摘要 :
Cell imbalance in a multicell battery occurs over time due to varying operating environments. This imbalance leads to overall inefficiency in battery discharging due to the relatively weak cells in the battery. Reconfiguring the c...
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Cell imbalance in a multicell battery occurs over time due to varying operating environments. This imbalance leads to overall inefficiency in battery discharging due to the relatively weak cells in the battery. Reconfiguring the cells in the battery is one option for addressing the problem, but relevant circuits may lead to severe safety issues. In this article, we aim to optimize the discharge efficiency of a multicell battery using safety-supplemented hardware. To this end, we first design a cell string-level reconfiguration scheme that is safe in hardware operations and also provides scalability due to the low switching complexity. Second, we propose a machine learning-based run-time switch control that considers various battery-related factors, such as the state of charge, state of health, temperature, and current distributions. Specifically, by exploiting the deep reinforcement learning (DRL) technique, we train the complex relationship among the battery factors and derive the best switch configuration in run-time. We implemented a hardware prototype, validated its functionalities, and evaluated the efficacy of the DRL-based control policy. The experimental results showed that the proposed scheme, along with the optimization method, improves the discharge efficiency of multicell batteries. In particular, the discharge efficiency gain is maximized when the cells constituting the battery are unevenly distributed in terms of cell health and exposed temperature.
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In this paper, a novel decentralized guaranteed cost control method is designed for reconfigurable manipulators with uncertain environments based on the adaptive dynamic programming (ADP) approach. Each joint module, which is the ...
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In this paper, a novel decentralized guaranteed cost control method is designed for reconfigurable manipulators with uncertain environments based on the adaptive dynamic programming (ADP) approach. Each joint module, which is the basic unit for constructing the reconfigurable manipulators, is regarded as a subsystem with model uncertainties that include the error of frictional modeling and the interconnection dynamic coupling (IDC) effect. Then, by employing a robust controller and a neural network (NN) identifier-based compensation controller, the decentralized guaranteed cost control issue with uncertain environments can be changed into the optimal control issue of reconfigurable manipulators. Based on ADP algorithm, the critic neural network is introduced to approximate the modified cost function, and then the Hamilton-Jacobi-Bellman equation is addressed by the policy iterative algorithm, thus making the obtention of approximate optimal control policy doable. The stability of the robotic system under the proposed control policy is demonstrated by employing the Lyapunov theory. Finally, the effectiveness of the proposed control policy for reconfigurable manipulators with different configurations is verified by simulation experiments.
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Approximate computing, being able to tradeoff computation quality (e.g., accuracy) and computational effort (e.g., energy) for error-tolerant applications such as media processing and the emerging recognition, mining, and synthesi...
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Approximate computing, being able to tradeoff computation quality (e.g., accuracy) and computational effort (e.g., energy) for error-tolerant applications such as media processing and the emerging recognition, mining, and synthesis (RMS) applications, has gained significant traction in recent years. With approximate computing, we expect to obtain acceptable results, but how do we make sure the quality of the final results are good enough? This challenging problems remains largely unexplored. As many of the RMS applications employ iterative methods (IMs) for solution-finding, wherein a sequence of improving approximate solutions are generated before reaching the final converged solution, in this paper, we propose ApproxIt, a novel quality management framework of approximate computing dedicated for IMs with quality guarantees. ApproxIt is comprised of two stages: 1) offline stage and 2) online stage. To be specific, at offline stage, we first analyze the manifold of parameter space to identify the given problem as convex case or nonconvex case at the offline stage. And for each case, we propose the corresponding runtime dynamic quality calibration scheme and reconfiguration control policy. Then during runtime, our proposed lightweight quality estimator will evaluate the intermediate quality at specific calibration iteration, which is determined by the novel Markov model-based calibration scheme. If quality violation occurs, the configuration control policy will select the most appropriate approximate computing mode for the following iterations. With the proposed dynamic effort scaling technique, ApproxIt is able to dramatically improve application energy efficiency under quality guarantees, as demonstrated in our experimental results.
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In this paper, we develop a framework for reconfiguration of a discrete event system (DES) controller, which has a dynamic event observation set. We will show the designed reconfiguration yields a more tolerable controller than th...
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In this paper, we develop a framework for reconfiguration of a discrete event system (DES) controller, which has a dynamic event observation set. We will show the designed reconfiguration yields a more tolerable controller than the one designed in . Starting with a maximally permissive controller that has a full observation of its DES, we design a mega-controller that monitors the observation set of the DES controller and its state continuously. Upon a change in the observation set, the mega-controller reconfigures the controller by a aggregation or disaggregation of the controller states. The mega-controller is also responsible for feedback function adjustments if the available observation set causes a conflict in control. We illustrate the reconfiguration procedures by an example.
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We consider the optimal control problem for networks subjected to time-varying channels, reconfiguration delays, and interference constraints. We show that the simultaneous presence of time-varying channels and reconfiguration del...
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We consider the optimal control problem for networks subjected to time-varying channels, reconfiguration delays, and interference constraints. We show that the simultaneous presence of time-varying channels and reconfiguration delays significantly reduces the system stability region and changes the structure of optimal policies. We first consider memoryless channel processes and characterize the stability region in closed form. We prove that a frame-based Max-Weight scheduling algorithm that sets frame durations dynamically, as a function of the current queue lengths and average channel gains, is throughput-optimal. Next, we consider arbitrary Markov-modulated channel processes and show that memory in the channel processes can be exploited to improve the stability region. We develop a novel approach to characterizing the stability region of such systems using state-action frequencies, which are stationary solutions to a Markov Decision Process (MDP) formulation. Moreover, we develop a dynamic control policy using the state-action frequencies and variable frames whose lengths are functions of queue sizes and show that it is throughput-optimal. The frame-based dynamic control (FBDC) policy is applicable to a broad class of network control systems, with or without reconfiguration delays, and provides a new framework for developing throughput-optimal network control policies using state-action frequencies. Finally, we propose Myopic policies that are easy to implement and have better delay properties as compared to the FBDC policy.
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