Control Method of Cold and Hot Shock Test of Sensors in Medium

Abstract

In order to meet the latest requirements for sensor quality test in the industry, the sample sensor needs to be placed in the medium for the cold and hot shock test. However, the existing environmental test chamber cannot effectively control the temperature of the sample in the medium. This paper designs a control method based on the support vector machine (SVM) classification algorithm and K-means clustering combined with neural network correction. When testing sensors in a medium, the clustering SVM classification algorithm is used to distribute the control voltage corresponding to temperature conditions. At the same time, the neural network is used to constantly correct the temperature to reduce overshoot during the temperature-holding phase. Eventually, overheating or overcooling of the basket space indirectly controls the rapid rise or decrease in the temperature of the sensor in the medium. The test results show that this method can effectively control the temperature of the sensor in the medium to reach the target temperature within 15 min and stabilize when the target temperature is between 145 °C and −40 °C. The steady-state error is less than 0.31 °C in the high-temperature area and less than 0.39 °C in the low-temperature area, which well solves the dilemma of the current cold and hot shock test.

Design and Implementation of a Recursive Feedforward-Based Virtual Reference Feedback Tuning (VRFT) Controller for Temperature Uniformity Control Applications

“…However, obtaining an acceptable process model can be challenging in processes with uncertainty and non-linear behavior, compromising the system's stability and performance [1][2][3][4]. This is especially relevant for processes that face variable environments that may require not only an acceptable model but also a constant parameter adaptation using optimization algorithms or adaptive control methodologies, such as [5][6][7][8][9][10][11]. Thus, the interest in data-driven control methods has been increasingly motivated by the increasing availability of process data given new technologies such as IoT, edge computing, or cheap sensing [12].…”