Titlebook: Computationally Efficient Model Predictive Control Algorithms; A Neural Network App Maciej Ławryńczuk Book 2014 Springer International Publ

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书目名称Computationally Efficient Model Predictive Control Algorithms影响因子(影响力)




书目名称Computationally Efficient Model Predictive Control Algorithms影响因子(影响力)学科排名




书目名称Computationally Efficient Model Predictive Control Algorithms网络公开度




书目名称Computationally Efficient Model Predictive Control Algorithms网络公开度学科排名




书目名称Computationally Efficient Model Predictive Control Algorithms被引频次




书目名称Computationally Efficient Model Predictive Control Algorithms被引频次学科排名




书目名称Computationally Efficient Model Predictive Control Algorithms年度引用




书目名称Computationally Efficient Model Predictive Control Algorithms年度引用学科排名




书目名称Computationally Efficient Model Predictive Control Algorithms读者反馈




书目名称Computationally Efficient Model Predictive Control Algorithms读者反馈学科排名

起来了

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

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单独

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

Accomplish

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

陈列

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

陈列

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

微生物

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

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