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This paper presents an underwater snake robot composed of submersible actuators designed for minimal friction, a lubricant-free gear reducer, and no waterproof sealing. This makes it suitable for direct exposure to water. In particular, this paper focuses on underwater interactive tasks with an object. Static force analysis for straightforward tasks, such as the wrapping of a pole structure, is conducted. Experiments were performed to evaluate the snake robot outside a water environment. The results indicated that the static model was valid, although the errors were not negligible. The potential of executing various tasks with this sensorless underwater snake robot, such as wrapping around the pole and its collection or turning on/off a lever underwater, is presented.

The paper presents a novel model predictive flux control (MPFC) scheme for three-level inverter-fed sensorless induction motor drive operated in a wide speed region, including field weakening. The novelty of the proposed drive lies in combining in one system a number of new solutions providing important features, among which are: very high dynamics, constant switching frequency, no need to adjust weighting factors in the predictive cost function, adaptive speed and parameter (stator resistance, main inductance) estimation. The theoretical principles of the optimal switching sequence predictive stator flux control (OSS-MPFC) method used are also discussed. The method guarantees constant switching frequency operation of a three-level inverter. For speed estimation, a compensated model reference adaptive system (C-MRAS) was adopted while for IM parameters estimation a Q-MRAS was developed. Simulation and experimental results measured on a 50 kW drive that illustrates operation and performances of the system are presented. The proposed novel solution of a predictive controlled IM drive presents an attractive and complete algorithm/system which only requires the knowledge of nominal IM parameters for proper operation.

This paper introduces a novel approach to speed-sensorless predictive torque control (PTC) in an autonomous wind energy conversion system, specifically utilizing an asymmetric double star induction generator (ADSIG). To achieve accurate estimation of non-linear quantities, the Gaussian Process Regression algorithm (GPR) is employed as a powerful machine learning tool for designing speed and flux estimators. To enhance the capabilities of the GPR, two improvements were implemented, (a) hyperparametric optimization through the Bayesian optimization (BO) algorithm and (b) curation of the input vector using the gray box concept, leveraging our existing knowledge of the ADSIG. Simulation results have demonstrated that the proposed GPR-PTC would remain robust and unaffected by the absence of a speed sensor, maintaining performance even under varying magnetizing inductance. This enables a reliable and cost-effective control solution.

This study presents a simple sensorless algorithm based on the high-frequency signal injection for an interior permanent magnet synchronous motor. The sensorless drive using a square-wave-type injection signal has an enhanced control bandwidth and reduced acoustic noise owing to the reduction of filters and availability of high injection frequency. However, this method still needs discrete filters to extract the fundamental and the injected frequency component currents; so it has a limitation in enhancing the sensorless control performance. Therefore this study proposes a simple algorithm, which eliminates these filters and further simplifies the signal process for estimating the rotor position. As a result, the overall sensorless control can be implemented easily without any filters while providing an enhanced dynamics. Additionally, a detection method of an initial rotor position for start-up by using the same square-wave-type voltage injection is introduced. The experimental result shows that the speed control bandwidth in the sensorless drive simplified by the proposed algorithm becomes very close to the one achieved in sensored drives.

Owing to the widely used brushless DC motors (BDCMs) in high-efficiency applications, many position sensorless control methods based on voltage source inverters had been developed in the literature. Recently, current source inverters (CSIs) are receiving more and more attention because of their inherent short-circuit protection characteristics. But no position sensorless control for buck-type CSI with square-wave current had been found in the literature. In this study, the buck-type CSI-fed BDCM drive is designed and its application to the square-current position sensorless control is first proposed. The provided simulation and experimental results verify the effectiveness of the proposed CSI-based position sensorless control.

Purpose This paper aims to present a novel position sensorless control scheme with fault-tolerance ability for switched reluctance motor at low speed. Design/methodology/approach First, the detection pulses are injected in the freewheeling and idle intervals of each phase. Second, the aligned position of each phase can be detected by comparing the consecutive rise time of detection current. Third, the whole-region rotor position and real-time rotational speed can be updated four times for the improvement of detection accuracy. Finally, the fault-tolerant control strategy is performed to enhance the robustness and reliability of proposed sensorless scheme under faulty conditions. Findings Based on proposed sensorless control strategy, the estimated rotor position is in good agreement with the actual rotor position and the maximum rotor position error is 1.5°. Meanwhile, the proposed sensorless scheme is still effective when the motor with multiphase loss and the maximum rotor position error is 1.9°. Moreover, the accuracy of the rotor position estimation can be ensured even if the motor is in an accelerated state or decelerated state. Originality/value The proposed sensorless method does not require extensive memory, complicated computation and prior knowledge of the electromagnetic properties of the motor, which is easy to implement. Furthermore, it is suitable for different control strategies at low speed without negative torque generation.

In this study, a direct torque and flux control is described for a six-phase asymmetrical speed and voltage sensorless induction machine (IM) drive, based on non-linear backstepping control approach. First, the decoupled torque and flux controllers are developed based on Lyapunov theory, using the machine two axis equations in the stationary reference frame. In this control scheme, the actual stator voltages are determined from dc-link voltage using the switching pattern of the space vector pulse-width modulation inverter. Then, for a given motor load torque and rotor speed, a so-called fast search method is used to maximise the motor efficiency. According to this method, the rotor reference flux is decreased in the small steps, until the average of real input power to the motor reaches to a minimum value. In addition, a model reference adaptive system-based observer is employed for online estimating of the rotor speed. Finally, the feasibility of the proposed control scheme is verified by simulation and experimental results.

This study reports the implementation of a sensorless control scheme for AC machines that requires neither a fundamental model observer nor a test-signal. The model is based on saliency detection through the use of fundamental PWM excitation and the measurement of the derivatives of the line currents induced by the PWM voltage vectors. Derivation of the rotor position is possible at low and zero speeds without separate test signals and also at higher speed without the knowledge of the machine's fundamental model. Experimental results showing fully sensorless induction motor control at low and higher speeds validate the principle of this method.

This article proposes sensorless multiscalar control for a multiphase interior permanent magnet synchronous machine. The chosen parameters are estimated using an adaptive observer structure. In the proposed solution, the machine model vector form is in the stationary reference frame ( ), and transformation to ( ) – the coordinate system is unnecessary to implement the proposed control structure. In the control structure, the nonlinear model linearization is based on demonstrated nonlinear variables transformation for ( )( ) orthogonal planes. Using the proposed control technique, mechanical and electromagnetic subsystems are decoupled, which is the main advantage of this control structure. To provide a comparative analysis, the proposed multiscalar control structure is also compared with the existing multiscalar control scheme. Finally, the simulation and experimental results are demonstrated to validate the performance of the proposed control solution for a sensorless five-phase interior permanent magnet synchronous motor test setup.