scholarly journals Robust adaptive fault estimation for multizone building VAV terminal units

2021 ◽  
Author(s):  
Mona Subramaniam ◽  
Tushar Jain ◽  
Joseph Yame
Keyword(s):  
Adaptive Estimation ◽  
Estimation Algorithm ◽  
Fault Estimation ◽  
Variable Air Volume ◽  
Energy Management Systems ◽  
Building Energy Management ◽  
Terminal Units ◽  
Robust Adaptive ◽  
Utility Cost ◽  
The Impact

In this paper, we propose a novel bilinear observer- based robust fault detection, isolation and adaptive fault estimation methodology to precisely estimate a class of actuator faults, namely bias in damper position and lock-in-place faults, in Variable-Air-Volume (VAV) terminal units of Heating Ventilation and Air-Conditioning (HVAC) systems. The proposed adaptive fault estimator is robust in the sense that the fault estimates are not affected by the unmeasured disturbance variable and that the effects of measurement noises on fault estimates are attenuated. The fault estimation algorithm with the integrated building control system improves occupants comfort and reduces the operation, maintenance, and utility cost, thereby reducing the impact on the environment. The effectiveness of the methodology for adaptive estimation of multiple or single VAV damper faults is successfully demonstrated through different simulation scenarios with SIMBAD (SIMulator of Building And Devices), which is being used in industries for testing and validation of building energy management systems.

scholarly journals Robust adaptive fault estimation for multizone building VAV terminal units

2021 ◽  
Author(s):  
Mona Subramaniam ◽  
Tushar Jain ◽  
Joseph Yame
Keyword(s):  
Adaptive Estimation ◽  
Estimation Algorithm ◽  
Fault Estimation ◽  
Variable Air Volume ◽  
Energy Management Systems ◽  
Building Energy Management ◽  
Terminal Units ◽  
Robust Adaptive ◽  
Utility Cost ◽  
The Impact

In this paper, we propose a novel bilinear observer- based robust fault detection, isolation and adaptive fault estimation methodology to precisely estimate a class of actuator faults, namely bias in damper position and lock-in-place faults, in Variable-Air-Volume (VAV) terminal units of Heating Ventilation and Air-Conditioning (HVAC) systems. The proposed adaptive fault estimator is robust in the sense that the fault estimates are not affected by the unmeasured disturbance variable and that the effects of measurement noises on fault estimates are attenuated. The fault estimation algorithm with the integrated building control system improves occupants comfort and reduces the operation, maintenance, and utility cost, thereby reducing the impact on the environment. The effectiveness of the methodology for adaptive estimation of multiple or single VAV damper faults is successfully demonstrated through different simulation scenarios with SIMBAD (SIMulator of Building And Devices), which is being used in industries for testing and validation of building energy management systems.

scholarly journals A Robust Adaptive Estimation Algorithm for Hamiltonian Multi-Agent Networks

2021 ◽  
Vol 5 (4) ◽  
pp. 1243-1248
Author(s):  
Hoorie Giv ◽  
Azam Khalili ◽  
Amir Rastegarnia ◽  
Saeid Sanei
Keyword(s):  
Adaptive Estimation ◽  
Estimation Algorithm ◽  
Multi Agent ◽  
Robust Adaptive ◽  
Agent Networks

scholarly journals Robust Fast Adaptive Fault Estimation for T-S Fuzzy Markovian Jumping Systems with Mode-Dependent Time-Varying State Delays

2021 ◽  
Vol 2021 ◽  
pp. 1-26
Author(s):  
Chao Sun ◽  
Shengjuan Huang ◽  
Libing Wu ◽  
Suhuan Yi
Keyword(s):  
Adaptive Estimation ◽  
Estimation Error ◽  
External Disturbance ◽  
Estimation Algorithm ◽  
Fault Estimation ◽  
Time Varying ◽  
Markovian Jumping ◽  
Markovian Jumping Systems ◽  
Stochastically Stable ◽  
Estimation Scheme

This paper studies the problem of actuator fault estimation for a class of T-S fuzzy Markovian jumping systems, which is subject to mode-dependent interval time-varying delays and norm-bounded external disturbance. Based on the given fast adaptive estimation algorithm and by employing a novel Lyapunov–Krasovskii function candidate, a robust fault estimation scheme is proposed to estimate faults whose derivative is bounded. With this improved method, the proposed fault estimator minimizes the effect of disturbance on the estimation error and reduces the conservatism of systems stability results simultaneously. To be specific, an improved mode-dependent criterion for the existence of the fault estimation observer is established to guarantee the error dynamic system to be stochastically stable with a prescribed H ∞ performance and reduce the conservatism of designing procedure. Finally, three numerical examples are given to show the effectiveness of the proposed method.

scholarly journals Adaptive Estimation Algorithm for Correcting Low-Cost MEMS-SINS Errors of Unmanned Vehicles under the Conditions of Abnormal Measurements

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 623
Author(s):  
Lifei Zhang ◽  
Proletarsky Andrey Viktorovich ◽  
Maria Sergeevna Selezneva ◽  
Konstantin Avenirovich Neusypin
Keyword(s):  
Adaptive Estimation ◽  
Low Cost ◽  
Experimental Tests ◽  
Estimation Algorithm ◽  
Satellite System ◽  
Unmanned Vehicles ◽  
Estimation Accuracy ◽  
Accuracy Class ◽  
Unmanned Vehicle ◽  
The Impact

In this paper, a low-cost small-sized strap-down inertial navigation system (SINS)—Gyrolab GL-VG 109—is studied. When the system is installed on an unmanned vehicle and works in autonomous mode, it is difficult to determine the navigation parameters of the unmanned vehicle. Correcting the SINS information from the Global Navigation Satellite System (GNSS) can significantly increase the determination accuracy of the navigation parameters. However, this is only available when the GNSS signals are stable. A new adaptive estimation algorithm that can automatically detect, evaluate, and process the abnormal measurements is proposed in the present work. The determination of the navigation parameters can reach the third accuracy class using the proposed method. The effectiveness of the algorithm is verified by the mathematical simulation and the experimental tests (with a real SINS GL-VG 109), which are conducted in urban environments with a GNSS signal containing 15% and 40% abnormal measurements. The results show that the proposed method can significantly reduce the impact of abnormal measurements and improve the estimation accuracy.

scholarly journals Bi-Level Poisoning Attack Model and Countermeasure for Appliance Consumption Data of Smart Homes

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3887
Author(s):  
Mustain Billah ◽  
Adnan Anwar ◽  
Ziaur Rahman ◽  
Syed Md. Galib
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Model Performance ◽  
Building Energy ◽  
Energy Prediction ◽  
Energy Management Systems ◽  
Attack Model ◽  
Building Energy Management ◽  
And Performance ◽  
The Impact

Accurate building energy prediction is useful in various applications starting from building energy automation and management to optimal storage control. However, vulnerabilities should be considered when designing building energy prediction models, as intelligent attackers can deliberately influence the model performance using sophisticated attack models. These may consequently degrade the prediction accuracy, which may affect the efficiency and performance of the building energy management systems. In this paper, we investigate the impact of bi-level poisoning attacks on regression models of energy usage obtained from household appliances. Furthermore, an effective countermeasure against the poisoning attacks on the prediction model is proposed in this paper. Attacks and defenses are evaluated on a benchmark dataset. Experimental results show that an intelligent cyber-attacker can poison the prediction model to manipulate the decision. However, our proposed solution successfully ensures defense against such poisoning attacks effectively compared to other benchmark techniques.

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