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

Aboveboard

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.

DAFT

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.

Nonconformist

inveigh

Maciej ŁawryńczukPresents 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

PHONE

ALIBI

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

RADE

OCTO

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

LUCY

hurricane