This paper presents an FPGA-based low-power acceleration of sound source localization in HARK, open-source software for robot audition. Due to the massive matrix operations, sound source localization in HARK takes sub...
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ISBN:
(数字)9798350384147
ISBN:
(纸本)9798350384154
This paper presents an FPGA-based low-power acceleration of sound source localization in HARK, open-source software for robot audition. Due to the massive matrix operations, sound source localization in HARK takes substantial processing time in edge computing devices. To balance processing time and low power consumption, two functions in sound source localization that include many matrix operations are targeted and migrated on an FPGA SoC board called M-KUBOS. Compared to CPU-based computing on ARM Cortex A53, our implementation achieved a 2.0× speedup and 1.7× lower energy consumption.
Having access to a reliable and accurate prediction of the short-term power demand is a fundamental step for the widespread adoption of Electric Vehicles (EVs), as their charges may have a significant impact on the po...
Having access to a reliable and accurate prediction of the short-term power demand is a fundamental step for the widespread adoption of Electric Vehicles (EVs), as their charges may have a significant impact on the power system balancing. In this direction, we propose a short-term load demand predictor, based on distributed Long Short-Term Memory Networks, that employs consensus and fully-decentralized Federated Learning (FL) algorithms to seek cooperation among multiple points of charge without the requirement of sharing any user-related data.
This paper proposes a linear quadratic regulator (LQR)-based controller for a grid-connected photovoltaic (PV) with a supercapacitor (SC) system to ensure a smooth transition of control mode when the SC is unavailable...
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ISBN:
(数字)9798350376067
ISBN:
(纸本)9798350376074
This paper proposes a linear quadratic regulator (LQR)-based controller for a grid-connected photovoltaic (PV) with a supercapacitor (SC) system to ensure a smooth transition of control mode when the SC is unavailable (completely charged) during fault operations. The SC improves the low-voltage ride-through (LVRT) capability of the PV system by quickly balancing power between the PV system and the grid. When the SC reaches its full charge capacity, the DC-link voltage will be controlled by the inverter’s controller, and the PV system may need to curtail its power generation. During the transition, fluctuations in the DClink voltage can occur which can compromise the stability of the PV system, especially when the control mode switching happens during severe fault operations. The proposed LQR-based controller is validated on a hardware testbed platform to demonstrate its effectiveness in reducing DC-link overvoltage during the control mode transitions. As a result, the PV system can remain compliant with LVRT requirements while maintaining stability during fault operations.
With the rapid development of recycling and remanufacturing technologies, disassembly line balancing problems (DLBP) have drawn great attention. Considering the limitation of disassembly by humans or robots alone, thi...
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In the last years' digitalization process, the creation and management of documents in various domains, particularly in Public Administration (PA), have become increasingly complex and diverse. This complexity ari...
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This paper addresses the problem of adaptive output regulation of a minimum-phase MIMO LTI system in the presence of un-modeled (fast) input dynamics. The adoption of a post-processing tunable internal model makes it ...
This paper addresses the problem of adaptive output regulation of a minimum-phase MIMO LTI system in the presence of un-modeled (fast) input dynamics. The adoption of a post-processing tunable internal model makes it possible to implement standard methods for the analysis of two-time-scale systems. The proposed adaptation law guarantees, under suitable hypotheses, convergence to zero of the regulation error as well as of the parameter estimation error.
Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for the identification of nonlinear state-space models. However, if the resulting models are used directly for nonlinea...
Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for the identification of nonlinear state-space models. However, if the resulting models are used directly for nonlinear model predictive control (NMPC), the resulting nonlinear optimization problem is often overly complex due to the size of the network, requires the use of high-order observers to track the states of the ANN model, and the overall control scheme does not exploit the available autograd tools for these models. In this paper, we propose an efficient approach to auto-convert ANN statespace models to linear parameter-varying (LPV) form and solve predictive control problems by successive solutions of linear model predictive problems, corresponding to quadratic programs (QPs). Furthermore, we show how existing deep-learning methods, such as SUBNET that uses a state encoder, enable efficient implementation of MPCs on identified ANN models. Performance of the proposed approach is demonstrated by a simulation study on an unbalanced disc system.
The paper examines a new approach to controlling deterministic dynamical chaotic mode of power system with two electric generate-sources. A control law is synthesized in the class of one-parameter structurally stable ...
The paper examines a new approach to controlling deterministic dynamical chaotic mode of power system with two electric generate-sources. A control law is synthesized in the class of one-parameter structurally stable mappings from catastrophe theory. Analytical calculations and a numerical experiment in MatLab showed that the proposed system excludes the generation of a deterministic chaotic mode for any changes in the uncertain system parameters. The study of the control system is carried out by the gradient-velocity method Lyapunov vector functions
Bilateral teleoperation systems have become essential for enabling precise remote manipulation in fields such as robotic surgery and hazardous material handling. However, these systems face significant challenges, inc...
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ISBN:
(数字)9798331542726
ISBN:
(纸本)9798331542733
Bilateral teleoperation systems have become essential for enabling precise remote manipulation in fields such as robotic surgery and hazardous material handling. However, these systems face significant challenges, including time delays, environmental uncertainties, and nonlinear dynamics, which can affect their stability and performance. This research aims to investigate the application of Active Disturbance Rejection control (ADRC) to address these challenges. ADRC is chosen for its real-time estimation and compensation for disturbances, even with limited knowledge of the system model. Additionally, the study incorporates enhancements such as Fractional-Order control and optimization techniques, including Particle Swarm Optimization (PSO), for parameter tuning. A simulation study is conducted to assess the performance of the proposed controller in tracking control of bilateral teleoperation systems with 3-DOF and 4-DOF manipulators. Results illustrate the superiority of the fractional order ADRC in disturbance rejection and trajectory tracking compared to linear ADRC, making it a promising solution for teleoperation applications.
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