Model Predictive Control discretesystems with unknown input and its application to control problem of economic object
The problem considered in the paper deals with synthesis of Model Predictive Control that isapplied to the discrete system containing unknown input. The control is carried out on the base ofthe system output tracking. The prediction is derived on the base of state estimation obtained byKalman filter (extrapolator) and unknown input estimations. Two methods are considered to beused for evaluating the unknown input estimations. The first is based on the applying Kalman filter,the second - on the modified least-squares method. It is discussed that the choice of themethod depends on the available a priori statistical information concerning unknown input signals.The model investigated in the paper contains states, known and unknown inputs and disturbancesacting on the system. It is assumed also that the system is operating under the state and inputconstraints. The aim of the system control is to synthesize control inputs based on observationsproviding the system output to be close to the reference.The simulation results of the proposed methods are given for an example of the goods production,storage and delivery to consumers problem.
Keywords
production model,
estimations unknown input,
discrete systems,
model predictive control,
оценки неизвестного входа,
модель производства,
прогнозирующее управление,
дискретные системыAuthors
| Pristupa Marina Yu. | National Research Tomsk State University | kiselevamy@gmail.com |
| Smagin Valery I. | National Research Tomsk State University | vsm@mail.tsu.ru |
Всего: 2
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