This study designs a simple current controller employing deep symbolic regression (DSR) in a surface-mounted permanent magnet synchronous machine (SPMSM). A novel DSR-based optimal current control scheme is proposed, which after proper training and fitting, generates an analytical dynamic numerical expression that characterizes the data. This creates an understandable model and has the potential to estimate data that have not been seen before. The goal of this study was to overcome the traditional linear proportional–integral (PI) current controller because the performance of the PI is highly dependent on the system model. Moreover, the outer speed control loop gains are tuned using the cuckoo search algorithm, which yields optimal gain values. To demonstrate the efficacy of the proposed design, we apply the control design to different test cases, that is varied speed and load conditions, as well as sinusoidal speed reference, and compare the results with those of a traditional vector control design. Compared with traditional control approaches, we deduce that the DSR-based control design could be extrapolated far beyond the training dataset, laying the foundation for the use of deep learning techniques in power conversion applications.

The objective of the paper is to present the efficient and dynamic sensorless speed control of a surface permanent magnet synchronous motor (SPMSM) drive at a wide speed range. For high-performance speed sensorless control, a finite control set model predictive current control (FCS-MPCC) algorithm based on a model reference adaptive system (MRAS) is proposed. With the FCS-MPCC algorithm, the inner current control loop is eliminated, and the limitations of the cascaded linear controller are overcome. The proposed speed sensorless control algorithm provides an efficient speed control technique for the SPMSM drive owing to its fast dynamic response and simple principle. The adaptive mechanism is adopted to estimate the rotor shaft speed and position used in FCS-MPCC for dynamic sensorless control. FCS-MPCC uses a square cost function to determine the optimal output voltage vector (VV) from the switching states that give the low cost index. A discrete-time model of a system is used to predict future currents across all the feasible VVs produced by the voltage source inverter. The VV that reduced the cost function is adopted and utilized. Simulation results showed the efficacy of the presented scheme and the viability of the proposed sensorless speed control design under various load conditions at a wide speed operation range.

This paper presents a novel control strategy employing a deep reinforcement learning (DRL) scheme for online selection of optimal weighting factors in cost functions of the finite control set model predictive current controller of a permanent magnet synchronous motor (PMSM). Indeed, when designing predictive controllers for PMSMs’ phase currents, competing objectives appear, such as managing current convergence and switching transitions. These objectives result in an asymmetric cost function where they have to be balanced through weighting factors in order to enhance the inverter and motor performance. Leveraging the twin delayed deep deterministic policy gradient algorithm, the optimal weighting factor selection policy is obtained for online balancing of the choice between current deviation in the $dq$ frame and inverter commutations. For comparison, a metaheuristic-based artificial neural network is trained on static data obtained through a multi-objective genetic algorithm to predict the weights. The key performance markers, such as torque ripple, total harmonic distortion, switching frequency, steady-state, and dynamic performance, are provided through numerical simulations to verify the effectiveness of the proposed tuning scheme. The results of these simulations confirm that the proposed dynamic control scheme effectively resolves the challenges of weighting factor choice, meeting inverter performance requirements, and delivering better dynamic and steady-state performance.

The speed response of the interior permanent magnet synchronous motor (IPMSM) drive at low speeds was analyzed. To eliminate the effect of external disturbance or parameter uncertainty, a nonlinear speed control loop was designed based on the sliding-mode exponential reaching law, which reduces chatter, which is the major drawback of the constant reaching law sliding-mode control technique. The proposed nonlinear speed control eliminates speed ripples at low speed under load disturbance. The problem of speed convergence at low speed is caused by electromagnetic torque ripples, which cause shaft speed oscillations that affect drive performance. The main objective of the proposed method is to change the traditional IPMSM control design by compensating with an appropriate signal along the reference current and across the output of the speed control loop. To optimize the speed tracking performance during disturbances or parametric variations, a nonlinear speed control scheme is designed that can vigorously adapt to the change in the controlled system. The comparative analysis shows that the method provides excellent transient performance (e.g., fast convergence response, less overshoot, and fast settling time) and standstill performance (e.g., reduced steady-state error) compared with conventional control methods at low speed under varying load conditions. The method is easy to implement and does not require additional computational cost. To demonstrate the effectiveness and feasibility of the design approach, a numerical analysis was conducted, and the control scheme was verified using MATLAB/Simulink considering various operating conditions.

A comparative analysis of vector control scheme based on different current control switching pulses (HC, SPWM, DPWM and SVPWM) for the speed response of motor drive is analysed in this paper. The control system using different switching techniques, are comparatively simulated and analysed. Ac motor drives are progressively used in high-performance application industries due to small size, efficient performance, robust to torque response and high power to size ratio. A mathematical model of ac motor drives is presented in order to explain the numerical theory of motor drives. The vector control technique is utilized for efficient speed control of ac motor drive based on independent torque and air gap flux control. The study compares the total harmonic distortion contents of phase currents of ac motor drive and speed response in each case. The simulation result shows that total harmonic distortion across the phase current in SVPWM is less as compared to other switching techniques while the rise time in speed response across SVPWM technique is faster as compared to other switching methods. The simulation result of ac motor drives speed control is demonstrated in Matlab/Simulink 2018b.

With the advancement in semiconductor technology, the speed control of ac motor drive becomes easy. The development of the microprocessor has encouraged the research of the most efficient and economical speed control algorithms. A model predicted current control method is designed and used the discrete-time model of output state variable of the motor drive to predict the future value of output state variable across all the voltage vector generated by voltage source inverter. The squared cost function method is utilized to evaluate the current error. As compared to the conventional cascaded control method, the Model Predicted Control has better performance. The Simulation model is designed in Matlab/Simulink 2018b, which shows the excellent dynamic and steady-state performance of the control algorithm. The simulation results are demonstrated to verify the proposed method.