Adaptive output-feedback finite-time stabilisation of stochastic non-linear systems with application to a two-stage chemical reactor

• Published in: IET control theory & applications Vol. 13; no. 4; pp. 534 - 542
• Main Authors: Sui, Shuai, Philip Chen, C.L
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 05.03.2019
• Subjects: adaptive backstepping intelligent control; adaptive control; adaptive output‐feedback finite‐time stabilisation; chemical reactors; control nonlinearities; control system synthesis; controller design; differential equations; feedback; fuzzy control; fuzzy logic systems; fuzzy state observer; nonlinear control systems; observers; probability; Research Article; single‐input single‐output system; SISO; stability; stochastic differential equation; stochastic processes; stochastic systems; two‐stage chemical reactor; two‐stage continuous stirred tank reactor process; uncertain stochastic nonlinear system; uncertain systems; unmeasured system variables; chemical reactors; controller design; differential equations; feedback; fuzzy control; SISO; control nonlinearities; stochastic systems; single-input single-output system; adaptive backstepping intelligent control; stochastic processes; stability; uncertain systems; probability; adaptive output-feedback finite-time stabilisation; two-stage chemical reactor; control system synthesis; adaptive control; fuzzy state observer; two-stage continuous stirred tank reactor process; uncertain stochastic nonlinear system; nonlinear control systems; stochastic differential equation; fuzzy logic systems; unmeasured system variables; observers
• ISSN: 1751-8644; 1751-8652; 1751-8652
• DOI: 10.1049/iet-cta.2018.5431

In this study, the authors investigate the problem of the stochastically finite-time stability analysis and control design of an adaptive single-input and single-output (SISO) uncertain stochastic non-linear system via establishing a new stability criterion. Owing to the unknown dynamics and unmeasured system variables, fuzzy logic systems and a fuzzy state observer are constructed and applied to identify the stochastic system, respectively. Combining the finite-time definition, stochastic differential equation and $\hat o$o^ formula, a novel stochastically finite-time stability theorem is raised in this study. By utilising the novel criterion and adaptive backstepping intelligent control, a stochastically finite-time control method is proposed. It is illustrated that the controlled stochastic system is semi-global finite-time stable in probability and behaves excellent convergence. The simulation results of a two-stage continuous stirred tank reactor process reveal the validity and efficiency.