Finite Horizon Density Steering for Multi-input State Feedback Linearizable Systems

TL;DR

This paper develops a method for designing feedback controllers that steer the probability density of states in multi-input linearizable systems, minimizing control effort and extending Schrödinger bridge theory.

Contribution

It introduces a novel approach to density steering in nonlinear systems by exploiting structural nonlinearities and extends Schrödinger bridge theory to feedback linearizable dynamics.

Findings

Controllers successfully steer density between prescribed shapes

Minimized expected control effort demonstrated

Extended Schrödinger bridge framework for nonlinear systems

Abstract

In this paper, we study the feedback synthesis problem for steering the joint state density or ensemble subject to multi-input state feedback linearizable dynamics. This problem is of interest to many practical applications including that of dynamically shaping a robotic swarm. Our results here show that it is possible to exploit the structural nonlinearities to derive the feedback controllers steering the joint density from a prescribed shape to another while minimizing the expected control effort to do so. The developments herein build on our previous work, and extend the theory of the Schr\"{o}dinger bridge problem subject to feedback linearizable dynamics.