Stochastic modeling in hydrology and reliability theory, and optimal control of dynamical systems

水文学和可靠性理论中的随机建模以及动力系统的最优控制

• 批准号: RGPIN-2014-05273
• 负责人: Lefebvre, Mario
• 金额: $ 1.75万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679996
• 关键词: Stochastic; modeling; hydrology; reliability; theory

We mainly study three interrelated problems for dynamical systems in engineering.**1) Filtered renewal processes in hydrology. We have successfully used filtered Poisson processes to model river flows. However, our model implies that the time between events that significantly increase the river flow is a random variable having an exponential distribution. In reality, this hypothesis is generally false. Consequently, in order to improve the hydrological forecasts obtained from this model, we will generalize it by assuming that the flow evolves according to a filtered renewal process. We will have to derive new formulas to estimate the river flow. Furthermore, with an appropriate response function, filtered renewal processes are related to models used in queuing theory. We will make use of this fact to propose models that should yield even more accurate forecasts of river flows. In the case when analytical expressions cannot be derived, we will resort to simulations. The results that we will obtain are of great importance to dam managers, in particular.**2) Reliability. A basic problem in reliability theory is the determination of the distribution of the lifetime of a given product. To do so realistically, when the system is not repairable, the model used must be such that the remaining lifetime decreases with time. We want to use two-dimensional diffusion processes for this remaining lifetime, defined in such a way that the first component of the vectors is a deterministic decreasing function of the second component, which is a diffusion process. We will also consider other models for which the remaining lifetime decreases with time, such as conditioned one-dimensional diffusion processes and processes for which there is a reflecting boundary. We want to determine, in particular, the mean time required to reach a given boundary, that is, the mean-time-to-failure (MTTF). To do so, we must solve partial differential equations under the appropriate boundary conditions. Companies are interested in the MTTF in order to estimate the cost of the warranties they offer on their products. In the case of repairable systems, filtered renewal processes similar to the ones that we will use in hydrology, but with a different response function, can be proposed.**3) Stochastic optimal control. It is sometimes possible to obtain the optimal control of continuous stochastic processes until a random final time by making use of a theorem due to Whittle that enables us to express this optimal control in terms of a mathematical expectation computed for the corresponding uncontrolled processes. However, in order to apply Whittle's theorem, a certain relation between the control and noise matrices must be satisfied, which is rarely true in the most interesting applications. We want to solve this type of problems, sometimes called LQG homing, in the case when the relation in question is not satisfied. This entails solving the appropriate dynamic programming equation. Depending on the sign of a parameter in the cost function, the aim can be to minimize the time spent by the controlled process in the continuation region, or to maximize survival time instead. An application of these problems consists in computing the control that enables an aircraft to land optimally. Moreover, we want to extend LQG homing problems to the discrete-time case, which is often more realistic for the applications considered. Then, we will have to deal with nonlinear difference equations. Finally, we will also consider the problem of optimally controlling the filtered renewal processes used in hydrology and in reliability theory. In particular, we will determine the control that enables a dam manager to optimally release some water when the risk of flooding becomes too high.

我们主要研究工程动力系统的三个相互关联的问题。**1）水文学中的过滤更新过程。我们已经成功地使用过滤泊松过程来模拟河流流量。然而，我们的模型意味着显着增加河流流量的事件之间的时间是一个具有指数分布的随机变量。事实上，这个假设通常是错误的。因此，为了改进从该模型获得的水文预报，我们将通过假设流量根据过滤更新过程演变来对其进行概括。我们必须推导出新的公式来估算河流流量。此外，通过适当的响应函数，过滤更新过程与排队论中使用的模型相关。我们将利用这一事实来提出模型，以产生更准确的河流流量预测。当无法推导出解析表达式时，我们将求助于模拟。我们将获得的结果对于大坝管理者尤其重要。**2) 可靠性。可靠性理论的一个基本问题是确定给定产品的寿命分布。为了现实地做到这一点，当系统不可修复时，所使用的模型必须使得剩余寿命随着时间的推移而减少。我们希望对这个剩余寿命使用二维扩散过程，其定义方式是向量的第一个分量是第二个分量的确定性递减函数，第二个分量是扩散过程。我们还将考虑剩余寿命随时间减少的其他模型，例如条件一维扩散过程和存在反射边界的过程。我们特别想要确定达到给定边界所需的平均时间，即平均故障时间 (MTTF)。为此，我们必须在适当的边界条件下求解偏微分方程。公司对 MTTF 感兴趣，以便估算他们为其产品提供的保修成本。在可修复系统的情况下，可以提出类似于我们将在水文学中使用的过滤更新过程，但具有不同的响应函数。**3）随机最优控制。有时可以通过利用惠特尔定理获得连续随机过程的最优控制，直到随机的最终时间，该定理使我们能够用为相应的不受控过程计算的数学期望来表达这种最优控制。然而，为了应用惠特尔定理，必须满足控制矩阵和噪声矩阵之间的某种关系，但这在最有趣的应用中很少成立。我们希望在不满足所讨论的关系的情况下解决此类问题，有时称为 LQG 归零。这需要求解适当的动态规划方程。根据成本函数中参数的符号，目标可以是最小化受控过程在连续区域中花费的时间，或者最大化生存时间。这些问题的一个应用在于计算使飞机能够最佳着陆的控制。此外，我们希望将 LQG 归航问题扩展到离散时间情况，这对于所考虑的应用程序来说通常更现实。然后，我们将不得不处理非线性差分方程。最后，我们还将考虑水文学和可靠性理论中使用的过滤更新过程的最佳控制问题。特别是，我们将确定控制措施，使大坝管理者能够在洪水风险过高时以最佳方式释放一些水。