A multi-objective optimal PID control for a nonlinear system with time delay

It is generally difficult to design feedback controls of nonlinear systems with time delay to meet time domain specifications such as rise time, overshoot, and tracking error. Furthermore, these time domain specifications tend to be conflicting to each other to make the control design even more challenging. This paper presents a cell mapping method for multi-objective optimal feedback control design in time domain for a nonlinear Duffing system with time delay. We first review the multi-objective optimization problem and its formulation for control design. We then introduce the cell mapping method and a hybrid algorithm for global optimal solutions. Numerical simulations of the PID control are presented to show the features of the multi-objective optimal design

Wearable Microfluidic Diaphragm Pressure Sensor for Health and Tactile Touch Monitoring

Flexible pressure sensors have many potential applications in wearable electronics, robotics, health monitoring, and more. In particular, liquid-metal-based sensors are especially promising as they can undergo strains of over 200% without failure. However, current liquid-metal-based strain sensors are incapable of resolving small pressure changes in the few kPa range, making them unsuitable for applications such as heart-rate monitoring, which require a much lower pressure detection resolution. In this paper, a microfluidic tactile diaphragm pressure sensor based on embedded Galinstan microchannels (70 µm width × 70 µm height) capable of resolving sub-50 Pa changes in pressure with sub-100 Pa detection limits and a response time of 90 ms is demonstrated. An embedded equivalent Wheatstone bridge circuit makes the most of tangential and radial strain fields, leading to high sensitivities of a 0.0835 kPa^(−1) change in output voltage. The Wheatstone bridge also provides temperature self-compensation, allowing for operation in the range of 20–50 °C. As examples of potential applications, a polydimethylsiloxane (PDMS) wristband with an embedded microfluidic diaphragm pressure sensor capable of real-time pulse monitoring and a PDMS glove with multiple embedded sensors to provide comprehensive tactile feedback of a human hand when touching or holding objects are demonstrated

Wearable Microfluidic Diaphragm Pressure Sensor for Health and Tactile Touch Monitoring

Flexible pressure sensors have many potential applications in wearable electronics, robotics, health monitoring, and more. In particular, liquid-metal-based sensors are especially promising as they can undergo strains of over 200% without failure. However, current liquid-metal-based strain sensors are incapable of resolving small pressure changes in the few kPa range, making them unsuitable for applications such as heart-rate monitoring, which require a much lower pressure detection resolution. In this paper, a microfluidic tactile diaphragm pressure sensor based on embedded Galinstan microchannels (70 µm width × 70 µm height) capable of resolving sub-50 Pa changes in pressure with sub-100 Pa detection limits and a response time of 90 ms is demonstrated. An embedded equivalent Wheatstone bridge circuit makes the most of tangential and radial strain fields, leading to high sensitivities of a 0.0835 kPa^(−1) change in output voltage. The Wheatstone bridge also provides temperature self-compensation, allowing for operation in the range of 20–50 °C. As examples of potential applications, a polydimethylsiloxane (PDMS) wristband with an embedded microfluidic diaphragm pressure sensor capable of real-time pulse monitoring and a PDMS glove with multiple embedded sensors to provide comprehensive tactile feedback of a human hand when touching or holding objects are demonstrated

An Image-Based Double-Smoothing Cohesive Finite Element Framework for Particle-Reinforced Materials

In order to simulate the fracture process of particle-reinforced materials on the micro-scale, an image-based double-smoothing cohesive finite element framework is proposed in the present paper. Two separate smoothing processes are performed to reduce the noise in the digital image and eliminate the jagged elements in the finite element mesh. The main contribution of the present study is the proposed novel image-based cohesive finite element framework, and this method improved the quality of the meshes effectively. Meanwhile, the artificial resistance due to the jagged element is reduced with the double-smoothing cohesive finite element framework during the crack propagation. Therefore, the image-based double-smoothing cohesive finite element framework is significant for the simulation of fracture mechanics

Investigation on Analysis Method of Environmental Fatigue Correction Factor of Primary Coolant Metal Materials in LWR Water Environment

The environmental fatigue correction factor (Fen) is mainly used to analyze the influence of the coolant environment on the fatigue life of primary metal materials. Because the calculation of the transformed strain rate is related to the stress history of the component structure, how to determine the strain rate is the most critical step in calculating the Fen. The approaches of the detailed method were given by the Electric Power Research Institute (EPRI) guidelines and RCC-M-2017 Edition Section VI- RPP No. 3 separately, so a gap analysis was performed between the two methods. Furthermore, another average method was also proposed to determine the average strain rate and strain range. Based on the analysis benchmark provided in the EPRI guideline, a simple case study was performed to account for the effect on the fatigue life in applications with different strain rate approaches and different Fen expressions. Finally, two industry case studies were also completed, including on materials of low alloy steel, austenitic stainless steel, and nickel-base alloy. We suggest adopting a more accurate detailed method, and its methodology is recommended to provide more reasonable solutions

Simplified Elastoplastic Fatigue Correction Factor Analysis Approach Based on Minimum Conservative Margin

ASME and RCC-M codes specify an elastoplastic fatigue analysis technique: a simplified elastoplastic fatigue analysis method based on linear elastic analysis. In this method, the elastic strain range is multiplied by the elastoplastic correction factor (Ke) to envelope the actual plastic strain range for fatigue evaluation. The ASME or RCC-M provide the Ke parameters of typical materials, such as austenitic stainless steel and low alloy steel. However, how can the parameters of the material not included in the codes be determined? Based on the existing material Z2CND18.12 (nitrogen control) in the codes and taking into account various sensitive factors, the minimum conservative margin of Ke for this material is calculated, and then the parameters of nonstandard materials are determined iteratively based on the conservative margin. The sensitive factors include the different structure model, load types, the loading control mode, temperature value and the material constitutive model. Based this approach, the Ke parameters of TA16 are determined and verified by the transient with drastic change in temperature and pressure. The results of the case show that the simplified elastoplastic fatigue analysis can envelope the results of cyclic plastic fatigue analysis. The minimum margin approach established in this paper can reasonably determine the Ke value of materials beyond the ASME and RCC-M codes

Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation

The advent of the big data era brought new opportunities and challenges to draw treatment effect in data fusion, that is, a mixed dataset collected from multiple sources (each source with an independent treatment assignment mechanism). Due to possibly omitted source labels and unmeasured confounders, traditional methods cannot estimate individual treatment assignment probability and infer treatment effect effectively. Therefore, we propose to reconstruct the source label and model it as a Group Instrumental Variable (GIV) to implement IV-based Regression for treatment effect estimation. In this paper, we conceptualize this line of thought and develop a unified framework (Meta-EM) to (1) map the raw data into a representation space to construct Linear Mixed Models for the assigned treatment variable; (2) estimate the distribution differences and model the GIV for the different treatment assignment mechanisms; and (3) adopt an alternating training strategy to iteratively optimize the representations and the joint distribution to model GIV for IV regression. Empirical results demonstrate the advantages of our Meta-EM compared with state-of-the-art methods. The project page with the code and the Supplementary materials is available at https://github.com/causal-machine-learning-lab/meta-em

System Identification and Fractional-Order Proportional–Integral–Derivative Control of a Distributed Piping System

The vibration of piping systems is one of the most important causes of accelerated equipment wear and reduced work efficiency and safety. In this study, an active vibration control method based on a fractional-order proportional–integral–derivative (PID) controller was proposed to suppress pipeline vibration and reduce pipeline damage. First, a mathematical model of the distributed piping system was established using the finite element analysis method, and the characteristics of the distributed piping system were studied effectively. Further, the time-frequency domain parameter identification method was used to realise the system identification of the cross-point vibration transfer function between the brake and sensor, and the particle swarm optimisation algorithm was utilised to further optimise the transfer function parameters to improve the system identification accuracy. Therefore, a fractional-order PID controller was designed using the D-decomposition method, and the optimal controller parameters were obtained. The experimental and numerical simulation results show that the improved system identification algorithm can significantly improve modelling accuracy. In addition, the designed fractional-order PID controller can effectively reduce the system’s overshoot, oscillation time, and adjustment time, thereby reducing the vibration response of piping systems