Backstepping Observer for the Quasilinear Heat Equation with Linear Design Gains: Beyond Local Stability

TL;DR

This paper develops a backstepping boundary observer for a one-dimensional quasilinear heat equation, proving exponential convergence of the observation error despite nonlinearities and parameter mismatches.

Contribution

It extends backstepping observer design to quasilinear PDEs with state-dependent parameters, providing explicit stability conditions and revealing non-monotonic gain effects.

Findings

Observation error converges to zero despite parameter mismatch.

Explicit region of attraction depends on system parameters and gains.

Optimal observer gain exists, beyond which performance deteriorates.

Abstract

We consider the one-dimensional quasilinear heat equation with state-dependent heat capacity and thermal conductivity, and design a boundary-output observer based on the backstepping design for a linear heat equation with constant coefficients. Viewing the quasilinear system as a perturbation of the linear one, we establish exponential stability of the origin for the observation error dynamics in $H^1$, with an explicit region of attraction depending on the system parameters, observer gains, and the mismatch between the nonlinear diffusivity and the constant design diffusivity. Importantly, the observation error converges to zero rather than merely to a neighborhood scaling with this mismatch, even though, in contrast to backstepping-based stabilization of nonlinear PDEs, the mismatch need not decay along trajectories and may remain bounded away from zero, acting as a persistent state-dependent multiplicative perturbation. A technical challenge was to perform a sufficiently-fine Lyapunov analysis that does not yield overly conservative results such as mere boundedness of the observation error. Interestingly, while in the linear case the relationship between one of the backstepping observer gains and the convergence rate is monotonic, we show that in the nonlinear setting this is no longer the case: there may exist an optimal value of that gain, beyond which further increases deteriorate the system's performance. Such behavior cannot be predicted without our analysis: one might expect a priori the decay rate to be freely tunable at the expense of a region of attraction that shrinks to zero as the prescribed rate tends to infinity. However, our Lyapunov analysis (supported by numerical experiments) reveals that this intuition is incorrect.