The setting of an agent making decisions under uncertainty and under dynamic constraints is common for the fields of optimal control, reinforcement learning, and recently also for online learning. In the online learni...
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The setting of an agent making decisions under uncertainty and under dynamic constraints is common for the fields of optimal control, reinforcement learning, and recently also for online learning. In the online learning setting, the quality of an agent's decision is often quantified by the concept of regret, comparing the performance of the chosen decisions to the best possible ones in hindsight. While regret is a useful performance measure, when dynamical systems are concerned, it is important to also assess the stability of the closed-loop system for a chosen policy. In this work, we show that for linear state feedback policies and linear systems subject to adversarial disturbances, linear regret implies asymptotic stability in both time-varying and time-invariant settings. Conversely, we also show that bounded input bounded state stability and summability of the state transition matrices imply linear regret.
Quadruped robots show potential beyond that of wheeled robots for walking over complex terrain thanks to the bionic leg design, and to take advantage of this, we propose a control framework for a quadruped robot to ac...
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Deep-learning-based nonlinear system identification has shown the ability to produce reliable and highly accurate models in practice. However, these black-box models lack physical interpretability, and often a conside...
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This paper studies the fuzzy adaptive control problem for bionics-flapping aircraft with unknown disturbance. To establish the model of the bionics-flapping aircraft, four coordinate systems of ground, aircraft body, ...
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Semi-Markov jump systems(S-MJMs) not only characterize hybrid systems but also address the limitations of Markov jump systems(MJMs) [1–3]. Due to their ability to exhibit multi-time-scale features, singularly perturb...
Semi-Markov jump systems(S-MJMs) not only characterize hybrid systems but also address the limitations of Markov jump systems(MJMs) [1–3]. Due to their ability to exhibit multi-time-scale features, singularly perturbed models(SPMs) effectively model practical systems influenced by multiple time-scale phenomena [4]. In this study, the observer-based output feedback controller is asynchronous with the original system due to the time delay in the controller mode switching. A nonlinear plant with singularly perturbed parameters(SPPs) is represented using an interval type-2(IT2) fuzzy model [5].
The paper describes the modern control (governance, management) science approach to the socioeconomic development of a country's region (province, subject, municipality) in the context of public administration. Pr...
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The issue of iterative learning control (ILC) for distributed parameter systems with sensor/actuator networks is explored. Unlike the traditional setting of ILC where the desired trajectories are identical, here the d...
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The issue of iterative learning control (ILC) for distributed parameter systems with sensor/actuator networks is explored. Unlike the traditional setting of ILC where the desired trajectories are identical, here the desired trajectories are iteratively varying and characterized by a high-order internal mode (HOIM). To address this challenge, the D-type ILC algorithm based on HOIM is devised in this paper. This algorithm enables the systems to accurately track the desired trajectories that vary with each iteration. Using the principle of compressive mapping, the convergence conditions of the output error of the systems are given. In conclusion, numerical simulations are performed to verify the effectiveness of the proposed algorithm.
Augmented reality (AR) has increasingly been used in varying activities by superimposing visual instructions on the workbench directly. Nevertheless, most current AR deployment is mainly first-person perspective, and ...
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Combined with Fourier spectrum prior knowledge, a novel wavelet multiresolution analysis and forecasting algorithm is proposed. It focuses on long term trend prediction of multi-periodic, non-stationary, mobile commun...
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Combined with Fourier spectrum prior knowledge, a novel wavelet multiresolution analysis and forecasting algorithm is proposed. It focuses on long term trend prediction of multi-periodic, non-stationary, mobile communication traffic series. New algorithm calculates the Fourier spectrum for multi-periodic series at first, and takes the prominent period components with definite physical notion as the prior knowledge. After that, it extracts more valuable time domain features with wavelet multiresolution analysis. Finally, it adopts a single model to predict each of them, and integrates the prediction results to gain the final trend prediction of traffic time series. Experimental results on real traffic data show that all isolated components in proposed multiresolution analysis deliver the distinct physical information in traffic data. Additionally, our algorithm can pick out most of prominent period components revealed in the Fourier spectrum and improve prediction accuracy.
Quantum computing provides a powerful framework for tackling computational problems that are classically intractable. The goal of this paper is to explore the use of quantum computers for solving relevant problems in ...
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