Formal safety guarantees on the synthesis of controllers for stochastic systems can be obtained using correct-by-design approaches. These approaches often use abstractions as finite-state Markov Decision Processes (MD...
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A critical challenge in missile guidance is the frequent inaccessibility of essential target information by on-board seekers. This paper studies the three-dimensional cooperative guidance issue of multiple missiles si...
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In this paper, an improved marine predators algorithm (IMPA) is proposed to solve the short-term hydrothermal scheduling (STHS) problem. The marine predators algorithm (MPA) owns low diversity of the initial populatio...
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This paper focuses on the problem of stability analysis for Takagi-Sugeno systems with time-varying delays. Firstly, a suitable Lyapunov-Krasovskii functional (LKF) containing fuzzy line-integral Lyapunov functional i...
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Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel auto...
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This paper presents a single-loop Model Predictive control strategy that incorporates a reduced-order Generalized Proportional Integral Observe and a Kalman filter to enhance the speed regulation of Permanent Magnet S...
This paper presents a single-loop Model Predictive control strategy that incorporates a reduced-order Generalized Proportional Integral Observe and a Kalman filter to enhance the speed regulation of Permanent Magnet Synchronous Motor systems in the presence of complex disturbances and measurement noises. The proposed controller design seamlessly integrates the predictive control, disturbance observer, and state filter components, and it was evaluated through simulation comparisons. The performance of the proposed method is evaluated using various metrics, including maximum velocity drop, recovery time, and variance of steady-state error, which demonstrate its superior response performance and anti-disturbance ability when compared to other existing methods without state filtering.
Since landslide is one of the most universal natural disasters in China, the study of regional landslide susceptibility evaluation is important to protect people's lives and property. This paper analyzes the geosp...
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This paper presents a vision-based tracking control for a quadrotor to follow a moving target without assuming any quadrotor-target communication. Constant turn rate & acceleration model (CTRA) is first drawn to d...
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ISBN:
(数字)9798350340266
ISBN:
(纸本)9798350340273
This paper presents a vision-based tracking control for a quadrotor to follow a moving target without assuming any quadrotor-target communication. Constant turn rate & acceleration model (CTRA) is first drawn to describe the target's motion, allowing to infer unmeasurable target motion variables while mitigating the adverse effects of vision sensor noises through an unscented Kalman filter (UKF). Then the target's future trajectory is firstly predicted by using a Bézier curve, which is further treated as virtual observation of the CTRA model within the prediction horizon so that a better target trajectory prediction is derived by using the proposed Bézier- UKF fusion predictor method. The outputs of Bézier- UKF predictor are input into an incremental model predictive control (IMPC) planner as the target reference, enabling the derivation of the desired position, velocity and acceleration signal for the quadrotor. These signals are implemented by the low-level SE(3) geometric controller. Finally, we conduct a comparative study of the proposed method against baseline planners such as MPC and IMPC planners in high-fidelity AirSim simulation environment, demonstrating better target following performance.
This article investigates the asynchronous fault detection (FD) problem for fuzzy systems with event-triggered mechanism (ETM). A new dynamic ETM (DETM) is adopted to further reduce the waste of network resources. Con...
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Fall events have unique dynamic features, which are not fully utilized by existing fall detection methods. Based on video understanding, we propose Fall-LSTM to learn such features pertinently without additional input...
Fall events have unique dynamic features, which are not fully utilized by existing fall detection methods. Based on video understanding, we propose Fall-LSTM to learn such features pertinently without additional inputs. Fall-LSTM is composed of CNN-LSTM framework and two excitation modules i.e., Spatial Attention Module (SAM) and Temporal Location Module (TLM). SAM provides spatial constraints on motion for feature layers through foreground extraction and spatial pooling. TLM emphasizes frames with high probability of fall events to LSTM by inferring the rate and trend of motion in clips. Experimental results show that our proposed modules significantly improve the performance of LSTM model, outperforming the state-of-the-art methods on two public Fall Detection Datasets i.e., Le2i and UR.
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