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<p>This paper presents a prescribed-time fuzzy control algorithm for stochastic nonlinear systems with dynamic uncertainty and hysteresis nonlinearity. Firstly, an enhanced time-varying constraining function is devised to convert the prescribed-time control issue into one with delayed constraints on tracking error, thereby alleviating the restrictions on system initial conditions commonly employed in existing literature. Then, a dynamic signal mechanism is incorporated to mitigate the impact of dynamical disturbances, while an intermediate parameter is introduced into control law for adaptive compensation of hysteresis nonlinearity. Finally, by integrating the fuzzy backstepping method with It<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\hat{o}\)</EquationSource> <EquationSource Format="MATHML"><math> <mover accent="true"> <mi>o</mi> <mo stretchy="false">^</mo> </mover> </math></EquationSource> </InlineEquation> differential theory, a prescribed-time fuzzy controller is devised for stochastic systems. This controller realizes that tracking error converges to predefined range within the prescribed time, while allowing for flexible presetting of convergence time and accuracy within physical constraints. The validity of developed fuzzy control algorithm is supported via providing two illustrative examples.</p>

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Prescribed-Time Fuzzy Control for Stochastic Nonlinear Systems with Hysteresis and Dynamic Uncertainties

  • 期刊论文

  • 发表日期：

    2026年3月30日

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  • Hanzheng Ju,
  • Zhumu Fu,
  • Fazhan Tao,
  • Nan Wang

摘要

This paper presents a prescribed-time fuzzy control algorithm for stochastic nonlinear systems with dynamic uncertainty and hysteresis nonlinearity. Firstly, an enhanced time-varying constraining function is devised to convert the prescribed-time control issue into one with delayed constraints on tracking error, thereby alleviating the restrictions on system initial conditions commonly employed in existing literature. Then, a dynamic signal mechanism is incorporated to mitigate the impact of dynamical disturbances, while an intermediate parameter is introduced into control law for adaptive compensation of hysteresis nonlinearity. Finally, by integrating the fuzzy backstepping method with It \(\hat{o}\) o ^ differential theory, a prescribed-time fuzzy controller is devised for stochastic systems. This controller realizes that tracking error converges to predefined range within the prescribed time, while allowing for flexible presetting of convergence time and accuracy within physical constraints. The validity of developed fuzzy control algorithm is supported via providing two illustrative examples.