On stochastic stabilization via non-smooth control Lyapunov functions

TL;DR

This paper extends stabilization techniques using non-smooth control Lyapunov functions to stochastic systems, demonstrating practical stabilization under bounded noise with a sample-and-hold control scheme.

Contribution

It generalizes existing non-smooth control Lyapunov function methods to stochastic systems, including a new theorem for practical stabilization with sample-and-hold control.

Findings

Practical stabilization achieved under bounded noise.

Sample-and-hold control scheme effective for stochastic systems.

Framework extendable to various control schemes.

Abstract

Control Lyapunov function is a central tool in stabilization. It generalizes an abstract energy function -- a Lyapunov function -- to the case of controlled systems. It is a known fact that most control Lyapunov functions are non-smooth -- so is the case in non-holonomic systems, like wheeled robots and cars. Frameworks for stabilization using non-smooth control Lyapunov functions exist, like Dini aiming and steepest descent. This work generalizes the related results to the stochastic case. As the groundwork, sampled control scheme is chosen in which control actions are computed at discrete moments in time using discrete measurements of the system state. In such a setup, special attention should be paid to the sample-to-sample behavior of the control Lyapunov function. A particular challenge here is a random noise acting on the system. The central result of this work is a theorem that states, roughly, that if there is a, generally non-smooth, control Lyapunov function, the given stochastic dynamical system can be practically stabilized in the sample-and-hold mode meaning that the control actions are held constant within sampling time steps. A particular control method chosen is based on Moreau-Yosida regularization, in other words, inf-convolution of the control Lyapunov function, but the overall framework is extendable to further control schemes. It is assumed that the system noise be bounded almost surely, although the case of unbounded noise is briefly addressed.