Motor Control Strategies Research Articles

Opponent Hitting Behavior Prediction and Ball Location Control for a Table Tennis Robot.

The hitting position and velocity control for table tennis robots have been investigated widely in the literature. However, most of the studies conducted do not consider the opponent's hitting behaviors, which may reduce hitting accuracy. This paper proposes a new table tennis robot framework that returns the ball based on the opponent's hitting behaviors. Specifically, we classify the opponent's hitting behaviors into four categories: forehand attacking, forehand rubbing, backhand attacking, and backhand rubbing. A tailor-made mechanical structure that consists of a robot arm and a two-dimensional slide rail is developed such that the robot can reach large workspaces. Additionally, a visual module is incorporated to enable the robot to capture opponent motion sequences. Based on the opponent's hitting behaviors and the predicted ball trajectory, smooth and stable control of the robot's hitting motion can be obtained by applying quintic polynomial trajectory planning. Moreover, a motion control strategy is devised for the robot to return the ball to the desired location. Extensive experimental results are presented to demonstrate the effectiveness of the proposed strategy.

Sex-related differences in motor control strategies during isometric contractions in the Sorensen test posture with different external loading.

There is a sex-related difference in strength and endurance in trunk muscles: males have more strength while females have more endurance. Investigate sex-related differences in motor control strategies in back muscles during isometric contractions (IC) in the Sorensen test posture. Thirty-six healthy and young volunteers performed different tasks: three maximal voluntary contraction (MVC) tests recorded with a bio-feedback force sensor, and followed by five loaded IC (LIC) tests using bodyweight and loads of 0 to 8 kg with a 2-kg step. Surface electromyography was used to measure the activity of bilateral lumbar paravertebral (LP) and quadratus lumborum (QL) muscles. Sex-related and load-related differences in EMG amplitudes were highlighted during LIC tests. Females showed significantly (p-value = 0.02) greater EMG amplitude for the highest load (8 kg) than males. Besides, significant differences between low (2 and 4 kg) and high (6 and 8 kg) loads for both LP and QL muscles and for both sexes were observed. Finally, for MVC tests, males produced significantly (p-value = 1.02e-4) greater strength during MVC tests (4.25 ± 1.37 N/kg vs 2.60 ± 0.78N/kg). The results were clinically relevant to bring attention to load strategy during rehabilitation, particularly in females who seemed to recruit the QL muscle more.

A Serious Game for the Assessment of Visuomotor Adaptation Capabilities during Locomotion Tasks Employing an Embodied Avatar in Virtual Reality.

The study of visuomotor adaptation (VMA) capabilities has been encompassed in various experimental protocols aimed at investigating human motor control strategies and/or cognitive functions. VMA-oriented frameworks can have clinical applications, primarily in the investigation and assessment of neuromotor impairments caused by conditions such as Parkinson's disease or post-stroke, which affect the lives of tens of thousands of people worldwide. Therefore, they can enhance the understanding of the specific mechanisms of such neuromotor disorders, thus being a potential biomarker for recovery, with the aim of being integrated with conventional rehabilitative programs. Virtual Reality (VR) can be entailed in a framework targeting VMA since it allows the development of visual perturbations in a more customizable and realistic way. Moreover, as has been demonstrated in previous works, a serious game (SG) can further increase engagement thanks to the use of full-body embodied avatars. Most studies implementing VMA frameworks have focused on upper limb tasks and have utilized a cursor as visual feedback for the user. Hence, there is a paucity in the literature about VMA-oriented frameworks targeting locomotion tasks. In this article, the authors present the design, development, and testing of an SG-based framework that addresses VMA in a locomotion activity by controlling a full-body moving avatar in a custom VR environment. This workflow includes a set of metrics to quantitatively assess the participants' performance. Thirteen healthy children were recruited to evaluate the framework. Several quantitative comparisons and analyses were run to validate the different types of introduced visuomotor perturbations and to evaluate the ability of the proposed metrics to describe the difficulty caused by such perturbations. During the experimental sessions, it emerged that the system is safe, easy to use, and practical in a clinical setting. Despite the limited sample size, which represents the main limitation of the study and can be compensated for with future recruitment, the authors claim the potential of this framework as a useful instrument for quantitatively assessing either motor or cognitive impairments. The proposed feature-based approach gives several objective parameters as additional biomarkers that can integrate the conventional clinical scores. Future studies might investigate the relation between the proposed biomarkers and the clinical scores for specific disorders such as Parkinson's disease and cerebral palsy.

Characteristics of uneven surface walking in stroke patients: Modification in biomechanical parameters and muscle activity

BackgroundStroke patients have difficulty walking in outdoor environments, including uneven surfaces, reducing their opportunities for social participation. Changes in stroke patients’ gait while walking on even surfaces have been reported; however, gait alterations on uneven surfaces remain unclear. Research questionTo what extent do biomechanical parameters and muscle activity during even and uneven surface walking differ between stroke patients and healthy people? MethodsTwenty stroke patients and 20 age-matched healthy people walked on a 6 m even and uneven surfaces. Data on gait speed, root mean square (RMS) of trunk acceleration as a measure of gait stability, maximum joint angle, average muscle activity, and muscle activity time were quantified using accelerometers attached to the trunk, video camera images, and electromyography of lower extremities. A two-factor mixed-model analysis of variance was used to test the effects of group, surface, and group × surface interactions. ResultsGait speed decreased (p < 0.001) on the uneven surface in stroke patients and healthy people. RMS showed an interaction (p < 0.001), and the post-hoc test revealed an increase in stroke patients in the mediolateral direction during the swing phase on the uneven surface. The hip extension angle during the stance phase showed an interaction (p = 0.023), and the post-hoc test revealed a decrease in stroke patients on the uneven surface. The soleus muscle activity time showed an interaction during the swing phase (p = 0.041), and the post-hoc test revealed an increase in stroke patients compared to healthy people only on the uneven surface. SignificanceWhile walking on an uneven surface, stroke patients showed decreased gait stability, decreased hip extension angle during stance phase, and increased ankle plantar flexor activity time during swing phase. These changes may result from impaired motor control and compensatory strategies used by stroke patients on uneven surfaces.

Torque Ripple Minimization Technique of Position Sensorless BLDC Motor for Variable Speed Drives

Brushless Direct Current (BLDC) motors are advantageous because of their higher efficiency, higher speed operations and higher power density. Industrial applications demand BLDC motors free from torque ripple. The torque ripple is due to the unequal commutation period between the energised phase and unenergized phase current. It is a perilous problem in sensorless BLDC drive as it leads to speed oscillations, acoustic noise, serious faults, and vibration in machines. The torque ripple can be reduced either by improving motor design parameter or by improving the motor control strategy. This paper proposes a Proportional Integral (PI) controller-based control scheme for a cuk converter driven sensorless BLDC motor to reduce the torque ripple. The proposed scheme invokes Zero Crossing Point (ZCP) detection with back emf sensing approach. The presence of inductor reduces the ripple in the input and output currents. The performance of the strategy is verified using MATLAB R2018a Simulink for different operating conditions of a BLDC drive and the results prove that the recommended scheme decreases the torque ripple compared to the conventional scheme.

Modeling and performance analysis of underwater gliders based on the virtual prototype technology

An underwater glider often comprises a buoyancy propulsion system, attitude regulation system, control system, hydrodynamic shape, etc. Numerous dynamic models based on rigid body dynamics have been established to study the motion performance of underwater gliders, which often neglect their attitude regulation and control systems. In this paper, a virtual prototype model of underwater gliders is developed based on the virtual prototype technology and verified by sea trials. Using the virtual model developed, the effects of three different control strategies and passive accumulators on underwater glider motion performance are discussed. The results demonstrate the feasibility of the virtual prototype model for motion performance analysis and control strategy optimization instead of the physical prototype. Thus, the proposed model could cut down the research and design time and reduce design costs.

Robot Navigation in Complex Workspaces Employing Harmonic Maps and Adaptive Artificial Potential Fields.

In this work, we address the single robot navigation problem within a planar and arbitrarily connected workspace. In particular, we present an algorithm that transforms any static, compact, planar workspace of arbitrary connectedness and shape to a disk, where the navigation problem can be easily solved. Our solution benefits from the fact that it only requires a fine representation of the workspace boundary (i.e., a set of points), which is easily obtained in practice via SLAM. The proposed transformation, combined with a workspace decomposition strategy that reduces the computational complexity, has been exhaustively tested and has shown excellent performance in complex workspaces. A motion control scheme is also provided for the class of non-holonomic robots with unicycle kinematics, which are commonly used in most industrial applications. Moreover, the tuning of the underlying control parameters is rather straightforward as it affects only the shape of the resulted trajectories and not the critical specifications of collision avoidance and convergence to the goal position. Finally, we validate the efficacy of the proposed navigation strategy via extensive simulations and experimental studies.

Motor Synergies in the Control System of Complex Sports Balance Postures

The paper presents the motor synergies’ structure at the muscular and kinematic levels of their study during performing various sports balance postures. The features of the formation and modification of synergetic modules depending on the motor task are considered. An attempt is made to combine some computational aspects applied according of various concepts of synergetic control. The tools for identifying and analyzing synergy parameters included regression analysis and data factorization methods. It has been established that differing in biomechanical structure balance exercises can be realized using common patterns of kinematic synergies spatial organization, however, at the muscular level, the existence of common spatial activation profiles is not obvious. The presence of several different time patterns of synergy activation in the structure of each balance exercise has been revealed, which may be associated with various motor control strategies in the central nervous system. The structure of kinematic synergies may be influenced by artificial degrees of freedom restriction, unrelated to central control processes. It is shown that the inclusion of the concept of “variables’ area” in the motor control modular organization theory contributes to the convergence of existing motor control concepts based on synergy principle.

Back EMF-Based Dynamic Position Estimation in the Whole Speed Range for Precision Sensorless Control of PMLSM

Back EMF-based sensorless control strategies for permanent magnet linear synchronous motor (PMLSM) have the potential to simplify the mechatronic system, reduce the cost and prolong the service life. However, the poor performance in the low-to-zero speed region limits their application range. In this paper, a novel back electromotive force (EMF)-based mover position estimator is proposed to achieve consistent good accuracy in the whole speed range including high speed, medium speed, low speeds, temporary standstill, and speed reversals. The three-phase flux linkages are obtained by directly calculating the integration of back EMF. To overcome the curve drift caused by the integrator, this paper proposes a jumping correction algorithm (JCA) and a uniform correction algorithm (UCA). The mover position is calculated from the corrected flux linkages. This paper also realizes a closed-loop sensorless trajectory tracking control system using the proposed position estimator. Experimental results on a PMLSM demonstrate that the proposed position estimator can guarantee stability and accuracy in the whole speed range. Compared with existing back EMF-based methods working only well in high-speed region and usually with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$mm$</tex-math></inline-formula> -level accuracy, the proposed method achieves an accuracy of sub-200 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu m$</tex-math></inline-formula> regardless of the reference trajectory, and has exciting prospect in industrial applications.

Evidence of a Double Pulse Muscle Activation Strategy in Drummers’ Trunk and Upper Limb Muscles During High-velocity Cymbal Crashes

Background: Playing the drum kit is a physically and cognitively demanding task, and skilled drummers share many such attributes with elite athletes. The ‘double pulse’ muscle activation (DPMA) pattern is a motor control strategy that has been observed in athletes of sports involving ballistic movements (e.g., baseball, golf, Mixed Martial Arts), and is believed to function to increase force transfer to the target. Objective: This study examined the muscle activation patterns of highly skilled drummers for evidence of a DPMA during high-velocity cymbal crashes. Methods: Five drummers were instrumented with electromyography electrodes on the right latissimus dorsi, triceps brachii, erector spinae, rectus abdominis, deltoideus posterior (DP), teres major, extensor carpi radialis, and flexor carpi ulnaris muscles. Six trials of data were collected, including a resting baseline, three maximum voluntary exertions (MVE) consisting of maximal effort cymbal crashes, a drumming pattern that included multiple crashes, and a ‘free-play’ trial. Results: The DPMA waveform was observed in all trials, but only those observed during the MVE trials were confirmed to coincide with the crashing movement via video analysis. The DP muscle – which functions to extend the shoulder joint to crash the stick on to the cymbal – exhibited confirmed DPMAs the most frequently. Conclusion: The extent to which drummers use the DPMA to produce high-velocity cymbal crashes within authentic playing conditions is inconclusive and needs further examination. Future study of the DPMA phenomenon in drummers would benefit from the addition of 3-dimensional motion capture to further understand the purpose of the muscle contractions of the DPMA.

Alternative muscle synergy patterns of upper limb amputees.

Myoelectric hand prostheses are effective tools for upper limb amputees to regain hand functions. Much progress has been made with pattern recognition algorithms to recognize surface electromyography (sEMG) patterns, but few attentions was placed on the amputees' motor learning process. Many potential myoelectric prostheses users could not fully master the control or had declined performance over time. It is possible that learning to produce distinct and consistent muscle activation patterns with the residual limb could help amputees better control the myoelectric prosthesis. In this study, we observed longitudinal effect of motor skill learning with 2 amputees who have developed alternative muscle activation patterns in response to the same set of target prosthetic actions. During a 10-week program, amputee participants were trained to produce distinct and constant muscle activations with visual feedback of live sEMG and without interaction with prosthesis. At the end, their sEMG patterns were different from each other and from non-amputee control groups. For certain intended hand motion, gradually reducing root mean square (RMS) variance was observed. The learning effect was also assessed with a CNN-LSTM mixture classifier designed for mobile sEMG pattern recognition. The classification accuracy had a rising trend over time, implicating potential performance improvement of myoelectric prosthesis control. A follow-up session took place 6 months after the program and showed lasting effect of the motor skill learning in terms of sEMG pattern classification accuracy. The results indicated that with proper feedback training, amputees could learn unique muscle activation patterns that allow them to trigger intended prosthesis functions, and the original motor control scheme is updated. The effect of such motor skill learning could help to improve myoelectric prosthetic control performance.

Control Strategy of Synchronous Reluctance Motor Using Empirical Information Brain Emotional Learning Based Intelligent Controller Considering Magnetic Saturation

The synchronous reluctance motor (SynRM) has significant nonlinear characteristics due to the problems of magnetic saturation and cross-coupling and the poor adaptability of the general controller to parameter changes seriously affects the control performance of the motor. In order to solve the above problems, this paper proposed a control system for the SynRM with a brain emotion controller based on empirical information to solve the motor control problem of magnetic saturation. Firstly, the nonlinear mathematical model of the SynRM considering magnetic saturation is established by introducing the magnetic saturation parameter. Secondly, the sensory input function and emotional cue function based on systematic error are given and the vector control system of the SynRM considering magnetic saturation is designed. The influence of the parameters and the learning rate of the brain emotional learning based intelligent controller (EI-BELBIC) on the adjustment range of the controller parameters is studied. Then the SynRM is controlled under different working conditions and the control effect is observed. The results show that the designed vector control system of the SynRM based on EI-BELBIC has strong reliability, accurate control, rapid response, and strong anti-interference ability under magnetic saturation.

Hierarchical motion control strategies for handling interactions of automated vehicles

The paper proposes motion control strategies for automated road vehicles to handle interactions among vehicles. The control strategies are built in a hierarchical structure, which contains a high-level learning-based control, a low-level robust control and an optimization-based supervisor. The purposes of the control strategies are to guarantee collision-free motion of the vehicles, and moreover to improve economy and traveling time performance levels. In the paper two configurations of the hierarchical structure are proposed, which are based on the centralized and independent design for automated vehicle motion. The paper presents the comparison of the two configurations through simulation examples. Moreover, the operation of the independent configuration on a small-scaled indoor test vehicle platform is also presented.

Capacitance balance control strategy for three-phase four-switch open-winding permanent magnet synchronous motor

The open-winding permanent magnet synchronous motor (OWPMSM) system is a novel high-cost system that uses dual-inverters to control both sides of the stator to improve the performance of the motor. To save the system cost, the three-phase four-switch open-winding permanent magnet synchronous motor (TPFS-OWPMSM) system is proposed to replace the general three-phase six-switch OWPMSM system, but it brings the problem of bridge arm capacitor voltage offset. To solve the instability problem caused by capacitance, this paper carries out the state analysis and offset source analysis of the inverter capacitor voltage, and proposes a dual-inverters capacitor voltage balance control strategy. The capacitor balance control converts unstable capacitor offset voltage into current and injects the currents into the stator current loop through closed-loop correction. And this strategy includes the optimal control of different capacitors under dual-inverters with the goal of system stability. The effectiveness of the balancing strategy is verified by the simulation of various motor working conditions.

Predictive Control of a Heaving Compensation System Based on Machine Learning Prediction Algorithm

Floating structures have become a major part of offshore structure communities as offshore engineering moves from shallow waters to deeper ones. Floating installation ships or platforms are widely used in these engineering operations. Unexpected wave-induced motions affect floating structures, especially in harsh sea conditions. Horizontal motions on the sea surface can be offset by a dynamic positioning system, and heave motions can be controlled by a heave compensation system. Active heave compensation (AHC) systems are applied to control vertical heave motions and improve safety and efficiency. Predictive control based on machine learning prediction algorithms further improves the performance of active heave compensation control systems. This study proposes a predictive control strategy for an active heave compensation system with a machine learning prediction algorithm to minimise the heave motion of crane payload. A predictive active compensation model is presented to verify the proposed predictive control strategy, and proportion–integration–differentiation control with predictive control is adopted. The reliability of back propagation neural network (BPNN) and long short-term memory recurrent neural network (LSTM RNN) prediction algorithms is proven. The influence of the predictive error on compensation performance is analysed by comparing predictive feedforward cases with actual-data feedforward cases. Predictive feedforward control with regular and irregular wave conditions is discussed, and the possible strategies are examined. After implementing the proposed predictive control strategy based on a machine learning algorithm in an active heave compensation system, the heave motion of the payload is reduced considerably. This investigation is expected to contribute to the motion control strategy of floating structures.

Optimal Control Strategy of Construction Machinery Motor Considering Genetic Algorithm

Optimal Control Strategy of Construction Machinery Motor Considering Genetic Algorithm

Dynamic Modeling and Attitude–Vibration Cooperative Control for a Large-Scale Flexible Spacecraft

Modern spacecraft usually have larger and more flexible appendages whose vibration becomes more and more prominent, and it has a great influence on the precision of spacecraft attitude. Therefore, the cooperative control of attitude maneuvering and structural vibration of the system has become a significant issue in the spacecraft design process. We developed a low-dimensional and high-precision mathematical model for a large-scale flexible spacecraft (LSFS) equipped with a pair of hinged solar arrays in this paper. The analytic global modes are used to obtain the rigid–flexible coupling discrete dynamic model, and the governing equations with multiple DOFs for the system are derived by using the Hamiltonian principle. The rigid–flexible coupled oscillating responses of LSFS under the three-axis attitude-driving torque pulse during the in-orbit attitude maneuvering process are investigated. A study on the flexibility of the hinge was also conducted. Based on the simplified and accurate dynamic model of the system, we can obtain a state-space model for LSFS conveniently, and the cooperative control schemes for rigid motion and flexible oscillation control are designed by using the LQR, PD, and PD + IS algorithms. The simulation results show that three cooperative controllers can realize spacecraft attitude adjustment and synchronously eliminate flexible oscillation successfully. By comparison, the PD + IS controller is simpler so that it is suitable for the real-time attitude–vibration cooperative control of spacecraft.

Enhancing Microcomputer Edge Computing for Autonomous IoT Motion Control

Devices microprocessors, microcontrollers, and Field Programmable Gate Arrays (FPGA) play the core rule at the IoT edge level and it should be right provisioned. For proper controller performance, control algorithms should be implemented near the actuator eliminating the delay effects. In the IoT domain, this means to implement the mentioned algorithm at the edge level and prior data transmitting. The efficient IoT-enabled motion control can be obtained by considering two main factors; the first factor is from the actuator design point of view and the second factor is from the controller performance point of view. Therefore, in this article, the two mentioned factors are treated concerning the microprocessor rule and importance as a core for proper prototype design and as the main platform to implement the control algorithms. A comparison of controller performance indices for both prototypes is done using previously distributed motion control schemes and newly developed schemes after tuning the respective schemes gains in an optimal manner. The scheme with better behavior of both prototypes are selected for the IoT integration process, this scheme ensures optimal edge computing for the distributed motion control, making the implementation of all control computation take place at the IoT-edge level. As a result, the dynamic pipeline stages (DPS) based prototype gives better controller performance indices for most strategies, less power consumption, and optimally utilized resources encouraging the use of the microprocessors with reconfigurable components at the IoT-edge level.

Optimization Reduces Knee-Joint Forces During Walking and Squatting: Validating the Inverse Dynamics Approach for Full Body Movements on Instrumented Knee Prostheses.

Because of the redundancy of our motor system, movements can be performed in many ways. While multiple motor control strategies can all lead to the desired behavior, they result in different joint and muscle forces. This creates opportunities to explore this redundancy, for example, for pain avoidance or reducing the risk of further injury. To assess the effect of different motor control optimization strategies, a direct measurement of muscle and joint forces is desirable, but problematic for medical and ethical reasons. Computational modeling might provide a solution by calculating approximations of these forces. In this study, we used a full-body computational musculoskeletal model to (a)predict forces measured in knee prostheses during walking and squatting and (b)study the effect of different motor control strategies (i.e.,minimizing joint force vs. muscle activation) on the joint load and prediction error. We found that musculoskeletal models can accurately predict knee joint forces with a root mean squared error of <0.5 body weight (BW) in the superior direction and about 0.1BW in the medial and anterior directions. Generally, minimization of joint forces produced the best predictions. Furthermore, minimizing muscle activation resulted in maximum knee forces of about 4BW for walking and 2.5BW for squatting. Minimizing joint forces resulted in maximum knee forces of 2.25BW and 2.12BW, that is, a reduction of 44% and 15%, respectively. Thus, changing the muscular coordination strategy can strongly affect knee joint forces. Patients with a knee prosthesis may adapt their neuromuscular activation to reduce joint forces during locomotion.

Permanent Magnet Synchronous Motor Control Based on Phase Current Reconstruction

The traditional single current sensor control strategy of a permanent magnet synchronous motor (PMSM) often adopts the DC bus method, which makes it difficult to eliminate the blind area of current reconstruction. Therefore, a current reconstruction method based on a sliding mode observer is proposed. Based on the current equation of the motor, the method takes the α-axis and β-axis currents as the observation objects and shares the same synovial surface, so that the α-axis current observation value and the β-axis current observation value converge to the actual current value at the same time and the unknown β-axis current information is obtained. The control system first tests the performance of the motor under different working conditions when the parameters are matched, and then tests the current reconstruction ability of the parameter mismatch. The results show that the current observer with a matched parameter can accurately and quickly reconstruct the β-axis current under various operating conditions, and the maximum current error does not exceed 4 mA. When the parameters are mismatched, high-performance control of the motor can still be achieved. The proposed method has excellent robustness.