Position Tracking Tasks Research Articles

Position measurement and output feedback tracking method of hydraulic servo system based on pressure sensor

Instead of using position sensor, a position measurement and output feedback tracking method is proposed based on pressure sensor for a class of hydraulic servo system. First, the desired accumulator pressure is calculated and obtained in an open-loop way according to the desired cylinder position. Then, the position tracking task can be indirectly realized by achieving the pressure control. As a result, only one pressure sensor is required instead of position sensor, which is different from almost all the traditional output feedback position controller of hydraulic system. For the closed-loop pressure tracking, a desired compensation method and an auxiliary variable-based state observer are integrated to form the nonlinear controller. The theoretical and numerical simulation results demonstrate that the presented controller can attain satisfactory pressure tracking performance.

Effect of Dyadic Haptic Collaboration on Ankle Motor Learning and Task Performance.

Optimizing skill acquisition during novel motor tasks and regaining lost motor functions have been the interest of many researchers over the past few decades. One approach shown to accelerate motor learning involves haptically coupling two individuals through robotic interfaces. Studies have shown that an individual's solo performance during upper-limb tracking tasks may improve after haptically-coupled training with a partner. In this study, our goal was to investigate whether these findings can be translated to lower-limb motor tasks, more specifically, during an ankle position tracking task. Using one-degree-of-freedom ankle movements, pairs of participants (i.e., dyads) tracked target trajectories independently. Participants alternated between tracking trials with and without haptic coupling, achieved by rendering a virtual spring between two ankle rehabilitation robots. In our analysis, we compared changes in task performance across trials while training with and without haptic coupling. The tracking performance of both individuals (i.e., dyadic task performance) improved during haptic coupling, which was likely due to averaging of random errors of the dyadic pair during tracking. However, we found that dyadic haptic coupling did not lead to faster individual learning for the tracking task. These results suggest that haptic coupling between unimpaired individuals may not be an effective method of training ankle movements during a simple, one-degree-of-freedom task.

Regularized nonlinear regression for simultaneously selecting and estimating key model parameters: Application to head-neck position tracking

In system identification, estimating parameters of a biomechanical model using limited observations results in poor identifiability. To cope with this issue, we propose a new method to simultaneously select and estimate sensitive parameters as key model parameters while fixing the remaining parameters to a set of typical values. The problem is formulated as a nonlinear least-squares estimator with L1-regularization on the deviation of parameters from a set of typical values. In addition, a modified optimization approach is introduced to find the solution to the formulated problem. As a result, we provided consistency and oracle properties of the proposed estimator as a theoretical foundation. To show the effectiveness of the proposed method, we conducted simulation and experimental studies. In the simulation study, the proposed Lasso performed significantly better than the ordinary L1-regularization methods in terms of the bias and variance of the parameter estimates. The experimental study presented an application identifying a biomechanical parametric model of a head position tracking task for ten human subjects from limited data. Compared with the variance of the parameter estimates from nonlinear ordinary least-squares regression, that of parameter estimates from the proposed Lasso decreased by 96% using the simulated data. Using the real-world data, the variance of estimated parameters decreased by 71%. In addition, the proposed method kept variance accounted for (VAF) at 83% and was 54 times faster than the ordinary Lasso using a standard simplex-based optimization algorithm.

AUV position tracking and trajectory control based on fast-deployed deep reinforcement learning method

Aiming at the difficult problem of motion control of under-actuated and X-rudder autonomous underwater vehicle (AUV), the present work adopts deep reinforcement learning (DRL) method for its posture control. First, an AUV agent is trained with deep deterministic policy gradient (DDPG) algorithm in a simulation environment, and three-degree-of-freedom posture control of the AUV at a constant speed, fixed roll, variable pitch, and variable yaw, is successfully achieved. Subsequently, the AUV's yaw angle range is extended, and the control failure problem when AUV's yaw angle approaches a critical value is solved, realizing the rapid deployment of the DRL algorithm for AUV control. On this basis, the position-tracking task of AUV for targets in different orientations in three-dimensional space is completed, achieving a six-degree-of-freedom control of AUV. Additionally, by decomposing the trajectory control task of AUV in three-dimensional space into multiple position-tracking missions, the trajectory control of AUV in the underwater horizontal plane and underwater three-dimensional space is realized, demonstrating the significant task generalization ability of the control methods proposed.

Adaptive Damping Variable Sliding Mode Control for an Electrohydrostatic Actuator

An electrohydrostatic actuator (EHA) is a basic mechanical/hydraulic system with deficiencies including significant nonlinearity and parametric uncertainties. In line with the challenges of designing a high-precision control strategy, an adaptive damping variable sliding mode controller is established, which extends our previous work on EHA control. The proposed controller integrates variable-damping sliding mode control, parametric adaptation, and an extended state observer. The parametric uncertainties are effectively captured and compensated by employing an adaptive control law, while system uncertainties are reduced, and disturbances are estimated and compensated with a fast and stable response. We evaluated the proposed control strategy on a variety of position tracking tasks. The experimental results demonstrate that our controller significantly outperforms the widely used methods in overshoot suppression, settling time, and tracking accuracy.

Analysis of Coupling Effect in Human-Commanded Stiffness During Bilateral Tele-Impedance

Tele-impedance augments classic teleoperation by enabling the human operator to actively command remote robot stiffness in real-time, which is an essential ability to successfully interact with the unstructured and unpredictable environment. However, the literature is missing a study on benefits and drawbacks of different types of stiffness command interfaces used in bilateral tele-impedance. In this article, we introduce a term called coupling effect, which pertains to the coupling between human-commanded stiffness going to the remote robot and force feedback coming from the remote robot. We hypothesize that, whenever the operator's commanded stiffness and force feedback are subject to coupling effect (e.g., muscle activity based stiffness command interfaces), force feedback can invoke involuntary changes in the commanded stiffness due to human reflexes. Although the coupling effect takes away some degree of the operator's control over the commanded stiffness, these involuntary changes can be either beneficial (e.g., during position tracking) or detrimental (e.g., during force tracking) to the task performance on the remote robot side. We examined the coupling effect in an experimental study with <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">16</b> participants, who performed position and force tracking tasks by using both coupled type (muscle activity based) and decoupled type (external device based) of interface. The results demonstrate a benefit of the coupling effect when the remote robot is operating in presence of unexpected force perturbations, where lower absolute error in position tracking task was observed. On the other hand, the decoupled type of interface is beneficial for force tracking tasks on the remote robot side, such as establishing or maintaining a stable contact with objects. However, the coupling effect negatively influences the commanding of reference stiffness to the remote robot in both position and force tracking tasks for the coupled type of interface, compared to the decoupled type of interface, which is not affected.

Self-Aligning Mechanism Improves Comfort and Performance With a Powered Knee Exoskeleton

Misalignments between powered exoskeleton joints and the user's anatomical joints are inevitable due to difficulty locating the anatomical joint axis, non-constant location of the anatomical joint axis, and soft-tissue deformations. Self-aligning mechanisms have been proposed to prevent spurious forces and torques on the user's limb due to misalignments. Several exoskeletons have been developed with self-aligning mechanisms based on theoretical models. However, there is no experimental evidence demonstrating the efficacy of self-aligning mechanisms in lower-limb exoskeletons. Here we show that a lightweight and compact self-aligning mechanism improves the user's comfort and performance while using a powered knee exoskeleton. Experiments were conducted with 14 able-bodied subjects with the self-aligning mechanism locked and unlocked. Our results demonstrate up to 15.3% increased comfort and 38% improved performance when the self-aligning mechanism was unlocked. Not surprisingly, the spurious forces and torques were reduced by up to 97% when the self-aligning mechanism was unlocked. This study demonstrates the efficacy of self-aligning mechanisms in improving comfort and performance for sit-to-stand and position tracking tasks with a powered knee exoskeleton.

GPIO‐based robust control of magnetic levitation system subject external disturbance

This article considers the robust control problem of non-linear magnetic levitation system with parameter uncertainties and external disturbance. To obtain a simple and practical controller that does not depend on the exact model information, a generalised-proportional-integral observer is constructed to estimate the velocity and disturbance. Then, based on mild assumptions and the estimated values, an output feedback tracking controller is proposed such that the reference position tracking task could be realised. The obtained control approach is confirmed strictly by theoretical analysis. Finally, simulation results are provided to show the effectiveness of the proposed control method.

Feasibility of Incorporating Test-Retest Reliability and Model Diversity in Identification of Key Neuromuscular Pathways During Head Position Tracking.

To study the complex neuromuscular control pathways in human movement, biomechanical parametric models and system identification methods are employed. Although test-retest reliability is widely used to validate the outcomes of motor control tasks, it was not incorporated in system identification methods. This study investigates the feasibility of incorporating test-retest reliability in our previously published method of selecting sensitive parameters. We consider the selected parameters via this novel approach to be the key neuromuscular parameters, because they meet three criteria: reduced variability, improved goodness of fit, and excellent reliability. These criteria ensure that the parameter variability is below a user-defined value, the number of these parameters is maximized to enhance goodness of fit, and their test-retest reliability is above a user-defined value. We measured variability, the goodness of fit, and reliability using Fisher information matrix, variance accounted for, and intraclass correlation, respectively. We also incorporated model diversity as a fourth optional criterion to narrow down the solution space of key parameters. We applied this approach to the head position tracking tasks in axial rotation and flexion/extension. A total of forty healthy subjects performed the tasks during two visits. With variability and reliability measures ≤0.35 and ≥0.75, respectively, we selected three key parameters out of twelve with the goodness of fit >69%. The key parameters were associated with at least two neuromuscular pathways out of four modeled pathways (visual, proprioceptive, vestibular, and intrinsic), which is a measure of model diversity. Therefore, it is feasible to incorporate reliability and diversity in system identification of key neuromuscular pathways in our application.

Evaluation of Control Interfaces for Active Trunk Support

A feasibility study was performed to evaluate the control interfaces for a novel trunk support assistive device (Trunk Drive), namely, joystick, force on sternum, force on feet, and electromyography (EMG) to be used by adult men with Duchene muscular dystrophy. The objective of this paper was to evaluate the performance of the different control interfaces during a discrete position tracking task. We built a one degree of freedom flexion-extension active trunk support device that was tested on 10 healthy men. An experiment, based on the Fitts law, was conducted, whereby subjects were asked to steer a cursor representing the angle of the Trunk Drive into a target that was shown on a graphical user interface, using the above-mentioned control interfaces. The users could operate the Trunk Drive via each of the control interfaces. In general, the joystick and force on sternum were the fastest in movement time (more than 40%) without any significant difference between them, but there was a significant difference between force on sternum on the one hand, and EMG and force on feet on the other. All control interfaces proved to be feasible solutions for controlling an active trunk support, each of which had specific advantages.

Selecting Sensitive Parameter Subsets in Dynamical Models With Application to Biomechanical System Identification.

Estimating many parameters of biomechanical systems with limited data may achieve good fit but may also increase 95% confidence intervals in parameter estimates. This results in poor identifiability in the estimation problem. Therefore, we propose a novel method to select sensitive biomechanical model parameters that should be estimated, while fixing the remaining parameters to values obtained from preliminary estimation. Our method relies on identifying the parameters to which the measurement output is most sensitive. The proposed method is based on the Fisher information matrix (FIM). It was compared against the nonlinear least absolute shrinkage and selection operator (LASSO) method to guide modelers on the pros and cons of our FIM method. We present an application identifying a biomechanical parametric model of a head position-tracking task for ten human subjects. Using measured data, our method (1) reduced model complexity by only requiring five out of twelve parameters to be estimated, (2) significantly reduced parameter 95% confidence intervals by up to 89% of the original confidence interval, (3) maintained goodness of fit measured by variance accounted for (VAF) at 82%, (4) reduced computation time, where our FIM method was 164 times faster than the LASSO method, and (5) selected similar sensitive parameters to the LASSO method, where three out of five selected sensitive parameters were shared by FIM and LASSO methods.

Haptic Guidance on Demand: A Grip-Force Based Scheduling of Guidance Forces.

In haptic shared control systems (HSC), a fixed strength of guidance force equates to a fixed level of control authority, which can be insufficient for complex tasks. An adaptable control authority based on operator input can allow the HSC system to better assist the operator under varied conditions. In this paper, we experimentally investigate () an adaptable authority HSC system that provides the operator with a direct way to adjust the control authority based on applied grip force. This system can serve as an intuitive 'manual override' function in case of HSC system malfunction. In a position tracking task, we explore two opposite approaches to adapt the control authority: increasing versus decreasing guidance strength with operator grip. These approaches were compared with unassisted control and two levels of fixed-level haptic guidance. Results show that the grip-adaptable approach allowed the operators to increase performance over unassisted control and over a weak guidance. At the same time, the approach substantially reduced the operator physical control effort required to cope with HSC system disturbances. Predictions based on the formalized model of the complete human-in-the-loop system corresponded to the experimental results, implying that such validated formalization can be used for model-based analysis and design of guidance systems.

Implementation of EMG- and Force-Based Control Interfaces in Active Elbow Supports for Men With Duchenne Muscular Dystrophy: A Feasibility Study.

While there is an extensive number of studies on the development and evaluation of electromyography (EMG)- and force-based control interfaces for assistive devices, no studies have focused on testing these control strategies for the specific case of adults with Duchenne muscular dystrophy (DMD). This paper presents a feasibility study on the use of EMG and force as control interfaces for the operation of active arm supports for men with DMD. We have built an experimental active elbow support, with a threefold objective: 1) to investigate whether adult men with DMD could use EMG- and force-based control interfaces; 2) to evaluate their performance during a discrete position-tracking task; and 3) to examine users' acceptance of the control methods. The system was tested in three adults with DMD (21-22 years). Although none of the three participants had performed any voluntary movements with their arms for the past 3-5 years, all of them were 100% successful in performing the series of tracking tasks using both control interfaces (mean task completion time EMG: [Formula: see text] , force: [Formula: see text] ). While movements with the force-based control were considerably smoother in Subject 3 and faster in Subject 1, EMG based-control was perceived as less fatiguing by all three subjects. Both EMG- and force-based interfaces are feasible solutions for the control of active elbow supports in adults with DMD and should be considered for further investigations on multi-DOF control.

Evaluation of EMG, force and joystick as control interfaces for active arm supports.

BackgroundThe performance capabilities and limitations of control interfaces for the operation of active movement-assistive devices remain unclear. Selecting an optimal interface for an application requires a thorough understanding of the performance of multiple control interfaces.MethodsIn this study the performance of EMG-, force- and joystick-based control interfaces were assessed in healthy volunteers with a screen-based one-dimensional position-tracking task. The participants had to track a target that was moving according to a multisine signal with a bandwidth of 3 Hz. The velocity of the cursor was proportional to the interface signal. The performance of the control interfaces were evaluated in terms of tracking error, gain margin crossover frequency, information transmission rate and effort.ResultsNone of the evaluated interfaces was superior in all four performance descriptors. The EMG-based interface was superior in tracking error and gain margin crossover frequency compared to the force- and the joystick-based interfaces. The force-based interface provided higher information transmission rate and lower effort than the EMG-based interface. The joystick-based interface did not present any significant difference with the force-based interface for any of the four performance descriptors. We found that significant differences in terms of tracking error and information transmission rate were present beyond 0.9 and 1.4 Hz respectively.ConclusionsDespite the fact that the EMG-based interface is far from the natural way of interacting with the environment, while the force-based interface is closer, the EMG-based interface presented very similar and for some descriptors even a better performance than the force-based interface for frequencies below 1.4 Hz. The classical joystick presented a similar performance to the force-based interface and holds the advantage of being a well established interface for the control of many assistive devices. From these findings we concluded that all the control interfaces considered in this study can be regarded as a candidate interface for the control of an active arm support.

Associated Reactions during a Visual Pursuit Position Tracking Task in Hemiplegic and Quadriplegic Cerebral Palsy

Most previous studies of associated reactions (ARs) in people with cerebral palsy have used observation scales, such as recording the degree of movement through observation. The sensitive quantitative method can detect ARs that are not amply visible. The aim of this study was to provide quantitative measures of ARs during a visual pursuit position tracking task. Twenty-three hemiplegia (H) (mean +/- SD: 21y 8m +/- 11y 10m), twelve quadriplegia (Q) (21y 5m +/- 10y 3m) and twenty-two subjects with normal development (N) (21y 2m +/- 10y 10m) participated in the study. An upper limb visual pursuit tracking task was used to study ARs. The participants were required to follow a moving target with a response cursor via elbow flexion and extension movements. The occurrence of ARs was quantified by the overall coherence between the movements of tracking and non-tracking limbs and the amount of movement due to ARs was quantified by the amplitude of movement the non-tracking limbs. The amplitude of movement of the non-tracking limb indicated that the amount of ARs was larger in the Q group than the H and N groups with no significant differences between the H and N groups. The amplitude of movement of the non-tracking limb was larger during non-dominant than dominant tracking in all three groups. Some movements in the non-tracking limb were correlated with the tracking limb (correlated ARs) and some movements that were not correlated with the tracking limb (uncorrelated ARs). The correlated ARs comprised less than 40% of the total ARs for all three groups. Correlated ARs were negatively associated with clinical evaluations, but not the uncorrelated ARs. The correlated and uncorrelated ARs appear to have different relationships with clinical evaluations, implying the effect of ARs on upper limb activities could be varied.

Comparison of electromyography and force as interfaces for prosthetic control

The ease with which persons with upper-limb amputations can control their powered prostheses is largely determined by the efficacy of the user command interface. One needs to understand the abilities of the human operator regarding the different available options. Electromyography (EMG) is widely used to control powered upper-limb prostheses. It is an indirect estimator of muscle force and may be expected to limit the control capabilities of the prosthesis user. This study compared EMG control with force control, an interface that is used in everyday interactions with the environment. We used both methods to perform a position-tracking task. Direct-position control of the wrist provided an upper bound for human-operator capabilities. The results demonstrated that an EMG control interface is as effective as force control for the position-tracking task. We also examined the effects of gain and tracking frequency on EMG control to explore the limits of this control interface. We found that information transmission rates for myoelectric control were best at higher tracking frequencies than at the frequencies previously reported for position control. The results may be useful for the design of prostheses and prosthetic controllers.

Global Localization and Position Tracking of an Automated Guided Vehicle

AbstractA swarm of Automated Guided Vehicles (AGVs) can be used in warehouses, distribution centers and manufacturing plants in order to automate the internal material flow. This swarm can overcome the disadvantages of roll conveyors and belts. The main problems of AGVs are global localization, position tracking, path planning and communication (especially within the swarm). Another technique which provides potential for logistic applications are Wireless Sensor Networks (WSNs). They can be used in warehouses to realize a decentralized stock database, monitoring a cold chain as well as for localization of an AGV and communication within the swarm. The paper presents a technique for global localization and position tracking of an omnidirectional AGV equipped with Mecanum wheels, which was designed to transport Euro-bins in a distribution center or warehouse. Global localization is realized through a technique based on range measurements obtained from an IEEE 802.15.4a WSN. The WSN is also used for communication within the swarm as well as for communication with the central warehouse computer. A developed sensor fusion of two safety laser range finders with the range measurements of a WSN solve the position tracking task. Laser range finders are used to detect pairs of landmarks to provide accurate positioning for docking maneuvers. The paper also presents a sensor model for IEEE 802.15.4a range measurements and experimental results. The latter show that the global localization technique guarantees accurate positioning and that the sensor fusion can be used for docking applications.

H∞ reinforcement learning control of robot manipulators using fuzzy wavelet networks

In this paper, an H ∞ reinforcement learning controller based on a fuzzy wavelet network (FWN) is proposed to perform a position-tracking task for a robot manipulator. The proposed controller adopts the actor-critic reinforcement learning control scheme. The primary reinforcement is generated by a performance measurement unit. The learning unit of the controller consists of an associative search network (ASN) and an adaptive critic network (ACN). The ASN is employed to approximate unknown nonlinear functions in the robot dynamics and the ACN is utilized to construct a more informative signal than the primary reinforcement alone to tune the ASN. Since the FWN can provide accurate function approximation, both the ASN and ACN are implemented by the FWN. In addition, the proposed controller requires no prior knowledge about the dynamics of the robot manipulators and no off-line learning phase. Moreover, by employing the H ∞ control theory, it is possible to attenuate the effects of the approximation errors of the FWNs and external disturbances to a prescribed level. In contrast to the general H ∞ problem, only simple equations, rather than Riccati equations, should be solved. Computer simulations on a SCARA robot with 3 degrees-of-freedom confirm the effectiveness of the FWN-based controller with H ∞ stabilization.

Fluctuations in motor output of a hand muscle can be altered by the mechanical properties of the position sensor

Fluctuations in motor output are typically quantified by the standard deviation (SD) of displacement or acceleration. The aim of the study was to determine the influence of a linear variable-displacement transducer (LVDT) on the SDs and spectral content of displacement and acceleration during steady isometric and anisometric contractions performed with the first dorsal interosseus muscle. Thirteen young adults supported six loads when performing position-holding and position-tracking tasks when the LVDT either was or was not attached to the index finger. The LVDT reduced the magnitude of the SDs in displacement and acceleration and disrupted the load-dependent modulation of the spectral properties of these signals. When the LVDT was not connected to the finger, the displacement SD was greatest during concentric contractions, the acceleration SD was greatest during eccentric contractions, and there were load-dependent changes in the power density spectra. Although the LVDT may be used for assessing relative changes in displacement, its ability to provide absolute measures of fluctuations in motor output is limited. The results provide baseline measures of the fluctuations in motor output during steady contractions with a hand muscle and how the method used to detect displacement alters these measures.

다관절 휴머노이드 로봇 팔의 제어를 위한 시간지연 제어기의 FPGA 구현 및 실험

In this paper, a time-delayed controller for position control of humanoid robot arms is designed and implemented on a field programmable gate array(FPGA) chip. The time-delayed control algorithm is simple to implement, and robust to reject disturbances. The time-delayed control method uses the one sample time-delayed previous information to cancel out uncertainties in the system. Since the sampling time is so fast with the current hardware technology, the time-delayed controller can be implemented. However, inertia values should be correctly estimated to have the better performance. The position tracking tasks of humanoid robot arms are tested to compare performances of several control algorithms including the time-delayed controller.