Adaptive inferential control

Guilandoust, M; Morris, A.J.; Tham, M.T.

doi:10.1049/ip-d.1987.0026

Cited by 41 publications

(5 citation statements)

References 3 publications

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“…An interesting point about the above equation is that an estimator of identical structure can also be derived by using a state space representation of the plant, following an altogether different approach to that adopted here (Guilandoust et al, 1987). This agreement between the results of the two different techniques provides additional confidence in the arguments made in this section.…”

mentioning

confidence: 64%

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An adaptive estimation algorithm for inferential control

Guilandoust¹,

Morris²,

Tham³

1988

Ind. Eng. Chem. Res.

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An estimator relationship for inferring infrequently measured process outputs, from other more rapidly sampled secondary outputs, is derived. The structure of the estimator is not application dependent, and its parameters can be continuously estimated and updated. As a result, slow variations in plant or disturbance characteristics can be tracked. Closed-loop control schemes using the estimated output data are shown to exhibit superior performance over those using the slowly measured output data. In contrast to some other inferential control strategies suggested in the literature, the method proposed requires minimal effort for estimator design and secondary output selection.

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mentioning

confidence: 64%

“…This approach is described elsewhere (Guilandoust et al, 1987) and has been briefly compared with the input-output method to be described here (Guilandoust and Morris, 1985). The algorithm is derived from an unknown general input-input representation of the plant.…”

Section: -"~+"~G(z-')h~(z-~)/h~(z-')mentioning

confidence: 99%

An adaptive estimation algorithm for inferential control

Guilandoust¹,

Morris²,

Tham³

1988

Ind. Eng. Chem. Res.

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“…It is important to note that the previous multirate formulation does not require a fixed integer ratio of the minor and major sampling instants. In earlier approaches to multirate identification ( Lu and Fisher, 1990;Guilandoust et al, 1987) using transfer function models, it was required that the major sampling time arrives every fixed integer, say J , sampling intervals of the minor sampling instants. This is too restrictive a requirement for the resulting strategy to be generally applicable at an implementation level.…”

Section: Multirate Iterated Extended Kalman Filtermentioning

confidence: 99%

“…To achieve this objective when the process is time varying, adaptive multirate system identification strategies need to be used to formally accommodate the two or more sampling rates that exist in such a scenario. Guilandoust et al (1987Guilandoust et al ( , 1988 proposed an adaptive multirate inferential estimation algorithm in state space and transfer function form. Lu and Fisher (1990) have formulated the multirate estimation problem such that the working equations explain the inferential relationships in a more fundamental way and thus have formally proved the convergence properties of such an inferential control algorithm.…”

Section: Introductionmentioning

confidence: 99%

Adaptive multirate state and parameter estimation strategies with application to a bioreactor

1995

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“…The soft sensing technology was originally developed to improve the control of chemical and biological processes [67][68][69][70][71]. Over the last three decades enormous efforts have been made to develop soft sensors and soft sensor based control schemes [9,11,72].…”

Section: Introductionmentioning

confidence: 99%

Grey box modelling and advanced control scheme for building heating systems

Jassar¹

2021

Preprint

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This dissertation is aimed at generating new knowledge on Recurrent Neuro-Fuzzy Inference Systems (RenFIS) and to explore its application in building automation. Inferential sensing is an attractive approach for modeling the behavior of dynamic processes. Inferential sensor based control strategies are applied to optimize the control of residential heating systems and demonstrate significant energy saving and comfort improvement. Despite the rapidly decreasing cost and improving accuracy of most temperature sensors, it is normally impractical to use a lot of sensors to measure the average air temperature because the wiring and instrumentation can be very expensive to install and maintain. To design a reliable inferential sensor, of fundamental importance is to build a simple and robust dynamic model of the system to be controlled. This dissertation presents the development of an innovative algorithm that is suitable for the robust black-box model. The algorithm is derived from ANFIS (Adaptive Neuro-Fuzzy Inference System) and is referred to as RenFIS. Like all other modelling techniques, RenFIS performance is sensitive to the training data. In this study, RenFIS is used to model two different heating systems, hot water heating system and forced warm-air heating system. The training data is collected under different operational conditions. RenFIS gives better performance if trained with the data set representing overall qualities of the whole universe of the experimental data. The robustness analysis is conducted by introducing simulated noise to the training data. Results show that RenFIS is less sensitive than ANFIS to the quality of training data. The RenFIS based inferential sensor is then applied to design and inferential control algorithm that can improve the operation of residential heating systems. In current practice, the control of heating systems is based on measurement of the air temperature at one point within the building. The inferential control strategy developed through this study allows the control to be based on an estimate of the overall thermal performance. This is achieved through estimating the average room temperature using a RenFIS based inferential sensor and incorporating the estimate with conventional control technology. The performance of this control technology has been investigated through simulation study.

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Adaptive inferential control

Cited by 41 publications

References 3 publications

An adaptive estimation algorithm for inferential control

An adaptive estimation algorithm for inferential control

Adaptive multirate state and parameter estimation strategies with application to a bioreactor

Grey box modelling and advanced control scheme for building heating systems

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