Safety-Aware Learning-Based Control of Systems with Uncertainty Dependent Constraints
Safety-Aware Learning-Based Control of Systems with Uncertainty Dependent Constraints
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DOI：
10.23919/acc55779.2023.10156490
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Jafar Abbaszadeh Chekan;Cédric Langbort
中科院分区：
文献类型：
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作者：
Jafar Abbaszadeh Chekan;Cédric Langbort
在本文中，我们解决的问题，安全稳定的原始（部分）未知的系统，同时确保它不会留下一个规定的“安全集”，其结构本身取决于系统的动态的未知部分。为了这个目的，我们采用了一种流行的方法的基础上控制李雅普诺夫函数（CLF），控制障碍函数（CBF），高斯过程（建立信任集周围的未知项），这已被证明是成功的已知安全设置。然而，与上述的安全集结构，我们见证了高阶项的引入，估计和有界的概率很高，只使用系统状态测量。在本文中，我们建立在最近的文献高斯过程（GP）和再生内核来应对挑战，并展示如何修改CLF-CBF为基础的方法相应地获得安全保证。为了克服这些条件在连续域上的验证的棘手性，我们应用离散化的状态空间，并使用Lipschitz连续性的动态推导出等效的CLF和CBF证书在离散状态空间。最后，我们讨论了使用派生证书的控制设计目标的策略。
In this paper, we tackle the problem of safely stabilizing an originally (partially) unknown system while ensuring that it does not leave a prescribed ’safe set’ whose structure itself depends on the unknown part of the system’s dynamics. For this aim, we apply a popular approach based on control Lyapunov functions (CLF), control barrier functions (CBF), and Gaussian processes (to build confidence set around the unknown term), which has proved successful in the known-safe set setting. However, with the mentioned safety set structure, we witness the introduction of higher-order terms to be estimated and bounded with high probability using only system state measurements. In this paper, we build on the recent literature on Gaussian Processes (GPs) and reproducing kernels to address the challenge and show how to modify the CLF-CBF-based approach correspondingly to obtain safety guarantees. To overcome the intractability of verification of these conditions on the continuous domain, we apply discretization of the state space and use Lipschitz continuity properties of dynamics to derive equivalent CLF and CBF certificates in discrete state space. Finally, we discuss the strategy for the control design aim using the derived certificates.