This paper is dedicated to control theoretically explainable application of autoencoders to optimal fault detection in nonlinear dynamic systems. Autoencoder-based learning is a standard machine learning method and wi...
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This paper presents an algorithmic approach towards bounding the peak time-windowed average value attained by a state function along trajectories of a dynamical system. An example includes the maximum average current ...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
This paper presents an algorithmic approach towards bounding the peak time-windowed average value attained by a state function along trajectories of a dynamical system. An example includes the maximum average current flowing across a power line in any 5 -minute window. The peak time-windowed mean estimation task may be posed as a finite-dimensional but nonconvex optimization problem in terms of an initial condition and stopping time. This problem can be lifted into an infinite-dimensional linear program in occupation measures, where no conservatism is introduced under compactness and dynamical regularity assumptions. The peak time-windowed mean estimation linear program is in turn truncated into a convergent sequence of semidefinite programs using the moment-Sum-of-Squares hierarchy. Bounds of the time-windowed mean are computed for example systems.
During the drilling process of an oil rig, the torsional stiffness decrease as the drilling depth increase and it leads to a stick-slip vibration of the drill string system. The dynamic process of torsional stiffness ...
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ISBN:
(纸本)9781665401166
During the drilling process of an oil rig, the torsional stiffness decrease as the drilling depth increase and it leads to a stick-slip vibration of the drill string system. The dynamic process of torsional stiffness and moment of inertia is complex while it is hard to control the influence of model parameters. In this paper, the mechanism of vibration that meets the actual situation is analyzed, and a new method that uses fuzzy PID control with the Smith predictor is proposed. The simulation and results show that the proposed method can suppress the hysteresis effect and has a certain inhibitory effect on the occurrence of stick-slip vibration.
This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordin...
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In modern chemical industries, soft sensors related to key indicators are crucial in production processes, particularly for multimode chemical processes. However, challenges arise from scarce labeled data and constant...
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Since the earliest conceptualizations by Lee and Markus, and Propoi in the 1960s, Model Predictive control (MPC) has become a major success story of systems and control with respect to industrial impact and with respe...
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Since the earliest conceptualizations by Lee and Markus, and Propoi in the 1960s, Model Predictive control (MPC) has become a major success story of systems and control with respect to industrial impact and with respect to continued and wide-spread research interest. The field has evolved from conceptually simple linear-quadratic (convex) settings in discrete and continuous time to nonlinear and distributed settings including hybrid, stochastic, and infinite-dimensional systems. Put differently, essentially the entire spectrum of dynamic systems can be considered in the MPC framework with respect to both—system theoretic analysis and tailored numerics. Moreover, recent developments in machine learning also leverage MPC concepts and learning-based and data-driven MPC have become highly active research areas. However, this evident and continued success renders it increasingly complex to live up to industrial expectations while enabling graduate students for state-of-the-art research in teaching MPC. Hence, this position paper attempts to trigger a discussion on teaching MPC. To lay the basis for a fruitful debate, we subsequently investigate the prospect of covering MPC in undergraduate courses; we comment on teaching textbooks; and we discuss the increasing complexity of research-oriented graduate teaching of MPC.
This paper uses linear matrix inequality techniques and the Kalman-Yakubovich-Popov lemma to design an iterative learning control law for a class of batch processes with state delays. The design procedure is based on ...
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This paper uses linear matrix inequality techniques and the Kalman-Yakubovich-Popov lemma to design an iterative learning control law for a class of batch processes with state delays. The design procedure is based on the stability theory for repetitive processes, a class of 2D systems. A numerical example illustrates the new design and demonstrates that the design has advantages compared to the existing alternatives.
The increasing application of voltage source converter (VSC) based high voltage direct current (VSC-HVDC) technology in power grids has raised the importance of incorporating DC grids and converters into the existing ...
The increasing application of voltage source converter (VSC) based high voltage direct current (VSC-HVDC) technology in power grids has raised the importance of incorporating DC grids and converters into the existing transmission network. This poses significant challenges in dealing with the resulting optimal power flow (OPF) problem. In this paper, a recently proposed nonconvex distributed optimization algorithm — Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN), is tailored to solve the nonconvex AC/DC OPF problem for emerging voltage source converter (VSC) based multiterminal high voltage direct current (VSC-MTDC) meshed AC/DC hybrid systems. The proposed scheme decomposes this AC/DC hybrid OPF problem and handles it in a fully distributed way. Compared to the existing state-of-art Alternating Direction Method of Multipliers (ADMM), which is in general, not applicable for nonconvex problems, ALADIN has a theoretical convergence guarantee. Applying these two approaches to VSC-MTDC coupled with an IEEE benchmark AC power system illustrates that the tailored ALADIN outperforms ADMM in convergence speed and numerical robustness.
The increasing application of voltage source converter (VSC) based high voltage direct current (VSC-HVDC) technology in power grids has raised the importance of incorporating DC grids and converters into the existing ...
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The integration of visuotactile sensor technology with endoscopy enables the provision of tactile information for minimally invasive surgeries and other operations. This paper proposes a real-time sensing framework fo...
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ISBN:
(数字)9798350372601
ISBN:
(纸本)9798350372618
The integration of visuotactile sensor technology with endoscopy enables the provision of tactile information for minimally invasive surgeries and other operations. This paper proposes a real-time sensing framework for contact deformation pattern shape, providing a foundation for precise perception in miniaturized visuotactile sensors. Specifically, we first construct a Deformation Pattern shape Descriptor (DPSD) and then track the key points in the descriptor using optical flow to obtain contact deformation shape information. Through extensive qualitative and quantitative experiments, our proposed algorithm achieves an average perception accuracy of around 83.81% for contact pattern shapes. It demonstrates consistent perception accuracy across various contact force magnitudes, directions, and scenarios, highlighting its robustness. Additionally, compared to traditional marker-level methods, it achieves a notable 46.5% improvement in displacement field perception accuracy. This research elevates tactile perception to the dimension of contact pattern shape sensing and has the potential for application in miniaturized visual tactile sensors.
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