Wyniki wyszukiwania - BazTech

1

Parity equations-based unknown input estimator for multiple-input multiple-output linear systems

Sumisławska M., Larkowski T., Burnham K. J.

Systems Science

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2010

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Vol. 36, no 3

49-59

EN

This paper presents a novel approach to the problem of unknown input estimation of multiple-input multiple-output systems. In the setup considered the measured output signals are affected by white zero-mean Gaussian measurement noise. The observer developed here is based on the parity equations concept and the Lagrange multiplier method is used to obtain an analytical solution for the filter parameters, hence minimising the unwanted effect of the noise. The paper extends the concept of the parity equation based unknown input observer approach, which has been previously developed by the authors for the case of single-input single-output systems. A simulation study is carried out, which illustrates a potential application of the proposed approach to a steel rolling mill for the purpose of improving the control performance.

2

Heating ventilation and air conditioning system; enhancement of energy consumption via control optimisation

Zajic I., Burnham K. J., Larkowski T., Hill D.

Systems Science

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2010

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Vol. 36, no 3

39-47

EN

Nowadays, because of higher energy costs and the awareness of the environmental issues a general trend can be observed to increase the energy efficiency of heating ventilation and air conditioning (HVAC) systems. The paper demonstrates that this aim can be achieved by highly stable control allowing for operation close to the specification limits, where the highest profitability can be obtained. In this regard, black-box models of both the controlled zone air temperature and humidity are constructed for a subsequent analysis of the present control system via a computer simulation. The results obtained are utilised for tuning of controllers on a real plant leading, subsequently, to substantial energy savings. The paper also investigates a potential scope for further energy savings.

3

Structured total least norm for MISO ARX system identification

Larkowski T., Vinsonneau B., Burnham K. J.

Systems Science

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2007

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Vol. 33, no 4

73-83

EN

The errors-in-variables (EIV) identification framework concerns the identification of dynamic models of systems where all the variables are corrupted by noise. The total least squares (TLS) is one of the most prominent techniques that has proven to be both robust and reliable. The structured total least norm (STLN) can be seen as a natural extension to TLS that preserves any affine structure of the joint data matrix, which is mostly the case in identification schemes. In contrast to the least squares (LS), TLS or mixed LS-TLS problems, the STLN solution cannot be expressed in a closed form, therefore, an optimization procedure is required. Note that STLN allows different norms to be considered other than the usual square norm (or 2 norm). This paper describes a direct application of the STLN approach for systems that can be represented by auto-regressive with exogenous input (ARX) multi-input single-output (MISO) models. The performance of the proposed STLN algorithm (in the case of the square norm) is compared to the LS, the bias-eliminating LS (BELS), the extended matrix LS (EMLS), the instrumental variables (IV), TLS and the compensated TLS (CTLS) methods when applied to a simulated MISO ARX system. Results, obtained from Monte Carlo simulation, show that, under the conditions considered here, STLN surpasses all other investigated techniques, attaining the best estimates of the true system parameters.