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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Bayesian Nonparametric Adaptive Control Using Gaussian Processes
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Bayesian Nonparametric Adaptive Control Using Gaussian Processes

机译：使用高斯过程的贝叶斯非参数自适应控制

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摘要

Most current model reference adaptive control (MRAC) methods rely on parametric adaptive elements, in which the number of parameters of the adaptive element are fixed , often through expert judgment. An example of such an adaptive element is radial basis function networks (RBFNs), with RBF centers preallocated based on the expected operating domain. If the system operates outside of the expected operating domain, this adaptive element can become noneffective in capturing and canceling the uncertainty, thus rendering the adaptive controller only semiglobal in nature. This paper investigates a Gaussian process-based Bayesian MRAC architecture (GP-MRAC), which leverages the power and flexibility of GP Bayesian nonparametric models of uncertainty. The GP-MRAC does not require the centers to be preallocated, can inherently handle measurement noise, and enables MRAC to handle a broader set of uncertainties, including those that are defined as distributions over functions. We use stochastic stability arguments to show that GP-MRAC guarantees good closed-loop performance with no prior domain knowledge of the uncertainty. Online implementable GP inference methods are compared in numerical simulations against RBFN-MRAC with preallocated centers and are shown to provide better tracking and improved long-term learning.
机译：当前大多数模型参考自适应控制（MRAC）方法都依赖于参数自适应元素，其中自适应元素的参数数量是固定的，通常是通过专家判断来确定的。这种自适应元件的一个示例是径向基函数网络（RBFN），其中RBF中心基于预期的操作域进行了预分配。如果系统在预期的操作域之外操作，则该自适应元件在捕获和消除不确定性方面可能变得无效，从而使自适应控制器本质上仅是半全局的。本文研究了基于高斯过程的贝叶斯MRAC体系结构（GP-MRAC），该体系结构利用了GP贝叶斯非参数不确定性模型的强大功能和灵活性。 GP-MRAC不需要预先分配中心，可以固有地处理测量噪声，并使MRAC可以处理更广泛的不确定性，包括那些定义为功能分布的不确定性。我们使用随机稳定性参数来表明GP-MRAC可以确保良好的闭环性能，而无需事先了解不确定性。在数值模拟中，将在线可实施的GP推理方法与具有预分配中心的RBFN-MRAC进行了比较，结果显示该方法可提供更好的跟踪和改进的长期学习。

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