| Summary: | The performance of the control system relies on the linearity of the sensor, which can be influenced by various factors such as aging and alterations in material properties. However, current sensor linearization techniques, such as utilizing neural networks and piecewise regression models in the digital domain, suffer from issues like errors, excessive power consumption, and slow response times. To address these constraints, this investigation employs a translinear based analog circuit to realize neural networks and piecewise regression models for the purpose of linearizing the selected sensors. A conventional feed-forward back propagation network is constructed and trained using the Levenberg-Marquardt algorithm. The developed linearization algorithm is implemented using a translinear circuit, where the trained weights, biases, and sensor output are fed as input current sources into the current-mode circuit. Further in this work, the piecewise regression model is designed and implemented using a translinear circuit and the breakpoint is determined using 'R' language. The simulation results indicate that the implementation of the current-mode circuit with metal-oxide-semiconductor field-effect transistors (MOSFETs) for the neural network algorithm leads to a substantial reduction in full-scale error as compared to the piecewise current mode model. Additionally, a performance analysis was conducted to compare the utilization of current-mode circuits with digital approaches for the linearization of sensors. The proposed translinear implementation surpasses the other researcher's work by delivering notable results. It showcases a significant improvement in linearity, ranging from 60% to 80%, for the selected sensors. Furthermore, the proposed implementation excels not only in linearity but also in terms of both response speed and power consumption. The improvement in the linearity of the sensor can be enhanced further by replacing the MOSFETs with bipolar transistors or any versatile materials such as gallium arsenide or gallium nitride-based transistors.
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