To improve the control performance of the permanent magnet synchronous motor speed control system, the fractional order calculus theory is combined with the sliding mode control to design the fractional order integral sliding mode sliding mode surface (FOISM) to improve the robustness of the system. Secondly, considering the existence of chattering phenomenon in sliding mode control, a new second-order sliding mode reaching law (NSOSMRL) is designed to improve the control accuracy of the system. Finally, the effectiveness of the proposed strategy is demonstrated by simulation.

To reduce the common mode voltage (CMV), suppress the CMV spikes, and improve the steady-state performance, a simplified reactive torque model predictive control (RT-MPC) for induction motors (IMs) is proposed. The proposed prediction model can effectively reduce the complexity of the control algorithm with the direct torque control (DTC) based voltage vector (VV) preselection approach. In addition, the proposed CMV suppression strategy can restrict the CMV within ±Vdc/6, and does not require the exclusion of non-adjacent non-opposite VVs, thus resulting in the system showing good steady-state performance. The effectiveness of the proposed design has been tested and verified by the practical experiment. The proposed algorithm can reduce the execution time by an average of 26.33% compared to the major competitors.

This paper proposes the new series highly integrated intelligent power module (IPM), which is developed to provide a ultra-compact, high performance and reliable motor drive system. Details of the key design technologies of the IPM is given and practical application issues such as electrical characteristics, system operation performance and power dissipation are discussed. Layout placement and routing have been optimized in order to reduce and balance the parasitic impedances. By implementing an innovative direct bonding copper (DBC) ceramic substrate, which can effectively dissipate heat, the IPM delivers a fully integrated power stages including two three-phase inverters, power factor correction (PFC) and rectifier in an ultra-compact 75.5mm × 30mm package, offering up to a 17.3 percent smaller space than traditional motor drive scheme.

Deep Neural Network (DNN) is widely used for computer vision tasks, such as image classification, object detection, and segmentation. DNN accelerator on FPGA and especially Convolutional Neural Network (CNN) is a hot topic. More research and education should be conducted to boost this field. A starting point is required to make it easy for new entrants to join this field. We believe that FPGA-based Autonomous Driving (AD) motor cars are suitable for this because DNN accelerators can be used for image processing with low latency. In this paper, we propose an FPGA-based simple and open-source mini motor car system named RVCar with a RISC-V soft processor and a CNN accelerator. RVCar is suitable for the new entrants who want to learn the implementation of a CNN accelerator and the surrounding system. The motor car consists of Xilinx Nexys A7 board and simple parts. All modules except the CNN accelerator are implemented in Verilog HDL and SystemVerilog. The CNN accelerator is converted from a PyTorch model by our tool. The accelerator is written in C++, synthesizable by Vitis HLS, and an easy-to-customize baseline for the new entrants. FreeRTOS is used to implement AD algorithms and executed on the RISC-V soft processor. It helps the users to develop the AD algorithms efficiently. We conduct a case study of the simple AD task we define. Although the task is simple, it is difficult to achieve without image recognition. We confirm that RVCar can recognize objects and make correct decisions based on the results.

Observed results of arc discharges generated between the brush and commutator are reported. The motion of the arc discharges was observed by a high-speed camera. The brush and commutator were installed to an experimental device that simulated the rotational motion of a real DC motor. The aim of this paper is to investigate the occurring position, dimensions, and moving characteristics of the arc discharges by means of high-speed imaging. Time evolutions of the arc voltage and current were measured, simultaneously. The arc discharges were generated when an inductive circuit was interrupted. Circuit current before interruption was 4A. The metal graphite or graphite brush and a copper commutator were used. Following results were obtained. The arc discharge was dragged on the brush surface and the arc discharge was sticking to the side surface of the commutator. The positions of the arc spots were on the end of the commutator and the center of the brush in rotational direction. The dimensions of the arc discharge were about 0.2 mm in length and about 0.3 mm in width. The averaged arc voltage during arc duration became higher and the light emission from the arc discharge became brighter, as the copper content of the cathode decreased.

A laser imaging detection and ranging (LIDAR) is one of the key sensors for autonomous driving. In order to improve its performance of the measurable distance, especially toward the front-side direction of the vehicle, this paper presents rapid revolution speed control of a brushless DC (BLDC) motor with a cyclostationary command signal. This enables the increase of the signal integration time for the designated direction, and thus improves the signal-to-noise ratio (SNR), while maintaining the averaged revolution speed. We propose the use of pre-emphasis circuits to accelerate and decelerate the revolution speed of the motor rapidly, by modifying the command signal so as to enhance the transition of the speed. The adaptive signal processing can adjust coefficients of the pre-emphasis filter automatically, so that it can compensate for the decayed response of the motor and its controller. Experiments with a 20-W BLDC motor prove that the proposed technique can achieve the actual revolution speed output to track the designated speed profile ranging from 600 to 1400 revolutions per minute (rpm) during one turn.

To improve the cutting skills of learners, we developed a method for improving the skill involved in creating paper cuttings based on a steering task in the field of human-computer interaction. TaWe made patterns using the white and black boundaries that make up a picture. The index of difficulty (ID) is a numerical value based on the width and distance of the steering law. First, we evaluated novice and expert pattern-cutters, and measured their moving time (MT), error rate, and compliance with the steering law, confirming that the MT and error rate are affected by pattern width and distance. Moreover, we quantified the skills of novices and experts using ID and MT based models. We then observed changes in the cutting skills of novices who practiced with various widths and evaluated the impact of the difficulty level on skill improvement. Patterns considered to be moderately difficult for novices led to a significant improvement in skills.

Dual-motor driving servo systems are widely used in many military and civil fields. Since backlash nonlinearity affects the dynamic performance and steady-state tracking accuracy of these systems, it is necessary to study a control strategy to reduce its adverse effects. We first establish the state-space model of a system. To facilitate the design of the controller, we simplify the model based on the state-space model. Then, we design an adaptive controller combining a projection algorithm with dynamic surface control applied to a dual-motor driving servo system, which we believe to be the first, and analyze its stability. Simulation results show that projection algorithm-based dynamic surface control has smaller tracking error, faster tracking speed, and better robustness and stability than mere dynamic surface control. Finally, the experimental analysis validates the effectiveness of the proposed control algorithm.

Motorcycles are driven in a road widely but must be driven carefully because they are easily damaged by obstacles, bumps or potholes in the road. Thus, motorcycle trajectories are valuable for detecting road abnormalities. The trajectories are usually obtained from GPS (Global Positioning System). However, errors often occur in GPS positioning. In this research, we will present a detection idea of the GPS error based on behavior estimation of riders. Moreover, we will propose a novel behavior estimation method.

Many DC commutator motors are widely used in automobiles. In recent years, as compact and high output DC motors have been developed due to advanced technology, the faster the rotational speed is required and the commutation arc causes a high rate of wear/erosion of brush and commutator. Therefore, it is important how the motor speed influences commutation phenomena such as arc duration, residual current and erosion and wear of commutator and brush in order to achieve high reliability and extensive lifespan. In this paper waveforms of commutation voltage and current are measured at the rotation speed of 1000 to 5000min-1and the relation between rotation speed and arc duration / residual current is obtained. In addition long term tests are carried out at the rotation speed of 1000 to 5000min-1 the change of arc duration and effective commutation period is examined during the test of 20hours. Further, brush wear is evaluated by the difference of brush length between before and after test. Consequently, it can be made clear that as the speed increases, the effective commutation period decreases and the arc duration is almost same at the speed up to 3000min-1 and is around 42µsec.

The power packet dispatching system, in which electric power is transferred in a pulse-shaped form with information, is expected to realize dynamical management of multiple power sources in independent systems such as robots. In this letter, close-loop control of a stepper motor by power packets is discussed. The precise angle control is achieved by the combined transfer of power and control information in experiments.

Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique, suitable for measurement during motor learning. However, effects of contamination by systemic artifacts derived from the scalp layer on learning-related fNIRS signals remain unclear. Here we used fNIRS to measure activity of sensorimotor regions while participants performed a visuomotor task. The comparison of results using a general linear model with and without systemic artifact removal shows that systemic artifact removal can improve detection of learning-related activity in sensorimotor regions, suggesting the importance of removal of systemic artifacts on learning-related cerebral activity.

An extended harmonic disturbance observer is designed for speed (or position) sensorless current control of DC motor subject to a biased sinusoidal disturbance and parameter uncertainties. The proposed method does not require the information on the mechanical part of the motor equation. Theoretical analysis via the singular perturbation theory is performed to verify that the feedforward compensation using the estimation can improve the robust transient performance of the closed-loop system. A stability condition is derived against parameter uncertainties. Comparative experimental results validate the robustness of the proposed method against the uncertainties.

In order to efficiently drive a low-power DC motor using microwave power transfer (MPT), a compact power-receiving device is developed, which consists of a rectenna array and an improved DC-DC converter with constant input resistance characteristics. Since the conversion efficiency of the rectenna is strongly affected by the output load, it is difficult to efficiently drive a dynamic load resistance device such as DC motor. Using both continuous-wave (CW) and pulsed-wave MPT, experiments are carried out on driving the DC motor whose load resistance is varying from 36 to 140 Ω. In the CW case, the measured overall efficiency of the power-receiving device is constant over 50% for the power density of 0.25 to 2.08 mW/cm2. In particular, the overall efficiency is 62%, 70.8% for the power density of 0.25, 0.98 mW/cm2 where the received power of the single antenna is 13, 50 mW, respectively. In the pulsed-wave case, the measured overall efficiency is over 44% for a duty ratio of 0.2 to 1 for the power density of 0.98 mW/cm2.

This paper proposes an updating state dependent disturbance observer (USDDOB) to reject position dependent disturbances when parameters vary slowly, and input and output are time-delayed. To reject the effects of resultant slowly-varying position dependent disturbances, the USDDOB uses the control method of the state dependent disturbance observer (SDDOB) and time-invariance approximation. The USDDOB and a main proportional integral (PI) controller constitute a robust controller. Simulations and experiments using a 1-degree-of-freedom (1-DOF) tilted planar robot show the effectiveness of the proposed method.

Since the conventional cascade controller for electric motor drives requires accurate information about the system parameters and load conditions to achieve a desired performance, this paper presents a new practical control structure to improve the robust performance against parameter uncertainties. Two first-order disturbance observers (DOB) are incorporated with the cascade structure, to preserve the nominal performance. The analysis of the robust performance of the DOB is presented by using the singular perturbation theory. Simulation results suggest that the proposed controller can be used effectively as an additional compensator to the conventional cascade scheme.

We propose a motor speed ripple elimination method using a state dependent disturbance observer (SDDOB). The SDDOB eliminates the state dependent disturbance in the system regardless of the operation frequency, input time delay and output time delay. The SDDOB and a main proportional integral (PI) controller constitute a robust motor speed controller. Experimental results show the effectiveness of the proposed method.

The brain-machine interface (BMI) is a new method for man-machine interface, which enables us to control machines and to communicate with others, without input devices but directly using brain signals. Previously, we successfully developed a real time control system for operating a robot arm using brain-machine interfaces based on the brain surface electrodes, with the purpose of restoring motor and communication functions in severely disabled people such as amyotrophic lateral sclerosis patients. A fully-implantable wireless system is indispensable for the clinical application of invasive BMI in order to reduce the risk of infection. This system includes many new technologies such as two 64-channel integrated analog amplifier chips, a Bluetooth wireless data transfer circuit, a wirelessly rechargeable battery, 3 dimensional tissue-fitting high density electrodes, a titanium head casing, and a fluorine polymer body casing. This paper describes key features of the first prototype of the BMI system for clinical application.

In an automotive fuel pump system, a small DC motor is widely used to drive the pump and driven by a automotive battery. Recently a bio-fuel, usually a mixture of gasoline and ethanol has been used due to shortage of gasoline and environmental aspect. It affects strongly the performances of a DC motor, especially commutation phenomena, what kind of fuel is used. Therefore the authors have started to investigate the influence of ethanol on the commutation phenomena. They have been reporting the wear of brush and carbon flat commutator in gasoline and ethanol so far. In this paper commutation period, arc duration, brush and commutator wear are examined in ethanol 50-gasoline 50%. Brush wears are very small compared with the previous results. Namely in the present test a mechanical sliding wear is predominant rather than erosion by arc due to short arc duration. Further, an area eroded by arc is observed to re-appear as a sliding surface. From these results a threshold arc energy between arc erosion and mechanical sliding wear is obtained, and a wear model is proposed to explain the above wear pattern on the sliding surface.

Unpredicted contactor failure can interrupt production and affect the uptime and throughput of manufacturing. Usually the life of a contactor is based on the manufacturers' life test data. However, due to the way of how the contactor is operated and the environment it is operated in, the working life of a contactor can vary significantly. In this paper, a novel technology has been investigated to predict potential failures of DC actuated contactors by monitoring their DC coil current and contactor currents. Three parameters are derived from this set of data to monitor the health of contactors: contact over-travel, armature pull-in time and coil current differential. Contact over-travel provides information on the remaining life of contacts and coil current differential provides indication of contact weld and carrier jam due to debris. The armature pull-in time provides information on contactor closing speed. Prototype contactors have been built and AC4 tests have been carried out for evaluation. Test results show that the contact over-travel parameter agrees well with contact mass loss data taken after contactors failed. The derived armature pull-in time agrees well with that measured by a laser displacement sensor. The defined parameters provide effective monitoring and prediction of potential contactor failures.