We aim to find the optimal control against the worst-case copulae in a sequence of shrinking uncertainty sets which are generated from continuously observing the data. Then, we use a stochastic gradient descent ascent algorithm to numerically handle the corresponding high dimensional dynamic inf-sup...
Then, the idea of neural dynamic programming is adopted to perform the main controller design task by building and training a critic network. Finally, the effectiveness of the present adaptive robust control strategy is illustrated via a simulation example....
Zhong-Ping JIANG is a Professor ofElectrical and Computer Engineering at New York University, a recipient of theDistinguished Overseas Chinese Scholar Award from the NSF of China. Prof. Jiangis a Fellow of the IEEE and a Fellow of the IFAC.0...
来源期刊 Circuits and Systems II: Express Briefs, IEEE Transactions on 研究点推荐 cooperative output regulation Multi-Agent Systems Data-Driven Cooperative Output Regulation Robust Adaptive Dynamic Programming cyclic-small-gain 站内活动 0关于我们 百度学术集成海量学术资源,融合人工智能、深度学习、大数据...
gof test and develop a dynamic programming approach to this particular problem. the authors establish conditions for asymptotic convergence for this problem but do not discuss finite sample guarantees. by contrast, we provide a systematic study of gof testing and data-driven dro. by connecting these...
Data-driven-based sliding-mode dynamic event-triggered control of unknown nonlinear systems via reinforcement learning 2024, Neurocomputing Citation Excerpt : Although these control strategies can achieve satisfactory performances, the actor network generated approximation errors in the process of implementation...
1.2.2. Data-Driven FD Different data-driven methods have been proposed for the FD problem. In [36], the fuzzy inference system in conjunction with thresholder was proposed for the FD of DC motors. In [37], 4 different Wiener models were ensembled for the fault analysis of an industrial ...
Motivated by a ride service platform, this paper investigates a short-term vehicle rebalancing problem under demand uncertainty in the presence of contextual data. We deploy a novel data-driven robust optimization approach that takes a direct path from “Data” to “Decision” instead of the ...
Chance-constrained programming, data-driven uncertainty, risk hedging, and artificial intelligence are some of the popular approaches, adopted to enhance the computational efficiency of the RO methods (Inapakurthi, Pantula, Miriyala and Mitra, 2020; Sharma, Pantula, Miriyala, & Mitra, 2021). In ...
It also comes with powerful object-relational mappers for data abstraction and modeling that are just as lightweight as the framework. No configuration needed.That's not all. F3 is packaged with other optional plug-ins that extend its capabilities:-...