標(biāo)題: Titlebook: Computationally Efficient Model Predictive Control Algorithms; A Neural Network App Maciej ?awryńczuk Book 2014 Springer International Publ [打印本頁] 作者: Jejunum 時(shí)間: 2025-3-21 18:32
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書目名稱Computationally Efficient Model Predictive Control Algorithms影響因子(影響力)學(xué)科排名
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書目名稱Computationally Efficient Model Predictive Control Algorithms網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computationally Efficient Model Predictive Control Algorithms被引頻次
書目名稱Computationally Efficient Model Predictive Control Algorithms被引頻次學(xué)科排名
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書目名稱Computationally Efficient Model Predictive Control Algorithms年度引用學(xué)科排名
書目名稱Computationally Efficient Model Predictive Control Algorithms讀者反饋
書目名稱Computationally Efficient Model Predictive Control Algorithms讀者反饋學(xué)科排名
作者: 起來了 時(shí)間: 2025-3-21 22:48
MPC Algorithms Based on Double-Layer Perceptron Neural Models: the Prototypes,The “ideal” MPC algorithm with nonlinear optimisation and a few suboptimal MPC algorithms with different on-line linearisation methods are discussed. In order to illustrate properties of the considered MPC algorithms they are compared in two control systems: a yeast fermentation reactor and a high p作者: 不遵守 時(shí)間: 2025-3-22 04:20 作者: 單獨(dú) 時(shí)間: 2025-3-22 05:03
MPC Algorithms Based on Neural State-Space Models,s well as of two suboptimal MPC-NPL and MPL-NPLPT algorithms are presented. All the algorithms are considered in two versions: with the state set-point trajectory and with the output set-point trajectory. Simulation results are concerned with the polymerisation reactor introduced in the previous cha作者: Accomplish 時(shí)間: 2025-3-22 08:44
MPC Algorithms Based on Neural Multi-Models,hapter is concerned with MPC algorithms based on neural multi-models. The classical dynamic models, both input-output and state-space structures, are used recurrently in MPC algorithms as they calculate the predictions for the whole prediction horizon. In such a case the prediction error is propagat作者: 陳列 時(shí)間: 2025-3-22 13:43
MPC Algorithms with Neural Approximation,arisation. A specially designed neural network (the neural approximator) approximates on-line the step-response coefficients of the model linearised for the current operating point of the process (such an approach is used in the MPC-NPL-NA and DMC-NA algorithms which are extensions of the MPC-NPL an作者: 陳列 時(shí)間: 2025-3-22 19:38
Stability and Robustness of MPC Algorithms,ity and robustness are reviewed with a view to using them in the suboptimal MPC algorithms with on-line linearisation. A modification of the dual-mode MPC strategy is thoroughly discussed which leads to the suboptimal MPC algorithm with theoretically guaranteed stability. Finally, a modification of 作者: 微生物 時(shí)間: 2025-3-22 21:55
Cooperation between MPC Algorithms and Set-Point Optimisation Algorithms,first, the classical multi-layer control system structure is discussed, the main disadvantage of which is the necessity of on-line nonlinear optimisation. Three control structures with on-line linearisation for set-point optimisation are presented next: the multi-layer structure with steady-state ta作者: engrave 時(shí)間: 2025-3-23 02:16 作者: Flat-Feet 時(shí)間: 2025-3-23 09:14 作者: CRAB 時(shí)間: 2025-3-23 10:17
Book 2014ral approximation), the presented suboptimal algorithms do not require demanding on-line nonlinear optimization. The presented simulation results demonstrate high accuracy and computational efficiency of the algorithms. For a few representative nonlinear benchmark processes, such as chemical reactor作者: indifferent 時(shí)間: 2025-3-23 14:37
2198-4182 timization. The presented simulation results demonstrate high accuracy and computational efficiency of the algorithms. For a few representative nonlinear benchmark processes, such as chemical reactor978-3-319-35021-9978-3-319-04229-9Series ISSN 2198-4182 Series E-ISSN 2198-4190 作者: defendant 時(shí)間: 2025-3-23 18:34
Concluding Remarks and Further Research Directions,es, the linear MPC algorithms give good control quality, much better than that of the previously used classical PID algorithms (often single-loop ones). Furthermore, the MPC technique makes it possible to take into account all the necessary constraints in a systematic manner, the satisfaction of which is of paramount importance very frequently.作者: conduct 時(shí)間: 2025-3-23 22:21
https://doi.org/10.1007/978-1-4615-0493-1es, the linear MPC algorithms give good control quality, much better than that of the previously used classical PID algorithms (often single-loop ones). Furthermore, the MPC technique makes it possible to take into account all the necessary constraints in a systematic manner, the satisfaction of which is of paramount importance very frequently.作者: 分期付款 時(shí)間: 2025-3-24 04:08
Book 2014treated include:.·???????? A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction..·???????? Implementation details of the MPC algorithms for feed forward perceptron neural models作者: antidote 時(shí)間: 2025-3-24 10:18
2198-4182 dictive Control.Written by an expert in the field.This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include:.·???????? A few types of suboptimal MPC algorithms in which a linear approximation o作者: ADAGE 時(shí)間: 2025-3-24 12:50
Power Electronics and Power Systemsssible to reduce computational burden of nonlinear MPC algorithms are shortly described, including the on-line linearisation approach. A history of MPC algorithms is given. Finally, a short review of nonlinear model structures is included, their advantages and disadvantages as well as possibilities of using them in MPC are pointed out.作者: Embolic-Stroke 時(shí)間: 2025-3-24 18:40 作者: Indurate 時(shí)間: 2025-3-24 21:20 作者: malign 時(shí)間: 2025-3-25 03:10
Protection Systems with Phasor Inputs MPC strategy is thoroughly discussed which leads to the suboptimal MPC algorithm with theoretically guaranteed stability. Finally, a modification of the MPC strategy with additional state constraints is presented which leads to the suboptimal MPC algorithm with guaranteed robustness.作者: Lipoprotein(A) 時(shí)間: 2025-3-25 03:27 作者: Barrister 時(shí)間: 2025-3-25 10:55 作者: 預(yù)定 時(shí)間: 2025-3-25 12:52 作者: Thrombolysis 時(shí)間: 2025-3-25 15:49
MPC Algorithms Based on Neural State-Space Models,t trajectory and with the output set-point trajectory. Simulation results are concerned with the polymerisation reactor introduced in the previous chapter. It is assumed that all state variables can be measured, but in practice some of them may be unavailable and an observer must be used.作者: DENT 時(shí)間: 2025-3-25 20:53 作者: V切開 時(shí)間: 2025-3-26 01:56
Cooperation between MPC Algorithms and Set-Point Optimisation Algorithms,ion. Three control structures with on-line linearisation for set-point optimisation are presented next: the multi-layer structure with steady-state target optimisation, the integrated structure and the structure with predictive optimiser and constraint supervisor. Implementation details are given for three classes of neural models.作者: ASSAY 時(shí)間: 2025-3-26 07:57
https://doi.org/10.1007/978-0-387-76537-2hms with neural approximation are also presented. They are very computationally efficient, because the neural approximator directly finds on-line the coefficients of the control law, successive on-line linearisation and calculations typical of the classical MPC algorithms are not necessary.作者: 外向者 時(shí)間: 2025-3-26 11:31
MPC Algorithms with Neural Approximation,hms with neural approximation are also presented. They are very computationally efficient, because the neural approximator directly finds on-line the coefficients of the control law, successive on-line linearisation and calculations typical of the classical MPC algorithms are not necessary.作者: malapropism 時(shí)間: 2025-3-26 16:01 作者: 精致 時(shí)間: 2025-3-26 18:33 作者: Aboveboard 時(shí)間: 2025-3-26 22:31
MPC Algorithms Based on Neural Hammerstein and Wiener Models,not need the inverse of the steady-state part. Modelling abilities of cascade neural models are demonstrated for a polymerisation process, properties of the presented MPC algorithms are compared in the control systems of two processes.作者: DAFT 時(shí)間: 2025-3-27 04:36
MPC Algorithms Based on Neural Multi-Models,s for the consecutive sampling instants of the prediction horizon. The structure of the neural multi-model is discussed in this chapter, implementation details of the MPC-NO algorithm and some suboptimal MPC schemes are given.作者: Nonconformist 時(shí)間: 2025-3-27 08:48 作者: inveigh 時(shí)間: 2025-3-27 09:49
Maciej ?awryńczukPresents recent research in Computationally Efficient Model Predictive Control Algorithms.Focuses on a Neural Network Approach for Model Predictive Control.Written by an expert in the field作者: PHONE 時(shí)間: 2025-3-27 17:42 作者: ALIBI 時(shí)間: 2025-3-27 17:59
Power Electronics and Power Systemsed. The general classification of MPC algorithms is given, i.e. linear and nonlinear approaches are characterised. Next, some methods which make it possible to reduce computational burden of nonlinear MPC algorithms are shortly described, including the on-line linearisation approach. A history of MP作者: RADE 時(shí)間: 2025-3-28 01:40 作者: OCTO 時(shí)間: 2025-3-28 03:05
https://doi.org/10.1007/978-3-319-50584-8i-input multi-output models are discussed and implementation details of three algorithms introduced in the previous chapter are given (MPCNO, MPC-NPL and MPC-NPLPT schemes are considered). Additionally, the MPC algorithms with simplified linearisation, which is possible due to special structures of 作者: LUCY 時(shí)間: 2025-3-28 08:05 作者: hurricane 時(shí)間: 2025-3-28 11:01 作者: DNR215 時(shí)間: 2025-3-28 15:34 作者: DUST 時(shí)間: 2025-3-28 20:28 作者: concise 時(shí)間: 2025-3-29 02:12
Protection Systems with Phasor Inputsfirst, the classical multi-layer control system structure is discussed, the main disadvantage of which is the necessity of on-line nonlinear optimisation. Three control structures with on-line linearisation for set-point optimisation are presented next: the multi-layer structure with steady-state ta作者: Asparagus 時(shí)間: 2025-3-29 06:59
https://doi.org/10.1007/978-1-4615-0493-1e using the currently available software and hardware platforms. Thousands of industrial applications of the linear MPC algorithms confirm their practical usefulness [262]. The majority of those processes have many inputs and many outputs and are characterised by significant delays. For such process作者: patriarch 時(shí)間: 2025-3-29 08:40
https://doi.org/10.1007/978-3-319-04229-9Control; Control Applications; Control Engineering; Mulitlayer Control; Neural Network; Optimization; Pred
