Results 291 to 300 of about 2,149,733 (349)
Koopman-Based Model Predictive Control of Functional Electrical Stimulation for Ankle Dorsiflexion and Plantarflexion Assistance. [PDF]
IEEE Trans Neural Syst Rehabil EngSingh M, Hakam N, Kesar TM, Sharma N.
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AI enhanced model predictive control for optimizing LPG recovery through integrated computational modeling design of experiments and multivariate regression. [PDF]
Sci RepEl Hakim BA +3 more
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Optimal design of a hybrid ship energy management system under various sea conditions using Model Predictive Control. [PDF]
PLoS OneMushtaq R, Iqbal M, Khaliq A, Iqbal J.
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Constrained predictive control of continuous stirred exothermical reactor using linearization
, 2016 Beata Ziętek, Joanna Ziętkiewicz
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Predictive metamorphic control
Automatica, 2013In spite of its easy implementation, ability to handle constraints and nonlinearities, etc., model predictive control (MPC) does have drawbacks including tuning difficulties. In this paper, we propose a refinement to the basic MPC strategy by incorporating a tuning parameter such that one can move smoothly from an existing controller to a new MPC ...
He Kong +2 more
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The control and predictability of a cryolesion
Cryobiology, 1970Summary An attempt has been made to analyze some of the physical factors which influence the quantitative effect of freezing as used in cryosurgical procedures. The predictability of a cryosurgical lesion has been considered and certain factors in the use of thermocouples to provide control are emphasized.
W, Gill, J, Da Costa, J, Fraser
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42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475), 2004
In this work a new method for designing predictive controllers for linear SISO systems is presented. It uses only one prediction of the process output J time intervals ahead to compute the correspondent future error. The predictive feedback controller is defined by introducing a filter that weights the last w-predicted errors.
Leonardo Giovanini, Michael J. Grimble
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In this work a new method for designing predictive controllers for linear SISO systems is presented. It uses only one prediction of the process output J time intervals ahead to compute the correspondent future error. The predictive feedback controller is defined by introducing a filter that weights the last w-predicted errors.
Leonardo Giovanini, Michael J. Grimble
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Predictive feedforward control
2016 12th IEEE International Conference on Control and Automation (ICCA), 2016Measurable but controllable disturbances are common in industry, which drive systems away from their references and take time for feedback control to reject them, especially when the systems present input-output delays due to mechanical properties. In this paper, a novel feedforward control based on disturbance prediction is proposed.
Xian Li +4 more
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Proceedings of the 13th annual conference on Genetic and evolutionary computation, 2011
In stochastic optimisation, all currently employed algorithms have to be parameterised to perform effectively. Users have to rely on approximate guidelines or, alternatively, undertake extensive prior tuning. This study introduces a novel method of parameter control, i.e.
Aldeida Aleti, Irene Moser
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In stochastic optimisation, all currently employed algorithms have to be parameterised to perform effectively. Users have to rely on approximate guidelines or, alternatively, undertake extensive prior tuning. This study introduces a novel method of parameter control, i.e.
Aldeida Aleti, Irene Moser
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Annual Review in Automatic Programming, 1995
Abstract Progress in the design and use of Model-Based Predictive Control is reviewed. The two-degree-of-freedom solution of many predictive algorithms enables optimal set-point response and rejection of known disturbance patterns, improves robustness against model/plant mismatch, or provides a compromise between these objectives.
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Abstract Progress in the design and use of Model-Based Predictive Control is reviewed. The two-degree-of-freedom solution of many predictive algorithms enables optimal set-point response and rejection of known disturbance patterns, improves robustness against model/plant mismatch, or provides a compromise between these objectives.
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