Results 271 to 280 of about 2,892,641 (307)
Model-Free Control of the PVTOL
2024 International Conference on Control, Automation and Diagnosis (ICCAD)International audienceThis paper present the implementation of the Model Free Control (MFC) to stabilize interconnected multi-SISO subsystems, such a the case of the Planar Vertical Take Off and Landing (PVTOL). The model free control (MFC) is integrated
Eduardo Tzitzihua García +4 more
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Model-free distributed learning
IEEE Transactions on Neural Networks, 1990Model-free learning for synchronous and asynchronous quasi-static networks is presented. The network weights are continuously perturbed, while the time-varying performance index is measured and correlated with the perturbation signals; the correlation output determines the changes in the weights.
Amir Dembo, Thomas Kailath
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Modelling free flight with collision avoidance
Proceedings Seventh IEEE International Conference on Engineering of Complex Computer Systems, 2002Free jlight has been proposed as a future alternative to the current policy in Air Trafic Management (ATM) where aircraft follow predefined corridors. In free Pight pilots can choose their own optimal routes, altitudes and velocities but are also responsible for the safe and fair resolution of trajectory conjlicts. This would require a safe distributed
Massink M, De Francesco N
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Cooperative Adaptive Model-Free Control With Model-Free Estimation and Online Gain Tuning
IEEE Transactions on Cybernetics, 2022In this article, a distributed adaptive model-free control algorithm is proposed for consensus and formation-tracking problems in a network of agents with completely unknown nonlinear dynamic systems. The specification of the communication graph in the network is incorporated in the adaptive laws for estimation of the unknown linear and nonlinear terms,
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Set theory formulation of the model-free problem and the diffusion seeded model-free paradigm
Molecular BioSystems, 2007Abstract Model-free analysis of NMR relaxation data, which describes the motion of individual atoms, is a problem intricately linked to the Brownian rotational diffusion of the macromolecule. The diffusion tensor parameters strongly influence the optimisation of the various model-free models and the subsequent model selection between ...
Edward J, d'Auvergne, Paul R, Gooley
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2021
In the previous chapter, we looked at dynamic programming where we knew the model dynamics p(s’, r| s, a), and this knowledge was used to “plan” the optimal actions. This is also known as the planning problem. In this chapter, we will shift our focus and look at learning problems, i.e., a setup where the model dynamics are not known.
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In the previous chapter, we looked at dynamic programming where we knew the model dynamics p(s’, r| s, a), and this knowledge was used to “plan” the optimal actions. This is also known as the planning problem. In this chapter, we will shift our focus and look at learning problems, i.e., a setup where the model dynamics are not known.
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Preserving Structure in Model-Free Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014Model-free trackers can track arbitrary objects based on a single (bounding-box) annotation of the object. Whilst the performance of model-free trackers has recently improved significantly, simultaneously tracking multiple objects with similar appearance remains very hard.
Lu Zhang 0036, Laurens van der Maaten
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Model-free observer for MIMO systems
2015 IEEE Conference on Control Applications (CCA), 2015In this paper, a novel observer design is introduced to estimate the outputs of a Multi-Input-Multi-Output (MIMO) system. From the synthesis of state observer and Model-Free technique, the Model-Free Observer (MFO) is proposed to compensate for the un-modeled dynamics, modeling errors, and the system uncertainties.
Younes Al Younes +3 more
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Hybrid Model-Free and Model-Free Adaptive Fuzzy Controllers
2021Radu-Emil Precup +2 more
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Structural Safety, 2007
Abstract In this paper a novel technique for random vector sampling starting from rare data are presented. This model-free sampling technique is developed to operate without a probabilistic model. Instead of estimating a distribution function, the information contained in a given small sample is extracted directly to produce the sampling result as a ...
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Abstract In this paper a novel technique for random vector sampling starting from rare data are presented. This model-free sampling technique is developed to operate without a probabilistic model. Instead of estimating a distribution function, the information contained in a given small sample is extracted directly to produce the sampling result as a ...
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