In this article, a distributed semi-consensus-based data-driven fault detection scheme is developed based on the process variables collected by sensor networks to ensure the safety of the large-scale dynamic processes...
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In this article, a distributed semi-consensus-based data-driven fault detection scheme is developed based on the process variables collected by sensor networks to ensure the safety of the large-scale dynamic processes. For our purpose, the distributed data-driven process modeling scheme is developed for dynamic systems first by considering the communication topology of the sensor networks. Then, a distributed Kalman filter-based fault detection approach is developed aiming at achieving optimal detection performance at each sensor node. Specifically, the distributed iterative learning algorithm is implemented to calculate the needed parameter matrices for Kalman filter-based residual generator offline with the aid of average consensus algorithm. It is followed by a distributed fusion of local residual signals to perform online optimal fault detection. To avoid the detection delay caused by the traditional average consensus method, the semi-consensus algorithm is developed for the first time to ensure the timely detection of potential faults. A case study on the multiphase flow facility process is given in the end to demonstrate the proposed method.
This paper investigates the observer design problem for two-dimensional switched discrete-time linear systems with multiple equilibrium points. Firstly, a distributed state observer is constructed by considering that ...
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With the increasing prevalence of lithium-ion batteries, the accurate prediction of remaining useful life (RUL) has emerged as a critical concern. However, the model-based approach exhibits stable estimation but lacks...
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Certainty equivalence adaptive controllers are analysed using a "data-driven Riccati equation", corresponding to the model-free Bellman equation used in Q-learning. The equation depends quadratically on data...
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Certainty equivalence adaptive controllers are analysed using a "data-driven Riccati equation", corresponding to the model-free Bellman equation used in Q-learning. The equation depends quadratically on data correlation matrices. This makes it possible to derive simple sufficient conditions for stability and robustness to unmodeled dynamics in adaptive systems. The paper is concluded by short remarks on how the bounds can be used to quantify the interplay between excitation levels and robustness to unmodeled dynamics.
This paper explores the complexities of autonomous navigation for mobile robots in densely populated environments, where the accuracy of crowd behavior modeling is crucial. Recent studies have increasingly leveraged d...
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Reinforcement learning (RL) is highly dependent on the meticulous design of the reward function. However, accurately assigning rewards to each state-action pair in Long-Term RL (LTRL) challenges is formidable. Consequ...
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With the ever-escalating scale of urban distribution networks (UDNs), the traditional model-based reconfiguration methods are becoming inadequate for smart system control. On the contrary, the data-driven deep reinfor...
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With the ever-escalating scale of urban distribution networks (UDNs), the traditional model-based reconfiguration methods are becoming inadequate for smart system control. On the contrary, the data-driven deep reinforcement learning method can facilitate the swift decision-making but the large action space would adversely affect the learning performance of its agents. Consequently, this paper presents a novel multi-agent deep reinforcement learning method for the reconfiguration of UDNs by introducing the concept of "switch contribution". First, a quantification method is proposed based on the mathematical UDN reconfiguration model. The contributions of controllable switches are effective quantified. By excluding the controllable switches with low contributions during network reconfiguration, the dimensionality of action space can be significantly reduced. Then, an improved QMIX algorithm is introduced to improve the policy of multiple agents by assigning the weights. Besides, a novel two-stage learning structure based on a reward-sharing mechanism is presented to further decompose tasks and enhance the learning efficiency of multiple agents. In the first stage, agents control the switches with higher contributions while switches with lower contributions will be controlled in the second stage. During the two-stage process, the proposed reward-sharing mechanism could guarantee a reliable UND reconfiguration and the convergence of our learning method. Finally, numerical results based on a practical 297-node system are performed to validate our method's effectiveness.
With the increasing presence of autonomous vehicles (AVs) on public roads, developing robust control strategies to navigate the uncertainty of human-driven vehicles (HVs) is crucial. This paper introduces an advanced ...
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With the increasing presence of autonomous vehicles (AVs) on public roads, developing robust control strategies to navigate the uncertainty of human-driven vehicles (HVs) is crucial. This paper introduces an advanced method for modeling HV behavior, combining a first-principles model with Gaussian process (GP) learning to enhance velocity prediction accuracy and provide a measurable uncertainty. We validated this innovative HV model using real-world data from field experiments and applied it to develop a GP-enhanced model predictive control (GP-MPC) strategy. This strategy aims to improve safety in mixed vehicle platoons by integrating uncertainty assessment into distance constraints. Comparative simulation studies with a conventional model predictive control (MPC) approach demonstrated that our GP-MPC strategy ensures more reliable safe distancing and fosters efficient vehicular dynamics, achieving notably higher speeds within the platoon. By incorporating a sparse GP technique in HV modeling and adopting a dynamic GP prediction within the MPC framework, we significantly reduced the computation time of GP-MPC, marking it only 4.6% higher than that of the conventional MPC. This represents a substantial improvement, making the process about 100 times faster than our preliminary work without these approximations. Our findings underscore the effectiveness of learning-based HV modeling in enhancing both safety and operational efficiency in mixed-traffic environments, paving the way for more harmonious AV-HV interactions.
This paper explores reduced-order modeling of complex dynamical systems using data-driven moment matching. We present a novel approach to the modeling procedure based on data informativity, ensuring that the moments o...
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ISBN:
(纸本)9798331540845;9789887581598
This paper explores reduced-order modeling of complex dynamical systems using data-driven moment matching. We present a novel approach to the modeling procedure based on data informativity, ensuring that the moments of systems generating the data can be identified. Our contribution lies in providing a sufficient condition for data to be informative for moment matching. We demonstrate that persistently exciting data and data excited with specific frequencies are informative for achieving moment matching. Additionally, we discuss data-driven moment matching in scenarios where the original system's order is unknown and when the data are noisy. To illustrate the effectiveness of the method, we apply it to a building model, demonstrating its capability to obtain a reduced-order model from input-output data.
One of the critical aspects of assistive robotics is to provide a control system of a high-dimensional robot from a low-dimensional user input (i.e. a 2D joystick). data-driven teleoperation seeks to provide an intuit...
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ISBN:
(纸本)9798350377712;9798350377705
One of the critical aspects of assistive robotics is to provide a control system of a high-dimensional robot from a low-dimensional user input (i.e. a 2D joystick). data-driven teleoperation seeks to provide an intuitive user interface called an action map to map the low dimensional input to robot velocities from human demonstrations. Action maps are machine learning models trained on robotic demonstration data to map user input directly to desired movements as opposed to aspects of robot pose ("move to cup or pour content" vs. "move along x- or y-axis"). Many works have investigated nonlinear action maps with multi-layer perceptrons, but recent work suggests that local-linear neural approximations provide better control of the system. However, local linear models assume actions exist on a linear subspace and may not capture nuanced motions in training data. In this work, we hypothesize that local-linear neural networks are effective because they make the action map odd w.r.t. the user input, enhancing the intuitiveness of the controller. Based on this assumption, we propose two nonlinear means of encoding odd behavior that do not constrain the action map to a local linear function. However, our analysis reveals that these models effectively behave like local linear models for relevant mappings between user joysticks and robot movements. We support this claim in simulation, and show on a realworld use case that there is no statistical benefit of using non-linear maps, according to the users experience. These negative results suggest that further investigation into model architectures beyond local linear models may offer diminishing returns for improving user experience in data-driven teleoperation systems.
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