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This article is devoted to the stabilization via adaptive feedback for a class of uncertain coupled reaction–diffusion dynamics in the actuation path of an ordinary differential equation (ODE) system. The system under investigation, a class of coupled parabolic partial differential equation (PDE)–ODE systems, is more representative since the dynamics in actuation path (i.e., the PDE subsystem) are coupled rather than uncoupled parabolic equations and hence includes those in the related literature as special cases. Moreover, serious parametric uncertainties are present in both PDE and ODE subsystems, which bring obstacles to the traditional methods on this issue. To solve the control problem, an infinite-dimensional backstepping transformation with time-varying matrix-valued kernel functions is adopted to change the original system into a new one. Then, an adaptive state-feedback controller is designed for the new system, which guarantees that all the closed-loop system states are bounded while regulating the original system states to zero. Finally, a simulation example is provided to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper is devoted to the analysis methods/tools to stochastic convergence and stochastic adaptive output-feedback control. As the first contribution, a general stochastic convergence theorem is proposed for stochastic nonlinear systems. The theorem doesn't necessarily involve a positive-definite function of the system states with negative-semidefinite infinitesimal, essentially different from stochastic LaSalle's theorem (see e.g., [1]), and hence can provide more opportunities to achieve stochastic convergence. Moreover, as a direct extension of the convergence theorem, a general version of stochastic Barb ă lat's lemma is obtained, which requires the concerned stochastic process to be almost surely integrable, rather than absolutely integrable in the sense of expectation, unlike in [2]. As the second contribution, supported by the general stochastic convergence theorem, an adaptive output-feedback control strategy is established for the global stabilization of a class of stochastic nonlinear systems with severe parametric uncertainties coupled to unmeasurable states. Its feasibility analysis takes substantial effort, and is largely based on the general stochastic convergence theorem. Particularly, for the resulting closed-loop system, certain stochastic boundedness and integrability are shown by the celebrated nonnegative semimartingale convergence theorem, and furthermore, the desired stochastic convergence is achieved via the general stochastic convergence theorem. [ABSTRACT FROM PUBLISHER]