A Fuzzy-Based Proportional–Integral–Derivative with Space-Vector Control and Direct Thrust Control for a Linear Induction Motor

Abstract

In this study, the analysis and control of a multi-phase linear induction motor loaded with a variable mechanical system are carried out. Mathematical models are established, and simulation results are analyzed for an improved proportional–integral–derivative controller with closed-loop vector control for PLIM. To make the PID controller more responsive to load thrust disturbances, a fuzzy PID load thrust observer was developed. The FPID is similarly based on space-vector modulation DTC technology to regulate the PLIM’s speed, flux, and thrust. The FPID output is used to calculate the reference thrust force, which is compared to the actual thrust value to calculate the second error. To maintain the linear speed of the PLIM at the specified reference values and at different load values, the FPID controller settings are adjusted. Four indicators were used to compare the capabilities of the FPID controller with those of the conventional PID controller in order to evaluate the performance of PLIM in both cases. These indices represent the individual SSE for each operational phase and the total SSE for the entire loading period. According to the simulation results, the FPID works better than a regular PID when used to adjust the operation of DTC-SVM to drive a PLIM to improve the overall system performance. The simulation results using MATLAB Simulink for a PLIM-drive system show that the proposed FPID control provides improved control behavior and operating performance with fast and accurate speed tracking.

Grouping Neural Network-Based Smith PID Temperature Controller for Multi-Channel Interaction System

“…The goal of the Grouping-NN is to find the optimal network weights to minimize the error between predicted and actual values. The loss function is computed independently for each channel, shown in Equation (14). The partial derivative of the loss function vector for ŷnet (k) is shown in Equation (15).…”

“…The proportional, integral, and differential coefficients of PID controllers need to be tuned before control [11]. For some scenarios where the coefficients need to be adjusted in real time, some adaptive control methods have been proposed, such as fuzzy PID methods [12][13][14] and neural network-based PID methods [10,15]. In addition, in order to reduce the impact of time delay, many Smith predictor-based methods are used to modify PID controllers [16,17].…”

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