This paper presents passivity-based control of nonlinear systems with retarded delays in the state. To this end, we first show that the standard passivity concept can naturally be generalized to time-delay systems, wh...
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This paper presents passivity-based control of nonlinear systems with retarded delays in the state. To this end, we first show that the standard passivity concept can naturally be generalized to time-delay systems, which readily implies that a feedback interconnection (with or without communication delays) of passive time-delay systems is also passive. Then, we propose a storage functional for passivity analysis and further use it for stability analysis of controlled-passive time-delay systems. In particular, invoking an invariance principle for retarded functional differential equations, we show that a passive time-delay system can always be stabilized by a static output feedback controller under a delayed version of the zero-state detectability assumption.
The Traditional Chinese Medicine's ancient literature recorded the massive medical theories and abundant medical experiences. To better understand and utilize, the knowledge from the literature, the Acupuncture an...
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In this work, an Integral Reinforcement Learning (RL) framework is employed to provide provably safe, convergent and almost globally optimal policies in a novel Off-Policy Iterative method for simply-connected workspa...
In this work, an Integral Reinforcement Learning (RL) framework is employed to provide provably safe, convergent and almost globally optimal policies in a novel Off-Policy Iterative method for simply-connected workspaces. This restriction stems from the impossibility of strictly global navigation in multiply connected manifolds, and is necessary for formulating continuous solutions. The current method generalizes and improves upon previous results, where parametrized controllers hindered the method in scope and results. Through enhancing the traditional reactive paradigm with RL, the proposed scheme is demonstrated to outperform both previous reactive methods as well as an RRT* method in path length, cost function values and execution times, indicating almost global optimality.
We provide a novel transcription of monotone operator theory to the non-Euclidean finite-dimensional spaces ℓ 1 and ℓ ∞ . We first establish properties of mappings which are monotone with respect to the non-Euclidea...
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ISBN:
(数字)9781665467612
ISBN:
(纸本)9781665467629
We provide a novel transcription of monotone operator theory to the non-Euclidean finite-dimensional spaces ℓ 1 and ℓ ∞ . We first establish properties of mappings which are monotone with respect to the non-Euclidean norms ℓ 1 or ℓ ∞ . In analogy with their Euclidean counterparts, mappings which are monotone with respect to a non-Euclidean norm are amenable to numerous algorithms for computing their zeros. We demonstrate that several classic iterative methods for computing zeros of monotone operators are directly applicable in the non-Euclidean framework. We present a case-study in the equilibrium computation of recurrent neural networks and demonstrate that casting the computation as a suitable operator splitting problem improves convergence rates.
This paper introduces a learning-based optimal control strategy enhanced with nonmodel-based state estimation to manage the complexities of lane-changing maneuvers in autonomous vehicles. Traditional approaches often ...
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ISBN:
(数字)9798331505929
ISBN:
(纸本)9798331505936
This paper introduces a learning-based optimal control strategy enhanced with nonmodel-based state estimation to manage the complexities of lane-changing maneuvers in autonomous vehicles. Traditional approaches often depend on comprehensive system state information, which may not always be accessible or accurate due to dynamic traffic environments and sensor limitations. Our methodology dynamically adapts to these uncertainties and sensor noise by iteratively refining its control policy based on real-time sensor data and reconstructed states. We implemented an experimental setup featuring a scaled vehicle equipped with GPS, IMUs, and cameras, all processed through an Nvidia Jetson AGX Xavier board. This approach is pivotal as it addresses the limitations of simulations, which often fail to capture the complexity of dynamic real-world conditions. The results from real-world experiments demon-strate that our learning-based control system achieves smoother and more consistent lane-changing behavior compared to traditional direct measurement approaches. This paper underscores the effectiveness of integrating Adaptive Dynamic Program-ming (ADP) with state estimation techniques, as demonstrated through small-scale experiments. These experiments are crucial as they provide a practical validation platform that simulates real-world complexities, representing a significant advancement in the control systems used for autonomous driving.
The evaluation of the diffusion properties of optical clearing agents in biological tissues, which are necessary to characterize the transparency mechanisms, has been traditionally made using ex vivo tissues. With the...
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Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller ...
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ISBN:
(数字)9798350340266
ISBN:
(纸本)9798350340273
Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed.
This paper provides a review on representation learning for videos. We classify recent spatio-temporal feature learning methods for sequential visual data and compare their pros and cons for general video analysis. Bu...
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We consider a perimeter defense problem in a planar conical environment in which a single vehicle, having a finite capture radius, aims to defend a concentric perimeter from mobile intruders. The intruders are arbitra...
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ISBN:
(数字)9781665467612
ISBN:
(纸本)9781665467629
We consider a perimeter defense problem in a planar conical environment in which a single vehicle, having a finite capture radius, aims to defend a concentric perimeter from mobile intruders. The intruders are arbitrarily released at the circumference of the environment and they move radially toward the perimeter with fixed speed. We present a competitive analysis approach to this problem by measuring the performance of multiple online algorithms for the vehicle against arbitrary inputs, relative to an optimal offline algorithm that has information about entire input instance in advance. In particular, we establish two necessary conditions on the parameter space to guarantee (i) finite competitiveness of any algorithm and (ii) a competitive ratio of at least 2 for any algorithm. We then design and analyze three online algorithms and characterize parameter regimes in which they have finite competitive ratios. Specifically, our first two algorithms are provably 1, and 2-competitive, respectively, whereas our third algorithm exhibits different competitive ratios in different regimes of problem parameters. Finally, we provide a numerical plot in the parameter space to reveal additional insights into the relative performance of our algorithms.
Classic tools for measuring energy intake, such as food diaries and 24 hour recalls, are burdensome to use and have significant measurement error. This hinders research and interventions in obesity treatment and comor...
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