This paper presents the sensorless control algorithm for a permanent magnet synchronous motor (PMSM) drive system with the estimator and the intelligent controller. The estimator is constructed on the novel sliding mode observer (SMO) in combination with a phase-locked loop (PLL) to estimate the position and speed of the rotor. The intelligent controller is a radial basis function neural network (RBFNN)-based self-tuning PID (Proportional-Integral-Derivative) controller, applied to the velocity control loop of the PMSM drive control system to adapt strongly to dynamic characteristics during the operation with an external load. The I-f startup strategy is adopted to accelerate the motor from standstill, then switches to the sensorless mode smoothly. The control algorithm program is based on MATLAB and can be executed in simulations and experiments. The control system performance is verified on an experimental platform with various speeds and the dynamic load, in which the specified I-f startup mode and sensorless mode, inspected by tracking response and speed regulation. The simulation and experimental results demonstrate that the proposed method has worked successfully. The motor control system has smooth switching, good tracking response, and robustness against disturbance.

A neural fuzzy controller (NFC)-based speed controller for the sensorless permanent magnet synchronous motor (PMSM) drive control system is realized in this paper. The NFC is a fuzzy logic controller (FLC), which adjusts the RBFNN-based (radial basis function neural network) parameter by adapting the dynamic system characteristics. For sensorless PMSM drive, the integration of sliding mode observer (SMO) and phase-locked loop (PLL) is executed to estimate the rotor position and speed. To eliminate the initial rotor position estimation and overcome the conventional PLL-based position estimation error in the direction reversion transition, the I-f control strategy is applied to start up the motor and change the rotational direction effectively. The system performance was verified in various experimental conditions. The simulation and experimental results indicate that the proposed control algorithm is implemented efficiently. The motor starts up with diverse external loads, operates in a wide speed range for both positive and negative directions, and reverses the rotational direction stably. Furthermore, the system presents robustness against disturbance and tracks the command speed properly.

Information about rotor positions is critical when controlling a permanent-magnet synchronous motor (PMSM). This information can be gathered using a sensor or through an estimation without using a sensor. This article discusses a machine learning technique for estimating rotor positions. The proposed machine learning observer was constructed using a modified Elman neural network as the main algorithm. The network was trained offline with training data obtained from PMSM field-oriented control simulations and was tested using a validation data set. The PMSM control simulation results revealed that the rotor position estimated through machine learning was comparable with the simulated rotor position; the average error was 0.0127 per unit position. Furthermore, the machine learning model was implemented in an experimental PMSM-control hardware platform. Both the simulation and experimental results indicate that the proposed machine learning observer has an acceptable performance.

This article presents a double-integral sliding-mode controller with an adaptive proportional–integral–derivative observer for brushless direct current motor speed controller. First, an integral sliding-mode control is designed based on the motor dynamic model and system uncertainties. Accordingly, a novel double-integral sliding-mode controller is proposed to enhance the steady-state performance by employing the double-integral sliding surface with its inherent integral control feature. In addition, the control gains of the double-integral sliding-mode controller can be online adjusted using an adaptive proportional–integral–derivative observer. Thus, the proposed double-integral sliding-mode controller possesses the merits of integral sliding-mode control, proportional–integral–derivative, and adaptive law. An experimental setup including a digital signal processor is applied to verify the brushless direct current motor control system using different control scheme. The measured results show that satisfactory acceleration/deceleration speed tracking and load disturbance speed regulation characteristics are obtained via conventional proportional–integral–derivative controller and the developed integral sliding-mode control scheme under the various testing cases. Moreover, the maximum tracking error can be further reduced more than 50% by adopting the proposed double-integral sliding-mode controller with adaptive proportional–integral–derivative observer.

This study develops an adaptive recurrent neural network (NN) intelligentsliding‐mode controller (ARNISMC) for magnetic levitationsystem (MLS). First, a non‐linear dynamic model of the MLS is derived.Thereafter, a SM controller (SMC) method is presented to compensate for theuncertainties in the MLS. In addition, to enhance the control effort of aconventional SMC and further increase the tracking performance of the MLS, theuncertainty terms of the system dynamics can be estimated online by using an ARradial basis function NN estimator. Accordingly, the proposed controller notonly offers the accurate positioning tracking control, minimises steady‐stateerror, and improves conventional controller performance but also provides globalpositioning tracking control, which has not been previously reported in theliteratures. It can effectively solve problems associated with typical MLScontroller design. Moreover, the satisfactory tracking performance can beobserved from the experimental results by adopting the proposed ARNISMCscheme.