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A family of model predictive control algorithms with artificial neural networks

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Ławryńczuk M.

International Journal of Applied Mathematics and Computer Science

2007

tom 17

nr 2

217-232

This paper details nonlinear Model-based Predictive Control (MPC) algorithms for MIMO processes modelled by means of neural networks of a feedforward structure. Two general MPC techniques are considered: the one with Nonlinear Optimisation (MPC-NO) and the one with Nonlinear Prediction and Linearisation (MPC-NPL). In the first case a nonlinear optimisation problem is solved in real time on-line. In order to reduce the computational burden, in the second case a neural model of the process is used on-line to determine local linearisation and a nonlinear free trajectory. Single-point and multi-point linearisation methods are discussed. The MPC-NPL structure is far more reliable and less computationally demanding in comparison with the MPC-NO one because it solves a quadratic programming problem, which can be done efficiently within a foreseeable time frame. At the same time, closed-loop performance of both algorithm classes is similar. Finally, a hybrid MPC algorithm with Nonlinear Prediction, Linearisation and Nonlinear optimisation (MPC-NPL-NO) is discussed.

An infinite horizon predictive control algorithm based on multivariable input-output models

100%

Ławryńczuk M., Tatjewski P.

International Journal of Applied Mathematics and Computer Science

2004

tom 14

nr 2

167-180

In this paper an infinite horizon predictive control algorithm, for which closed loop stability is guaranteed, is developed in the framework of multivariable linear input-output models. The original infinite dimensional optimisation problem is transformed into a finite dimensional one with a penalty term. In the unconstrained case the stabilising control law, using a numerically reliable SVD decomposition, is derived as an analytical formula, calculated off-line. Considering constraints needs solving on-line a quadratic programming problem. Additionally, it is shown how free and forced responses can be calculated without the necessity of solving a matrix Diophantine equation.

Ograniczanie wyników

2 International Journal of Applied Mathematics and Computer Science

2 Ławryńczuk M.

1 Tatjewski P.

1 2007

1 2004

Wyniki wyszukiwania

A family of model predictive control algorithms with artificial neural networks

An infinite horizon predictive control algorithm based on multivariable input-output models