In this article, we present a hierarchical multirate control architecture for nonlinear autonomous systems operating in partially observable environments. Control objectives are expressed using syntactically co-safe linear temporal logic (LTL) specifications and the nonlinear system is subject to state and input constraints. At the highest level of abstraction, we model the system–environment interaction using a discrete mixed observable Markov decision process, where the environment states are partially observed. The high-level control policy is used to update the constraint sets and cost function of a model predictive controller (MPC) which plans a reference trajectory. Afterward, the MPC planned trajectory is fed to a low-level high-frequency tracking controller, which leverages control barrier functions to guarantee bounded tracking errors. Our strategy is based on model abstractions of increasing complexity and layers running at different frequencies. We show that the proposed hierarchical multirate control architecture maximizes the probability of satisfying the high-level specifications, while guaranteeing state and input constraint satisfaction. Finally, we tested the proposed strategy in simulations and experiments on examples inspired by the Mars exploration mission, where only partial environment observations are available.

中文翻译：

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统一多速率控制：从低级驱动到高级规划


在本文中，我们提出了一种用于在部分可观测环境中运行的非线性自治系统的分层多速率控制架构。控制目标使用语法上安全的线性时序逻辑 (LTL) 规范来表达，并且非线性系统受状态和输入约束。在最高的抽象层次上，我们使用离散混合可观察马尔可夫决策过程对系统与环境的交互进行建模，其中环境状态是部分观察到的。高级控制策略用于更新规划参考轨迹的模型预测控制器（MPC）的约束集和成本函数。随后，MPC 规划的轨迹被馈送到低级高频跟踪控制器，该控制器利用控制屏障函数来保证有界跟踪误差。我们的策略基于不断增加的复杂性和以不同频率运行的层的模型抽象。我们表明，所提出的分层多速率控制架构最大限度地提高了满足高级规范的概率，同时保证状态和输入约束满足。最后，我们在模拟和实验中测试了所提出的策略，这些例子受到火星探索任务的启发，其中只有部分环境观测可用。