An optimistic planning algorithm for switched discrete-time LQR

TL;DR

TROOP is a tree-based online planning algorithm for switched discrete-time linear quadratic regulators that balances computational effort with control performance, ensuring near-optimality and stability.

Contribution

The paper introduces TROOP, a novel A*-inspired online planner for switched LQR systems that efficiently balances computation and control quality.

Findings

TROOP achieves near-optimal control with reduced computation.

The algorithm guarantees stability of the closed-loop system.

Numerical simulations demonstrate effective trade-offs between computation and performance.

Abstract

We introduce TROOP, a tree-based Riccati optimistic online planner, that is designed to generate near-optimal control laws for discrete-time switched linear systems with switched quadratic costs. The key challenge that we address is balancing computational resources against control performance, which is important as constructing near-optimal inputs often requires substantial amount of computations. TROOP addresses this trade-off by adopting an online best-first search strategy inspired by A*, allowing for efficient estimates of the optimal value function. The control laws obtained guarantee both near-optimality and stability properties for the closed-loop system. These properties depend on the planning depth, which determines how far into the future the algorithm explores and is closely related to the amount of computations. TROOP thus strikes a balance between computational efficiency and control performance, which is illustrated by numerical simulations on an example.