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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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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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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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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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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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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
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Moerland, Thomas M. (author), Broekens, D.J. (author), Plaat, Aske (author), Jonker, C.M. (author)Sequential decision making, commonly formalized as optimization of a Markov Decision Process, is a key challenge in artificial intelligence. Two successful approaches to MDP optimization are reinforcement learning and planning, which both largely have their own research communities. However, if both research fields solve the same problem,...journal article 2022
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Ribeiro, M.J. (author), Ellerbroek, Joost (author), Hoekstra, J.M. (author)Current predictions on future drone operations estimate that traffic density orders of magnitude will be higher than any observed in manned aviation. Such densities redirect the focus towards elements that can decrease conflict rate and severity, with special emphasis on airspace structures, an element that has been overlooked within...journal article 2022
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Ribeiro, M.J. (author), Ellerbroek, Joost (author), Hoekstra, J.M. (author)Current predictions for future operations with drones estimate traffic densities orders of magnitude higher than any observed in manned aviation. Such densities call for further research and innovation, in particular, into conflict detection and resolution without the need for human intervention. The layered airspace concept, where aircraft...journal article 2022
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Baldi, S. (author), Zhang, Z. (author), Liu, Di (author)We propose a new reinforcement learning method in the framework of Recursive Least Squares-Temporal Difference (RLS-TD). Instead of using the standard mechanism of eligibility traces (resulting in RLS-TD((Formula presented.))), we propose to use the forgetting factor commonly used in gradient-based or least-square estimation, and we show that...journal article 2022
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Baccour, Emna (author), Mhaisen, N. (author), Abdellatif, Alaa Awad (author), Erbad, Aiman (author), Mohamed, Amr (author), Hamdi, Mounir (author), Guizani, Mohsen (author)Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems and speech processing applications to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive...journal article 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