear matrix inequality-based framework that accommodates partly known SMK information. The proposed SMC law drives the system states to a prespecified sliding region while effectively compensating for parameter uncertainties. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed control method.

Wenhai Qi, Feiyue Shen, Guangdeng Zong et al. · 0 citations

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