Modular Design of Strict Control Lyapunov Functions for Global Stabilization of the Unicycle in Polar Coordinates
Velimir Todorovski, Kwang Hak Kim, Miroslav Krstic
TL;DR
This work develops a modular framework for globally stabilizing a unicycle in polar coordinates, circumventing Cartesian Brockett obstructions by combining forward-velocity decoupling with angular control via backstepping and passivity. It constructs multiple composite control Lyapunov functions and barrier Lyapunov functions to guarantee global asymptotic stability or almost-global stabilization, with distinct controllers (GloBa, BAR-FLi, BoLSA, BAgAl) offering various region-of-attraction properties and barrier-enforced safety. The designs yield explicit convergence guarantees and CLFs, enabling systematic extension and barrier-based safety, with a companion paper extending to inverse-optimal redesigns. The results provide practical, modular designs for smooth stabilization and safe parking maneuvers of unicycle-like vehicles in polar coordinates, highlighting the trade-offs imposed by topology and barrier constraints.
Abstract
Since the mid-1990s, it has been known that, unlike in Cartesian form where Brockett's condition rules out static feedback stabilization, the unicycle is globally asymptotically stabilizable by smooth feedback in polar coordinates. In this note, we introduce a modular framework for designing smooth feedback laws that achieve global asymptotic stabilization in polar coordinates. These laws are bidirectional, enabling efficient parking maneuvers, and are paired with families of strict control Lyapunov functions (CLFs) constructed in a modular fashion. The resulting CLFs guarantee global asymptotic stability with explicit convergence rates and include barrier variants that yield "almost global" stabilization, excluding only zero-measure subsets of the rotation manifolds. The strictness of the CLFs is further leveraged in our companion paper, where we develop inverse-optimal redesigns with meaningful cost functions and infinite gain margins.
