Searched for: subject%3A%22Robust%255C+control%22
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Kaushal, Janki (author)
This research evaluates various linear quadratic control techniques, with a particular focus on loop transfer recovery methods, to enhance the safety and robustness of flight control systems. It aims to address the limitations of classical control strategies in managing complex, multivariable systems by implementing advanced recovery mechanisms....
master thesis 2024
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Loukisas, Chari (author)
This study demonstrates an effective systematic control design procedure by applying H∞ Loop-Shaping with a structured controller on an agile aerospace vehicle with a focus on automation. The gain-scheduled implementation is additionally described and tested with non-linear simulations, including a realistic moving point-hit scenario with...
master thesis 2024
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Oomen, T.A.E. (author), Rojas, Cristian R. (author)
A direct data-driven iterative algorithm is developed to accurately estimate the H<sub>∞</sub> norm of a linear time-invariant system from continuous operation, i.e., without resetting the system. The main technical step involves a reversed-circulant matrix that can be evaluated in a model-free setting by performing experiments on the real...
journal article 2024
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Rado, Karlo (author)
A key challenge for SaR robotics is to avoid dynamic obstacles in cluttered environments, with limited and noisy information. In this research, a controller for SaR robots is developed by coupling a local heuristic motion planner with a model predictive control (MPC) based trajectory tracker. Constraint tightening and tube-based control are used...
master thesis 2023
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Dong, Shawn (author)
This paper presents a train robust control method to optimize train operation based on the concept virtual coupling on train platoon. This approach is inspired by the recent development of platoon control for autonomous vehicles, and it is hoped that this platoon control can be applied to railway transportation. We use a decentralized model...
student report 2023
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Reed, Robert (author), Laurenti, L. (author), Lahijanian, Morteza (author)
Deep Kernel Learning (DKL) combines the representational power of neural networks with the uncertainty quantification of Gaussian Processes. Hence, it is potentially a promising tool to learn and control complex dynamical systems. In this letter, we develop a scalable abstraction-based framework that enables the use of DKL for control...
journal article 2023
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Guo, M. (author), De Persis, Claudio (author), Tesi, Pietro (author)
We consider data-driven control of input-affine systems via approximate nonlinearity cancellation. Data-dependent semi-definite program is developed to characterize the stabilizer such that the linear dynamics of the closed-loop systems is stabilized and the influence of the nonlinear dynamics is decreased. Because of the additional...
journal article 2023
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Tacx, Paul (author), Oomen, T.A.E. (author)
The selection of uncertainty structures is an important aspect of system identification for robust control. The aim of this paper is to provide insight into uncertain multivariable systems for robust control. A unified method for visualizing model sets is developed by generating Bode plots of multivariable uncertain systems, both in magnitude...
journal article 2023
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Völker, Willem (author)
Recent research on the Flying V - a flying-wing long-range passenger aircraft - shows that its airframe design is 25% more aerodynamically efficient than a conventional tube-and-wing airframe. The Flying V is therefore a promising contribution towards reduction in climate impact of long-haul flights. However, some design aspects of the Flying V...
master thesis 2022
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Tajdari, F. (author)
Aiming at a more efficient and accurate performance of parallel manipulators in the existence of complex kinematics and dynamics, a robust generalizable methodology is proposed here for an integrated 6-DOF Stewart platform with rotary time-delayed actuators torque control. The suggested method employs a time-delay linear–quadratic integral...
journal article 2022
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Sarafraz, Mohammad Saeed (author), Proskurnikov, Anton V. (author), Tavazoei, Mohammad Saleh (author), Mohajerin Esfahani, P. (author)
In this article, we investigate the problem of practical output regulation, i.e., to design a controller that brings the system output in the vicinity of a desired target value while keeping the other variables bounded. We consider uncertain systems that are possibly nonlinear and the uncertainty of their linear parts is modeled element wise...
journal article 2022
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Liu, L. (author), Lekić, A. (author), Popov, M. (author)
Due to its excellent performance, VSC-based high voltage direct current (HVDC) power systems draw significant attention. They are being heavily used in modern industrial applications, such as onshore and offshore wind farms, and for interconnection between asynchronous networks. However, the traditional proportional-integral (PI) control...
conference paper 2022
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Du, Zhe (author), Negenborn, R.R. (author), Reppa, V. (author)
Among the promising application of autonomous surface vessels (ASVs) is the utilization of multiple autonomous tugs for manipulating a floating object such as an oil platform, a broken ship, or a ship in port areas. Considering the real conditions and operations of maritime practice, this paper proposes a multi-agent control algorithm to...
journal article 2021
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Roy, S. (author), Lee, Jinoh (author), Baldi, S. (author)
This brief proposes a new adaptive-robust formulation for time-delay control (TDC) under a less-restrictive stability condition. TDC relies on estimating the unknown system dynamics via the artificial introduction of a time delay, often referred to as time-delay estimation (TDE). In conventional TDC, the estimation error, called TDE error, is...
journal article 2021
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Gravell, Benjamin J. (author), Mohajerin Esfahani, P. (author), Summers, Tyler H. (author)
Robust stability and stochastic stability have separately seen intense study in control theory for many decades. In this work we establish relations between these properties for discrete-time systems and employ them for robust control design. Specifically, we examine a multiplicative noise framework which models the inherent uncertainty and...
journal article 2021
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Khalik, Alan (author)
With data-driven control it is possible to design a controller for systems with non-parametric models. The intermediate step of modelling or identification of the system is not necessary, because a non-parametric model of the system can be obtained by means of experimental data. In the linear time-invariant (LTI) framework, these non-parametric...
master thesis 2020
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Das, Hemjyoti (author)
Pneumatic cylinders provide an environment-friendly actuation means by minimizing the leakage of any harmful industrial fluids, as occurs for hydraulic actuators. Thus, pneumatic actuators require less maintenance, compared to hydraulic actuators. Moreover, for a similar weight of hydraulic actuator, the cost of a pneumatic actuation system is...
master thesis 2020
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Skaltsis, Georgios (author)
Data-driven control approaches have been proved highly effective in applications where the dynamics of the system are unknown. The reason is that the use of data in control overcomes the challenge of parametric modeling which requires effort and often leads to an insufficient description of the system’s behavior. By contrast, the behavior of the...
master thesis 2020
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Stinenbosch, Tobias (author)
In the past decade, the wind energy subsidies provided by the Dutch government is declining. In order to adopt wind energy as a well-established source of renewable energy, the costs of (offshore) wind energy should be lowered. This can be achieved by increasing the energy production or by lowering the maintenance and initial costs. In search of...
master thesis 2020
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Roy, S. (author), Baldi, S. (author)
Available control methods for underactuated Euler–Lagrange (EL) systems rely on structure-specific constraints that may be appropriate for some systems, but restrictive for others. A generalized (structure-independent) control framework is to a large extent missing, especially in the presence of uncertainty. This paper introduces an adaptive...
journal article 2020
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