arXiv subjects

Yushuai Li

Publications and source records attributed to Yushuai Li.

26 records · Page 2Linked to original sources

arXiv2025

Ensemble Control for Stochastic Systems with Asymmetric Laplace Noises

This paper presents an adaptive ensemble control for stochastic systems subject to asymmetric noises and outliers. Asymmetric noises skew system observations, and outliers with large amplitude deteriorate the observations even further. Such disturbances induce poor system estimation and degraded stochastic system control. In this work, we model the asymmetric noises and outliers by mixed asymmetric Laplace distributions (ALDs), and propose an optimal control for stochastic systems with mixed ALD noises. Particularly, we segregate the system disturbed by mixed ALD noises into subsystems, each of which is subject to a specific ALD noise. For each subsystem, we design an iterative quantile filter (IQF) to estimate the system parameters using system observations. With the estimated parameters by IQF, we derive the certainty equivalence (CE) control law for each subsystem. Then we use the Bayesian approach to ensemble the subsystem CE controllers, with each of the controllers weighted by their posterior probability. We finalize our control law as the weighted sum of the control signals by the sub-system CE controllers. To demonstrate our approach, we conduct numerical simulations and Monte Carlo analyses. The results show improved tracking performance by our approach for skew noises and its robustness to outliers, compared with single ALD based and RLS-based control policy.

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arXiv2024

Active learning for anti-disturbance dual control of unknown nonlinear systems

This work concerns the control of unknown nonlinear systems corrupted by disturbances. For such systems, we propose an anti-disturbance dual control approach with active learning of the disturbances. Our approach holds the dual property of handling the two tasks simultaneously and iteratively: (i) learn the disturbances affecting the system and (ii) drive the system output towards a reference trajectory. Particularly, we model nonlinear system dynamics using a specialized neural network (SNN). This SNN formulates the disturbances via the designed additive and multiplicative disturbance components. We consider both additive and multiplicative disturbances for precise description and recognition of disturbance profile. We achieve the disturbance recognition in the SNN via the design of a Bayesian-based active learning approach, which allows the disturbance learning to be decoupled from the control law derivation. Such a decoupling contributes to the control robustness in the existence of varying and abrupt disturbances. We derive the dual control law based on the active learning of the SNN, and validate our approach via one-time and Monte Carlo simulations. The results demonstrate a fast disturbance recognition by our method in real-time and the robustness of control of unknown systems with abrupt disturbances. We evaluate our approach on the speed control of high-speed train, and the results manifest efficient control of the train speed with disturbance resilience, without prior knowledge about the train dynamics and the disturbances imposed on the train. We openly released the code for this work for reproduction purpose.

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arXiv2024

Digital Twin-Empowered Voltage Control for Power Systems

Emerging digital twin technology has the potential to revolutionize voltage control in power systems. However, the state-of-the-art digital twin method suffers from low computational and sampling efficiency, which hinders its applications. To address this issue, we propose a Gumbel-Consistency Digital Twin (GC-DT) method that enhances voltage control with improved computational and sampling efficiency. First, the proposed method incorporates a Gumbel-based strategy improvement that leverages the Gumbel-top trick to enhance non-repetitive sampling actions and reduce the reliance on Monte Carlo Tree Search simulations, thereby improving computational efficiency. Second, a consistency loss function aligns predicted hidden states with actual hidden states in the latent space, which increases both prediction accuracy and sampling efficiency. Experiments on IEEE 123-bus, 34-bus, and 13-bus systems demonstrate that the proposed GC-DT outperforms the state-of-the-art DT method in both computational and sampling efficiency.

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arXiv2024

UniTE: A Survey and Unified Pipeline for Pre-training Spatiotemporal Trajectory Embeddings

Spatiotemporal trajectories are sequences of timestamped locations, which enable a variety of analyses that in turn enable important real-world applications. It is common to map trajectories to vectors, called embeddings, before subsequent analyses. Thus, the qualities of embeddings are very important. Methods for pre-training embeddings, which leverage unlabeled trajectories for training universal embeddings, have shown promising applicability across different tasks, thus attracting considerable interest. However, research progress on this topic faces two key challenges: a lack of a comprehensive overview of existing methods, resulting in several related methods not being well-recognized, and the absence of a unified pipeline, complicating the development of new methods and the analysis of methods. We present UniTE, a survey and a unified pipeline for this domain. In doing so, we present a comprehensive list of existing methods for pre-training trajectory embeddings, which includes methods that either explicitly or implicitly employ pre-training techniques. Further, we present a unified and modular pipeline with publicly available underlying code, simplifying the process of constructing and evaluating methods for pre-training trajectory embeddings. Additionally, we contribute a selection of experimental results using the proposed pipeline on real-world datasets. Implementation of the pipeline is publicly available at https://github.com/Logan-Lin/UniTE.

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arXiv2024

Long-distance Geomagnetic Navigation in GNSS-denied Environments with Deep Reinforcement Learning

Geomagnetic navigation has drawn increasing attention with its capacity in navigating through complex environments and its independence from external navigation services like global navigation satellite systems (GNSS). Existing studies on geomagnetic navigation, i.e., matching navigation and bionic navigation, rely on pre-stored map or extensive searches, leading to limited applicability or reduced navigation efficiency in unexplored areas. To address the issues with geomagnetic navigation in areas where GNSS is unavailable, this paper develops a deep reinforcement learning (DRL)-based mechanism, especially for long-distance geomagnetic navigation. The designed mechanism trains an agent to learn and gain the magnetoreception capacity for geomagnetic navigation, rather than using any pre-stored map or extensive and expensive searching approaches. Particularly, we integrate the geomagnetic gradient-based parallel approach into geomagnetic navigation. This integration mitigates the over-exploration of the learning agent by adjusting the geomagnetic gradient, such that the obtained gradient is aligned towards the destination. We explore the effectiveness of the proposed approach via detailed numerical simulations, where we implement twin delayed deep deterministic policy gradient (TD3) in realizing the proposed approach. The results demonstrate that our approach outperforms existing metaheuristic and bionic navigation methods in long-distance missions under diverse navigation conditions.

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arXiv2024

A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance

Various animals exhibit accurate navigation using environment cues. The Earth's magnetic field has been proved a reliable information source in long-distance fauna migration. Inspired by animal navigation, this work proposes a bionic and data-driven approach for long-distance underwater navigation. The proposed approach uses measured geomagnetic data for the navigation, and requires no GPS systems or geographical maps. Particularly, we construct and train a Temporal Attention-based Long Short-Term Memory (TA-LSTM) network to predict the heading angle during the navigation. To mitigate the impact of geomagnetic anomalies, we develop the mechanism to detect and quantify the anomalies based on Maximum Likelihood Estimation. We integrate the developed mechanism with the TA-LSTM, and calibrate the predicted heading angles to gain resistance against geomagnetic anomalies. Using the retrieved data from the WMM model, we conduct numerical simulations with diversified navigation conditions to test our approach. The simulation results demonstrate a resilience navigation against geomagnetic anomalies by our approach, along with precision and stability of the underwater navigation in single and multiple destination missions.

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arXiv2023

Privacy-preserving transactive energy systems: Key topics and open research challenges

This manuscript aims to formalize and conclude the discussions initiated during the PriTEM workshop 22-23 March 2023. We present important ideas and discussion topics in the context of transactive energy systems. Moreover, the conclusions from the discussions articulate potential aspects to be explored in future studies on transactive energy management. Particularly, these conclusions cover research topics in energy technology and energy informatics, energy law, data law, energy market and socio-psychology that are relevant to the seamless integration of renewable energy resources and the transactive energy systems-in smart microgrids-focusing on distributed frameworks such as peer-to-peer (P2P) energy trading. We clarify issues, identify barriers, and suggest possible solutions to open questions in diversified topics, such as block-chain interoperability, consumer privacy and data sharing, and participation incentivization. Furthermore, we also elaborate challenges associated with cross-disciplinary collaboration and coordination for transactive energy systems, and enumerate the lessons learned from our work so far.

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arXiv2023

Adaptive robust tracking control with active learning for linear systems with ellipsoidal bounded uncertainties

This paper is concerned with the robust tracking control of linear uncertain systems, whose unknown system parameters and disturbances are bounded within ellipsoidal sets. We propose an adaptive robust control that can actively learn the ellipsoid sets. Particularly, the proposed approach utilizes the recursive set-membership state estimation in learning the ellipsoidal sets, aiming at mitigating uncertainties in the system control. Upon the learned sets representing the recognized uncertainties, we construct a robust control with one-step prediction for system output tracking. In deriving an optimized control law, we reformulate the optimization objective into a second-order cone programming problem that can be solved in a computationally friendly way. To further stimulate the active learning of uncertainties over the control procedures, we enrich the information used for the learning by maximizing the volume of the ellipsoid set, supposed to lead to increased learning accuracy and accelerated uncertainty reduction. To verify our approach, we conduct numerical simulations to compare the fixed-ellipsoidal-set robust control with ours, and investigate the positive effect of the designed active learning in the uncertain system control process.

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