Temperature Compensation and Pressure Bias Estimation for Piezoresistive Pressure Sensor Based on Machine Learning Approach

The requirements for accuracy of piezoresistive pressure sensors in modern society are becoming increasingly high. Besides, a wide application range of sensor is required. However, due to the influence of material properties, many piezoresistive pressure sensors have high temperature coefficient, which limits their application temperature range. With the change of ambient temperature, the response characteristic of the sensor has strong nonlinearity. To solve the above-mentioned crucial problems, a dynamic chaos quantum-behaved particle swarm optimization optimized multiple kernel relevance vector machine (DCQPSO-MKRVM) algorithm is presented in this article. First, a basic theory of temperature effect is given and a new idea of temperature compensation is proposed. Second, the multi-kernel relevance vector machine (MKRVM) is adopted to estimate the bias values of input pressure. Through heterogeneous kernel learning method, the kernels of MKRVM maintain diversity to obtain higher estimation accuracy. Third, dynamic chaos quantum-behaved particle swarm optimization (DCQPSO) is employed to optimize the optimal sparse weights of kernel functions in MKRVM. Moreover, the dynamic parameter is applied for the boundary of chaos search between original quantum-behaved particle swarm optimization (QPSO) swarm and the chaos swarm. The experimental result indicates the complex nonlinear relationship of temperature effect, and the method proposed in this article can effectively and accurately estimate the bias of input pressure fast to achieve temperature compensation goal. The mean relative accuracy (MRA) of estimation results achieves 99.5%. It proves that the method proposed in this article is applicable and effective for industrial applications.