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Zhang, Zheng (author), Zhang, Dengyu (author), Zhang, Qingrui (author), Pan, W. (author), Hu, Tianjiang (author)Integrating rule-based policies into reinforcement learning promises to improve data efficiency and generalization in cooperative pursuit problems. However, most implementations do not properly distinguish the influence of neighboring robots in observation embedding or inter-robot interaction rules, leading to information loss and inefficient...journal article 2024
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Wei, Zeyong (author), Chen, Honghua (author), Nan, L. (author), Wang, Jun (author), Qin, Jing (author), Wei, Mingqiang (author)Current point cloud denoising (PCD) models optimize single networks, trying to make their parameters adaptive to each point in a large pool of point clouds. Such a denoising network paradigm neglects that different points are often corrupted by different levels of noise and they may convey different geometric structures. Thus, the intricacy of...journal article 2024
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Çetindağ, Can (author)Active knee prostheses are potent in assisting users, providing symmetry in walking, reducing metabolic costs, and preventing long-term health problems. The heart of their complex control algorithm employs the Impedance Control (IC) Law, which controls the torque output of the device by three parameters: stiffness coefficient, equilibrium angle,...master thesis 2023
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Zhang, Hengkai (author)The railway timetable rescheduling problem is a challenging problem in both industry and academia. It is required to calculate a feasible and relatively good timetable within a limited time to reduce the negative impact of disturbances or disruptions. The railway timetable rescheduling problem is typically formulated as a mixed integer linear...master thesis 2023
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de Inza Niemeijer, Carlos (author)The continued increase in the number of satellites in low Earth orbit has led to a growing threat of collisions between space objects. On-orbit servicing and active debris removal missions can alleviate this threat by extending the lifetime of active satellites and deorbiting inactive ones, but this requires advanced guidance and control...master thesis 2023
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Lijcklama à Nijeholt, Floortje (author)As technology continues to evolve at a rapid pace, robots are becoming an increasingly common sight in our daily lives. <br/>Robots that work with humans need to adapt to a variety of users and tasks, and learn to optimise their behaviour. For non-specialist users to interact with such robots, the robot's learning process needs to be transparent...master thesis 2023
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Breysens, G. (author)This thesis investigates the potential of state-dependent sampling strategies (SDSS) for the control of heavy-haul trains. Event-triggered control (ETC) is a control approach in which data is only sent when some state-dependent condition, the triggering condition, is satisfied. In this way, the number of communications required to stabilise a...master thesis 2023
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Liu, Y. (author), Pan, W. (author)Machine learning can be effectively applied in control loops to make optimal control decisions robustly. There is increasing interest in using spiking neural networks (SNNs) as the apparatus for machine learning in control engineering because SNNs can potentially offer high energy efficiency, and new SNN-enabling neuromorphic hardware is being...journal article 2023
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Voogd, Kevin L. (author), Allamaa, Jean Pierre (author), Alonso-Mora, J. (author), Son, Tong Duy (author)Reinforcement learning (RL) is a promising solution for autonomous vehicles to deal with complex and uncertain traffic environments. The RL training process is however expensive, unsafe, and time-consuming. Algorithms are often developed first in simulation and then transferred to the real-world, leading to a common sim2real challenge where...journal article 2023
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Abolfazli, Amir (author), Spiegelberg, Jakob (author), Anand, A. (author), Palmer, Gregory (author)Configurable software systems have become increasingly popular as they enable customized software variants. The main challenge in dealing with configuration problems is that the number of possible configurations grows exponentially as the number of features increases. Therefore, algorithms for testing customized software have to deal with the...conference paper 2023
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Joseph, G. (author), Zhong, Chen (author), Gursoy, M. Cenk (author), Velipasalar, Senem (author), Varshney, Pramod (author)We address the problem of sequentially selecting and observing processes from a given set to find the anomalies among them. The decision-maker observes a subset of the processes at any given time instant and obtains a noisy binary indicator of whether or not the corresponding process is anomalous. We develop an anomaly detection algorithm that...journal article 2023
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Sun, D. (author), Jamshidnejad, A. (author), De Schutter, B.H.K. (author)Traffic control is essential to reduce congestion in both urban and freeway traffic networks. These control measures include ramp metering and variable speed limits for freeways, and traffic signal control for urban traffic. However, current traffic control methods are either too simple to respond to complex traffic environment, or too...journal article 2023
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Neustroev, G. (author)Sequential decision-making under uncertainty is an important branch of artificial intelligence research with a plethora of real-life applications. In this thesis, we generalize two fundamental properties of the decision-making process. First, we show that the theory on planning methods for finite spaces can be extended to infinite but countable...doctoral thesis 2022
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Sarkar, A. (author)Efforts to realize a sufficiently large controllable quantum processor are actively being pursued globally. These quantum devices are programmed by specifying the manipulation of quantum information via quantum algorithms. This doctoral research provides an application perspective to the design requirements of a quantum accelerator architecture....doctoral thesis 2022
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Jurševskis, Renāts (author)Recent developments in applying reinforcement learning to cooperative environments, like negotiation, have brought forward an important question: how well can a negotiating agent be trained through self-play? Previous research has seen successful application of self-play to other settings, like the games of chess and Go. This paper explores the...bachelor thesis 2022
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LU, Jingyi (author)Inspired by the natural nervous system, synaptic plasticity rules are applied to train spiking neural networks. Different from learning algorithms such as propagation and evolution that are widely used to train spiking neural networks, synaptic plasticity rules learn the parameters with local information, making them suitable for online learning...master thesis 2022
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Adibi, M. (author), van der Woude, J.W. (author)In this article, we present a reinforcement learning-based scheme for secondary frequency control of lossy inverter-based microgrids. Compared with the existing methods in the literature, we relax the common restrictions on the system, i.e., being lossless, and the transmission lines and loads to have known constant impedances. The proposed...journal article 2022
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Wei, Lianzhen (author), Gong, Jianwei (author), Chen, Huiyan (author), Li, Z. (author), Gong, Cheng (author)To deal with the nonlinear interference caused by chassis movement and road surface undulations with the tracking and aiming of unmanned combat ground vehicles, a tracking and aiming adaptive control method for unmanned combat ground vehicles on the move based on reinforcement learning compensation is proposed. This method consists of a main...journal article 2022
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Stops, L. (author), Leenhouts, Roel (author), Gao, Q. (author), Schweidtmann, A.M. (author)Process synthesis experiences a disruptive transformation accelerated by artificial intelligence. We propose a reinforcement learning algorithm for chemical process design based on a state-of-the-art actor-critic logic. Our proposed algorithm represents chemical processes as graphs and uses graph convolutional neural networks to learn from...journal article 2022
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Li, Guoqiang (author), Gorges, Daniel (author), Wang, M. (author)In this paper a learning-based optimization method for online gear shift and velocity control is presented to reduce the fuel consumption and improve the driving comfort in a car-following process. The continuous traction force and the discrete gear shift are optimized jointly to improve both the powertrain operation and the longitudinal...journal article 2022