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Volume 13 Issue 2
Feb.  2026

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2026  >  13(2): 300-310
X. Yue, H. Zhang, J. Sun, and X. Guo, “Universal intermittent state-constrained control without feasibility condition for nonlinear systems,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 2, pp. 300–310, Feb. 2026. doi: 10.1109/JAS.2025.125357
Citation: X. Yue, H. Zhang, J. Sun, and X. Guo, “Universal intermittent state-constrained control without feasibility condition for nonlinear systems,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 2, pp. 300–310, Feb. 2026. doi: 10.1109/JAS.2025.125357
shu

Universal Intermittent State-Constrained Control Without Feasibility Condition for Nonlinear Systems

doi: 10.1109/JAS.2025.125357
Funds:  This work was supported by the Fundamental Research Funds for the Central Universities (N2404005), the National Key Research and Development Program of China (2018YFA0702200), Liaoning Revitalization Talents Program (XLYC1801005), the National Natural Science Foundation of China (U23B20118), and the Nature Science Foundation of Liaoning Province of China (2022JH25/10100008)
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    Abstract
  • State constraints in nonlinear systems are commonly pursued by resorting to barrier functions, which enforce constraints over the entire duration of system operation. We propose a universal intermittent state-constrained solution, which not only offers flexibility by activating constraints just during specific time periods of interest to the user, but also successfully accommodates different types of constraint boundaries. The innovative shifting functions are proposed to facilitate seamless transitions between constrained and unconstrained operational phases, resulting in more user-friendly design and implementation. By blending an improved shifting transformation into intermittent constraint design, we construct a universal barrier function upon the constrained states, with which our control strategy removes the limitations on constraint functions and completely obviates the feasibility conditions. Furthermore, a modified fuzzy approximator driven by the prediction error rather than the tracking error achieves decoupling of the control and estimation loops, which not only ensures the estimation performance, but also facilitates proof of stability. Finally, the effectiveness of the proposed scheme is assessed by numerical simulation.

     

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