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The Permanent Magnet Synchronous Motor (PMSM) drive system is extensively utilized in high-precision positioning applications that demand superior dynamic performance across various operating conditions. Given the non-linear characteristics of the PMSM, a neuroadaptive sensorless controller based on B-spline neural networks is proposed to determine the control signals necessary for achieving the desired performance. The proposed control technique considers the system's non-linearities and can be adapted to varying operating conditions, all while maintaining a low computational cost suitable for real-time operation. The introduced neuroadaptive controller is evaluated under conditions of uncertainty, and its performance is compared to that of a conventional PI controller optimized using the Whale Optimization Algorithm (WOA). The results demonstrate the viability of the proposed approach. [ABSTRACT FROM AUTHOR]

A Proportional-Integral (PI) controller is a system designed to diminish the error between the desired reference value and the measured variable of the process to be regulated. The PI controller works according to two input parameters that must be calculated by a tuning process. In this work, the process is performed using intelligent swarm algorithms to seek for optimal parameters of PI controller. Three reactive, nature-inspired algorithms (bat, whale optimization and cuckoo search), were used to tune the PI controller. The control scheme based on linear regulators is used to improve the performance of the permanent magnet synchronous motor (PMSM) and to regulate the rotor speed to a desired reference value. In order to validate the effectiveness of the controller, simulation is performed under constant load condition, varying load condition and varying set speed conditions of the PMSM. [ABSTRACT FROM PUBLISHER]

Summary: Classical control design methods have been successfully applied to several practical engineering systems for regulation tasks around a desired equilibrium operation state. Nevertheless, real‐time information of some system parameters and mechanical torque could be required to guarantee an efficient and robust control performance for variable operation conditions. In this paper, an online and algebraic scheme is proposed for simultaneous estimation of parameters and variable mechanical load torque for nonlinear shunt DC motors. An adaptive PI velocity tracking control scheme is also described to verify the effectiveness and efficiency of the parametric estimation approach. Control dynamic gains are computed online by applying a B‐spline neural network algorithm. Computational simulation results confirm the fast estimation of parameters and variable mechanical torque. [ABSTRACT FROM AUTHOR]