Hysteresis Nonlinearity Identification Using New Preisach Model-Based Artificial Neural Network Approach

Preisach model is a well-known hysteresis identification method in which the hysteresis is modeled by linear combination of hysteresis operators. Although Preisach model describes the main features of system with hysteresis behavior, due to its rigorous numerical nature, it is not convenient to use in real-time control applications. Here a novel neural network approach based on the Preisach model is addressed, provides accurate hysteresis nonlinearity modeling in comparison with the classical Preisach model and can be used for many applications such as hysteresis nonlinearity control and identification in SMA and Piezo actuators and performance evaluation in some physical systems such as magnetic materials. To evaluate the proposed approach, an experimental apparatus consisting one-dimensional flexible aluminum beam actuated with an SMA wire is used. It is shown that the proposed ANN-based Preisach model can identify hysteresis nonlinearity more accurately than the classical one. It also has powerful ability to precisely predict the higher-order hysteresis minor loops behavior even though only the first-order reversal data are in use. It is also shown that to get the same precise results in the classical Preisach model, many more data should be used, and this directly increases the experimental cost

Optimization of Multi-zone Building HVAC Energy Consumption by Utilizing Fuzzy Model Based Predictive Controller

The rapid improvement of living standards has led to increased energy consumption in buildings worldwide. Globally, the energy consumed in buildings accounts for 20.1% of total delivered energy (EIA 2016). Improving energy efficiency in buildings therefore is an important component for combating climate change. This paper aims to improve end use energy efficiency in multi-zoned residential buildings through the application of thermal comfort based, energy optimization algorithms. We use a case study approach with a detailed analysis of a 4-story residential apartment building in central Illinois. The study building constitutes 21 thermal zones modeled in EnergyPlus. The model is validated using monthly energy consumption data. The effectiveness of four different steam heating system control methods are evaluated and described: a) a Model Predictive Controller (MPC) design based on neuro-fuzzy temperature predictor; b) a Proportional-Integral-Derivative (PID) tuned by fuzzy logic; c) a PID tuned by a genetic algorithm; and d) an on/off controller and the flow regulator based on indoor temperature. All are optimized for energy consumption reduction potential and thermal comfort. The main effect of the various control methods is tuning boiler feed flow by regulating the condensing cycle. A reduction in circulated steam flow results in decreased direct energy consumption and improved condensing pump efficiencies. We find that the MPC design using a neurofuzzy temperature predictor can reduce heating energy use by up to 38% in comparison with an on/off controller baseline

Modelling Hysteresis in Shape Memory Alloys Using LSTM Recurrent Neural Network

The complex behavior of shape memory alloys (SMAs), characterized by hysteresis and nonlinear dynamics, results in complex constitutive equations. To circumvent the complexity of solving these equations, a black box neural network (NN) has been employed in this research to model a rotary actuator actuated by an SMA wire. Considering the historical dependence of the pulley’s rotational angle on the applied voltage, a recurrent neural network (RNN) is suitable for capturing past information. Specifically, a long short-term memory (LSTM) neural network is selected due to its ability to address issues encountered in standard recurrent networks. There are major drawbacks with modelling hysteresis with NNs that do not account for historical behavior. Traditional NNs, characterized by a one-to-one mapping, struggle to capture hysteresis loops wherein system behavior varies during loading and unloading cycles. Therefore, a single-tag data is used to determine the loading or unloading state, but tag signal causes discontinuity in network and omits various aspects of hysteresis in SMA, particularly within minor loops. In contrast, NNs incorporating past data to predict hysteresis behavior alleviate the need for tag data. However, such networks tend to have complex structures with a substantial number of neurons to effectively capture the inherent nonlinearity in SMAs. The long short-term memory (LSTM) neural network employed in this research, characterized by a simpler structure, achieves high accuracy in predicting hysteresis in SMAs without the need for tag data. In the proposed LSTM model, data related to the pulley’s rotational angle and the wire’s applied voltage from the current moment and the two previous moments serve as input. The data passes through a layer comprising three LSTM cells, and the output from the last LSTM cell is fed into a fully connected layer to predict the pulley’s rotational angle for the next moment. Training data are obtained by applying voltage at various frequencies and formats to the SMA wire while simultaneously recording the pulley’s angle with an encoder. Evaluation of the LSTM model is conducted in two configurations: online prediction (one-step ahead) and offline prediction (multistep ahead). In the online configuration where the model uses encoder data as angular inputs, the root mean square error (RMSE) of predictions for various input voltages is significantly low at about 0.1 degrees where the maximum rotational angle of pulley is 8 degrees. In the offline configuration when using the model’s predictions as angular inputs instead of encoder data, the RMSE rises to 0.3 degrees. To provide a clear demonstration of the LSTM model’s ability in this particular configuration, a comparison has been conducted between LSTM model and a rate-dependent Prandtl-Ishlinskii (RDPI) hysteresis model for predicting the pulley’s angle. The LSTM model outperforms the RDPI model by 70% in terms of accuracy. Overall, the LSTM model demonstrates capability in effectively modeling SMA hysteresis in both online and offline configurations

How Multi-Criterion Optimized Control Methods Improve Effectiveness of Multi-Zone Building Heating System Upgrading

This paper aims to develop multi-objective optimized control methods to improve the performance of retrofitting building heating systems in reducing consumed energy as well as providing comfortable temperature in a multi-zone building. While researchers evaluate various controllers in specific systems, providing a comprehensive controller for retrofitting the existing heating systems of multi-zone buildings is less investigated. A case study approach with a four-story residential building is simulated. The building energy consumption is modeled by EnergyPlus. The model is validated with energy data. Then, the building steam system model is upgraded, and in the other case, renewed by a hydronic system instead of a steam one. Three optimized controller groups are developed, including Model Predictive Controller (MPC), fuzzy controllers (Fuzzy Logic Controller (FLC) and an Optimized Fuzzy Sliding Mode Controller (OFSMC)), and optimized traditional ones. These controllers were applied to the upgraded steam and hydronic heating systems. The control methods affected the tuning of the boiler feed flow by regulating the condensing cycle and circulating the pump flow of the hydronic system. Accordingly, renewing the heating system improves energy efficiency by up to 29% by implementing a hydronic system instead of the steam one. The fuzzy controllers increased renewing effectiveness by providing comfortable temperatures and reducing building environmental footprints by up to 95% and 12%, respectively, compared with an on/off controller baseline

Echocardiographic Assessment of Systolic Pulmonary Arterial Pressure in HIV-Positive Patients

Pulmonary hypertension is rare but is one of the complications that occur due to HIV infection. Symptoms of HIV-associated pulmonary arterial hypertension are often non-specific but the main symptom of the disease is dyspnea. In this cross-sectional study, we measured systolic pulmonary arterial pressure (SPAP) by echocardiographic methods among HIV-positive patients who received ART. This research is a descriptive, cross-sectional study of 170 HIV-positive patients that was conducted in Imam-Khomeini hospital, Tehran, Iran during 2011-2013. All patients regularly received antiretroviral therapy at least for recent 2 years. There were not any cardiopulmonary symptoms (cough, dyspnea, exertional fatigue and chest discomfort) in these patients. All participants underwent echocardiography to estimate SPAP. The participants comprised 108 males (63.5%) and 62 females (46.5%). The mean age of patients was 41 years old, and the mean duration of HIV infection was 5.5 years. The mean CD4 cell count was 401 cell/µl. The principal regimen of antiretroviral therapy included two nucleoside reverse transcriptase inhibitor (NRTI) and one non-nucleoside reverse transcriptase inhibitor (NNRTI) in the hospital. The mean of systolic pulmonary arterial pressure was 25 mmHg in the participants; 156 (93.4%) of them had SPAP ≤ 30 mmHg (normal), six (3.6%) had SPAP: 31-35 mmHg (borderline) and five (3%) had SPAP > 35 mmHg (pulmonary hypertension). Our results indicated a significant increase of pulmonary hypertension in asymptomatic HIV-positive patients that had no association with any other risk factor. Also, antiretroviral therapy was not a risk factor for pulmonary hypertension in this study