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

CRAB

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

indifferent

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

defendant

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.

conduct

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.

分期付款

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

antidote

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

ADAGE

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.

Embolic-Stroke

Indurate

malign

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.