Motion Control Of Manipulators Research Articles

A Study on Performance and Characteristic Analysis According to the Operating Point of IPMSM Drive

This study presents the characteristics and performance of a motor at the operating point based on pulse-width modulation (PWM). PWM is used in modulating the output voltage of a three-phase inverter. The PWM technique has advantages and disadvantages depending on the system size and operating range. For example, some discontinuous pulse-width modulation (DPWM) techniques are advantageous in low-cost systems because they can reduce the number of switches. Some PWM techniques are complicated for software construction and require a lot of computation. In this paper, PWM techniques were constructed by using the offset voltage injection method. Therefore, the construction of the PWM technique is simplified by the method. In an experimental setup, an interior permanent-magnet synchronous motor (IPMSM) and a three-phase inverter were implemented to test motor control performance depending on the PWM technique. In addition, the current control characteristics and inverter efficiency were analyzed and compared depending on the speed and load of the motor. According to the results of this paper, it is possible to compare the motor and inverter control characteristics using various types of PWM rapidly. Additionally, it will help to select an appropriate PWM technique for a specific system.

Optimal design of Udwadia–Kalaba theory-based adaptive robust control for permanent magnet linear synchronous motor

The control performance of permanent magnet linear synchronous motor (PMLSM) is limited by uncertainty. The uncertainty is bounded by an unknown bound and may be time-varying. The boundary information of uncertainty is unknown but can be described by fuzzy set theory. To mitigate the impact of uncertainty on control performance, this paper proposes a Udwadia–Kalaba theory-based adaptive robust control (UKBARC). The proposed controller contains a Udwadia–Kalaba theory-based controller and an adaptive robust controller. In the Udwadia–Kalaba theory-based controller, the expected trajectory is taken as the expected constraint, and then the control task is converted to make the expected constraint be followed. Additionally, the adaptive robust controller is introduced to handle the system uncertainty without requiring the boundary information of uncertainty. The Lyapunov method is used to show that the performance of uniform boundedness (UB) and uniform ultimate boundedness (UUB) for the PMLSM system is guaranteed. The objective function used to characterize the trade-off between performance and control cost can be derived based on the fuzzy description of uncertainty. Thus, the optimal design of UKBARC (OUKBARC) can be achieved by minimizing the objective function. Simulation and experiment results together verify that high precision motion control can be ensured by using the proposed approach.

Development of a neural network for diagnosing the risk of depression according to the experimental data of the stop signal paradigm.

These days, the ability to predict the result of the development of the system is the guarantee of the successful functioning of the system. Improving the quality and volume of information, complicating its presentation, the need to detect hidden connections makes it ineffective, and most often impossible, to use classical statistical forecasting methods. Among the various forecasting methods, methods based on the use of artificial neural networks occupy a special place. The main objective of our work is to create a neural network that predicts the risk of depression in a person using data obtained using a motor control performance testing system. The stop-signal paradigm (SSP) is an experimental technique to assess a person's ability to activate deliberate movements or inhibit movements that have become inadequate to external conditions. In modern medicine, the SSP is most commonly used to diagnose movement disorders such as Parkinson's disease or the effects of stroke. We hypothesized that SSP could serve as a basis for detecting the risk of affective diseases, including depression. The neural network we are developing is supposed to combine such behavioral indicators as: the amount of missed responses, amount of correct responses, average time, the amount of correct inhibition of movements after stop-signal onset. Such a combination of indicators will provide increased accuracy in predicting the presence of depression in a person. The artificial neural network implemented in the work allows diagnosing the risk of depression on the basis of the data obtained in the stop-signal task. An architecture was developed and a system was implemented for testing motor control indicators in humans, then it was tested in real experiments. A comparison of neural network technologies and methods of mathematical statistics was carried out. A neural network was implemented to diagnose the risk of depression using stop-signal paradigm data. The efficiency of the neural network (in terms of accuracy) was demonstrated on data with an expert assessment for the presence of depression and data from the motor control testing system.

The Research on the Motion Control of the Sorting Manipulator based on Machine Vision

With the development of production technology, manipulators are gradually introduced in advanced production manufacturing industries to complete some tasks such as picking and sorting. However, the traditional manipulator has a complicated sorting process and low production efficiency. In order to improve the accuracy of sorting and reduce the labor intensity of workers, this paper studied the motion control of the sorting manipulator with machine vision. After placing four kinds of objects of different shapes on the conveyor belt, experiments were conducted on the catching and sorting process of the manipulator under different experimental environments, different conveyor belt speeds, and with or without machine vision. It was found that the overall success rate of the sorting robotic arm using machine vision for catching objects of different shapes was as high as 96%, and the sorting accuracy was as high as 97.91%. Therefore, it is concluded that the manipulator can achieve high accuracy in catching and sorting objects with the guidance of machine vision, and the adoption of machine vision has a positive impact on the motion control of the sorting manipulator.

The Non-Singular Fast Terminal Sliding Mode Control of Interior Permanent Magnet Synchronous Motor Based on Deep Flux Weakening Switching Point Tracking

This paper presents a novel non-singular fast terminal sliding mode control (NFTSMC) based on the deep flux weakening switching point tracking method in order to improve the control performance of permanent interior magnet synchronous motor (IPMSM) drive systems. The mathematical model of flux weakening (FW) control is established, and the deep flux weakening switching point is calculated accurately by analyzing the relationship between the torque curve and voltage decline curve. Next, a second-order NFTSMC is designed for the speed loop controller to ensure that the system converges to the equilibrium state in finite time. Then, an extended sliding mode disturbance observer (ESMDO) is designed to estimate the uncertainty of the system. Finally, compared with both the PI control and sliding mode control (SMC) by simulations and experiments with different working conditions, the method proposed has the merits of accelerating convergence, improving steady-state accuracy, and minimizing the current and torque pulsation.

Analysis and Compensation of Sigma-Delta ADC Latency for High Performance Motor Control and Diagnosis

High performance motor control and fault diagnosis requires high resolution isolated signal measurement. Sigma-delta ADCs (SD-ADC) are ideal for this purpose but cause remarkable measurement latency which can cause performance degradation and stability issues. In this paper, the effects of current measurement latency caused by a sigma-delta filter module (SDFM) and corresponding latency compensation are discussed in detail. First, the effect of SD-ADC on system phase margin and ADC update delay is analyzed in detail. Consequently, a Kalman filter based technique is proposed to compensate the latency caused by the group delay of the digital filter and increase the phase/gain margin of the system. In addition, three PWM update strategies are proposed and their tradeoffs discussed. The proposed algorithm and measurement results are experimentally verified on a PMSM drive. It is shown that with the proposed solution, Sigma-delta ADC can provide much higher resolution than traditional SAR ADCs and also yield superior diagnostics capability and control.

A Discrete Model-Free Scheme for Fault-Tolerant Tracking Control of Redundant Manipulators

Fault tolerance is a critical requirement for robust motion control of redundant robotic manipulators. This article aims to endow the redundant manipulator with the capability to achieve the required path of end-effector in the condition that one or some of its joints’ motion fail. Although many fault-tolerant control algorithms of redundant manipulator have been proposed in recent years. However, few of them are on the basis of the condition that the robotic model is unknown. The complexity of the calculation model limits the efficiency and portability of these algorithms. For the first time, we proposed a discrete model-free fault-tolerant tracking control scheme of redundant manipulator, which takes into account the fault tolerance in the control system of redundant manipulator by formulating it into a quadratic programming (QP) framework. The core of the proposed scheme consists of a discrete kinematic estimator and a discrete QP solver, powered by which the fault-tolerant control problem is transformed into a unified computing problem relaxing the need of knowing the redundant manipulator’s kinematic model. A discrete joint space observer is proposed for the detection of the happening of faulty states. Extensive simulations and experiments based on a redundant manipulator are performed and analyzed to support the verification of the efficiency and effectiveness of the proposed scheme.

From Simulation to Reality: A Learning Framework for Fish-Like Robots to Perform Control Tasks

The fish-like robot is one of the typical underwater robots, which has the advantage of high maneuverability with low noise due to its bioinspired structure and biomimetic locomotion. However, it is challenging to efficiently design motion controllers for such robots to achieve satisfactory performance on specific control tasks in the real underwater environment, since the complex fluid-structure interaction exists during their swimming and exact dynamic models are absent. In this article, we propose a learning framework, incorporating a simulation system and a training methodology, to autonomously and fast train in simulation to create control policies that are capable of directly applying to a type of physical fish-like robots to perform motion control tasks. First, we construct a simulation system combining a data-driven environment and a computational fluid dynamics (CFD)-based environment, thus well balancing the simulation accuracy and the calculation speed. Second, we design a training methodology to train deep reinforcement learning (DRL)-based policies for the robot in our constructed simulation system to perform a specific control task. Then, we use two typical motion control tasks to verify our proposed framework. One is the path-following control task, which is a one-objective problem with dense rewards, while the other is the pose control task which is a two-objective problem with sparse rewards. For each task, the DRL-based control policy trained by our learning framework is directly deployed on the physical fish-like robot to perform the task in the real world. Experimental results show that the policies trained in simulation still work well in the real world, and perform even better in terms of control accuracy and stability compared with the traditional control methods, thus demonstrating the effectiveness of our learning framework.

Aerodynamic Force Control for Tilt-Wing eVTOL Using Airflow Vector Estimation

Research and development are active in electric vertical takeoff and landing (eVTOL) aircraft. In particular, tilt-wing eVTOL aircraft receives much attention as one of the most efficient configurations; however, they are likely to be unstable during the transition from hover to cruise because the lift and thrust have limitations depending on the airflow and tilt angle. This study proposes a new aerodynamic force control method using airflow vector estimation. The airflow vector is estimated by combining motor current, rotational speed, and Pitot-tube measurements. Aerodynamic force control is achieved through the proper design of a feedback controller using disturbance observers (DOBs) to cope with propeller-wing interference caused by the propeller slipstream. This method takes advantage of the motor control performance and is unique in which it monitors the airflow vector and actively changes the tilt angle to quickly obtain the desired acceleration. The effectiveness of the method is verified via simulations and experiments in a wind tunnel.

The momentary and the macro in action control: A motor control analysis of impulse control difficulties.

According to cybernetic models of self-regulation, the success of one's overt behaviors may very well depend on how well one controls one's behaviors in more momentary terms. The present research applies such perspectives to the analysis of individual differences in impulse control difficulties, which are thought to constitute losses of control in responses to stressors or distress, representing a form of emotional impulsivity. In three studies (total N = 349), undergraduate participants were asked to perform motor control tasks involving stationary or moving targets, which permitted the computation of a tremor instability index that represented the extent to which movements were noisy or unpredictable (less controlled) from moment to moment. Study 1 found that individuals with higher levels of impulse control difficulties exhibited higher levels of tremor, Study 2 replicated this pattern, and Study 3 showed that the tremor instability index predicted similar contingencies in daily life. The findings, in total, identify a micromomentary signature of macrolevel tendencies toward emotional impulsivity. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Sensing Design, Trajectory Planning, and Motion Control of a Cable-Driven Redundant Manipulator Composed of Quaternion Joints

Abstract A cable-driven redundant manipulator (CDRM) composed of quaternion joints has important applications in confined space, including minimal invasive surgery, aircraft parts assembly, environment exploring, and so on. Benefitting from the unique joint characteristic and cable routing, it can achieve a larger workspace with fewer driving modules than traditional universal joint CDRM. However, the positioning accuracy of the end-effector suffers from the lack of joint feedback information and the delay effect of cable driving mechanisms. In this paper, we propose an equivalent sensing method and design corresponding sensors for each quaternion joint, and develop a high precision controller for the whole manipulator. The motion sensing of the quaternion joint is achieved by establishing the kinematics between its bending pose and middle limb joints. To realize real-time estimation, the fitting technique is adopted. To improve the efficiency of path planning, a geometric iterative inverse kinematics approach for quaternion joint CDRM is proposed based on the isosceles trapezoid simplified model. Furthermore, an accurate controller is designed by combining the feedforward gain and modified PID feedback control. Finally, an 8DOF CDRM prototype with four quaternion joints is developed, and the experiment verifies the effectiveness of the proposed method.

Adaptive Appointed-Time Consensus Control of Networked Euler–Lagrange Systems With Connectivity Preservation

With consideration of motion control performance and efficient information communication, the synchronization problem on communication connectivity preservation and guaranteed consensus performance for networked mechanical systems has attracted considerable attention in recent years. Different from the existing works, this article investigates a brand-new appointed-time consensus control approach for uncertain networked Euler-Lagrange systems on a directed graph via exploring the prescribed performance control structure. First, a two-layer prescribed performance envelope is formulated via using an appointed-time convergent function for position-related and velocity-related consensus errors, respectively. Then, a simple state-feedback virtual controller with online adaptive performance adjustment is developed to preserve the communication connectivity. Moreover, to guarantee the velocity consensus of the networked systems and improve the position consensus accuracy, an appointed-time adaptive controller is designed by applying the norm inequality to the system uncertainties and external disturbances. Compared to the existing consensus control approaches, the prime advantage of the proposed one is that the constraints generated from the communication ranges are approximated by a time-varying contractive performance envelope, wherein, the appointed-time convergence and steady-state tracking accuracy are preassigned a priori. Meanwhile, no repeated logarithmic error transformations are required in the relevant controller design, which implies that the complexity of the devised control laws has decreased dramatically. Finally, two groups of illustrative examples are organized to validate the effectiveness of the proposed consensus control approach.

철도차량 출입문 제어용 BLDC 전동기의 결선방법에 따른 전류 고조파 및 토크 특성 해석

In recent years, servo motors have been used for controlling the opening and closing of doors in addition to driving vehicles in railway vehicles. AC motors used as servo motors in the field can be divided into induction type and synchronous type. Synchronous motors used for small and medium capacity have the control performance of a DC motor and the robustness of an AC motor. Recently, for this purpose, a BLDC(Brushless Direct Current) motor, which is a permanent magnet type synchronous motor including a permanent magnet in a rotor, has been applied. The BLDC motor has the structure of a rotating field type motor that rotates the field after removing the brush and commutator from the DC motor. Since the BLDC motors do not use brushes, they do not have problems such as mechanical noise, friction, and sparks, so long-term durability can be secured, which has great advantages in terms of efficiency and maintenance. The door engine part is an important device in an electric door, which is one of the door driving methods of railroad vehicles. As an electric motor in charge of opening and closing doors accurately, the development of a BLDC motor with improved maintainability and direct driving of railroad doors without a reducer is required. In this study, the characteristic equations were derived for each analysis mode of the motor using the cut set equations. The current, current harmonics and torque characteristics were analyzed for the delta and wye connections of the stator winding while changing the firing angle in each analysis mode.

The Effect of Olfactory Inhalation on KPGA Golfers' Putting Performance, Postural Stability and Heart Rate.

Some athletes utilize olfactory inhalation treatments using ammonia salt and aromatic oils to attain their peak performance or for physical and psychological relaxation. However, there is still a lack of clear evidence on olfactory inhalation treatment and scent types via precise experiments, and there is no research regarding fine motor control performance in activities such as golf putting. Thus, the purpose of this study was to examine the effects of various olfactory inhalations (lavender, citrus, and ammonia) on professional golfers’ 3-meter putting performance (percentage of success), postural stability (CoP area), and heart rate (HR). In order to examine the effects of olfactory treatment on actual automated task performance, ten professional golfers were recruited for the putting task experiment. During the putting task, a biometric shirt was utilized to record the HR changes, and a force plate was used to measure changes in the CoP area. The results were as follows. First, the olfactory inhalation treatment inhibited the putting performance (no inhalation: 68.75%; lavender: 51.25%; citrus: 40.00%; ammonia: 52.50%); however, no statistically significant difference was found (p = 0.115). Second, the olfactory inhalation treatment inhibited postural stability while putting; it had a partially statistically significant lower value (address: p = 0.000; downswing: p = 0.035; total putting section: p = 0.047). Third, the olfactory inhalation treatment decreased the HR during putting; however, there was no statistically significant difference between groups (address: p = 0.838; putting: p = 0.878; total: p = 0.666). This study implies that olfactory inhalation affects putting performance, postural stability, and HR. The effect size results for the olfactory treatment in the CoP area during the putting task (address: η2 = 0.524; downswing: η2 = 0.349; total putting section: η2 = 0.298) suggest that arousal regulation through olfactory inhalation may have negative effects on dynamic postural stability in static tasks such as golf putting, showing the direction of its useful application for athletes in sports.

Generalized predictive speed control based on equivalent-input-disturbance for PMSM drive system

The speed control performance of permanent magnet synchronous motor (PMSM) drive system is degraded due to non-matching disturbances such as parameter perturbation and load torque mutation. This paper presents a nonlinear generalized predictive control method based on equivalent-input-disturbance (GPC-based-EID) to realize the fast response and strong robustness of the speed controller of the PMSM drive system. Firstly, the continuous time nonlinear system of a motor mechanical equation is established. A speed controller based on the generalized predictive theory rather than the PI controller of a traditional vector control is designed. Then, the drive system with disturbance is transformed into an EID system. An improved nonsingular fast terminal sliding-mode observer (NFTSMO) is introduced to accurately estimate the EID of total system non-matching disturbances. And the active compensation of non-matching disturbances is realized through feedforward method. This greatly enhances the robustness and the speed tracking performance of the drive system. Comparisons with PI control, traditional GPC and GPC-based-ESO methods show the effectiveness of the method.

Variable-Performance Nonlinear Feedback Filter Based on Dynamic Surface Stabilization Approach

The proposed surface stabilization technique based filter feedbacks the filtering error with its nonlinear variable gain algorithm in order to recover the source signal information rejecting the high-frequency component (e.g., low-pass filtering), thereby guaranteeing closed-loop stability. Thus, resultant implementation is possible without signal dynamics information. The result provides two contributions: first, the improvement of the transient filtering performance through the closed-form feedback gain magnification mechanism and second, the recovery of the exponential filtering performance due to the surface stabilization mechanism, including the exponential convergent disturbance estimator originated from the nonlinear disturbance observer. The experimental results verify the effectiveness of the proposed technique, which improves the 500-W three-phase motor control performance.

Training spiking neuronal networks to perform motor control using reinforcement and evolutionary learning.

Artificial neural networks (ANNs) have been successfully trained to perform a wide range of sensory-motor behaviors. In contrast, the performance of spiking neuronal network (SNN) models trained to perform similar behaviors remains relatively suboptimal. In this work, we aimed to push the field of SNNs forward by exploring the potential of different learning mechanisms to achieve optimal performance. We trained SNNs to solve the CartPole reinforcement learning (RL) control problem using two learning mechanisms operating at different timescales: (1) spike-timing-dependent reinforcement learning (STDP-RL) and (2) evolutionary strategy (EVOL). Though the role of STDP-RL in biological systems is well established, several other mechanisms, though not fully understood, work in concert during learning in vivo. Recreating accurate models that capture the interaction of STDP-RL with these diverse learning mechanisms is extremely difficult. EVOL is an alternative method and has been successfully used in many studies to fit model neural responsiveness to electrophysiological recordings and, in some cases, for classification problems. One advantage of EVOL is that it may not need to capture all interacting components of synaptic plasticity and thus provides a better alternative to STDP-RL. Here, we compared the performance of each algorithm after training, which revealed EVOL as a powerful method for training SNNs to perform sensory-motor behaviors. Our modeling opens up new capabilities for SNNs in RL and could serve as a testbed for neurobiologists aiming to understand multi-timescale learning mechanisms and dynamics in neuronal circuits.

Influence of Heat Exposure on Motor Control Performance and Learning as Well as Physiological Responses to Visuomotor Accuracy Tracking Task.

This study aimed to determine whether heat exposure attenuates motor control performance and learning, and blunts cardiovascular and thermoregulatory responses to visuomotor accuracy tracking (VAT) tasks. Twenty-nine healthy young adults (22 males) were divided into two groups performing VAT tasks (5 trials × 10 blocks) in thermoneutral (NEUT: 25 °C, 45% RH, n = 14) and hot (HOT: 35 °C, 45% RH, n = 15) environments (acquisition phase). One block of the VAT task was repeated at 1, 2, and 4 h after the acquisition phase (retention phase). Heat exposure elevated skin temperature to ~3 °C with a marginally increased core body temperature. VAT performance (error distance of curve tracking) was more attenuated overall in HOT than in NEUT in the acquisition phase without improvement in magnitude alteration. Heat exposure did not affect VAT performance in the retention phase. The mean arterial blood pressure and heart rate, but not for sweating and cutaneous vascular responses to VAT acquisition trials, were more attenuated in HOT than in NEUT without any retention phase alternations. We conclude that skin temperature elevation exacerbates motor control performance and blunts cardiovascular response during the motor skill acquisition period. However, these alternations are not sustainable thereafter.

A COMBINED MULTI-PRESSURE SWITCHED INERTANCE HYDRAULIC CONTROL CONCEPT OF A DIFFERENTIAL CYLINDER

Control of hydraulic drives using more than two pressure lines is studied as a means for improving energy efficiency, e.g. for excavators or for hydraulic power take off from sea wave power plant systems. All combinations of switching n different pressure levels to a differential cylinder provide in general 2.n force levels. In order to facilitate forces between these levels for a proper motion control, this digital concept was combined with throttling control. This, of course, causes energetic losses. In this paper a combination of multi-pressure and switched inertance control is presented and studied theoretically by mathematical modelling and simulation. The integration of the switched inertance part requires only a pipeline as an inertance element and a so called “Mikota resonator” flow and pressure ripple suppression. Simulation results show a good motion control performance and an efficiency gain over intermediate force adjustment by throttling in load lifting.

A new sensorless control strategy of the PMLSM based on an ultra-local model velocity control system

Abstract. To improve the control performance and dynamic response of the permanent magnet linear synchronous motor (PMLSM), a new sensorless control strategy of the PMLSM with the ultra-local model velocity control system is designed in this paper. Firstly, a model-free speed controller (MFSC) is constructed based on the principle of the ultra-local model. Meanwhile, based on the traditional sliding-mode observer (SMO), the back-electromotive force (BEMF) in the SMO is optimized by the model reference adaptive system (MRAS) to improve the observed speed information of the PMLSM. This control strategy improves the dynamic response ability and stability of the PMLSM system. Compared with the traditional motor control strategy, this design gets rid of the dependence on mechanical sensors, improves the dynamic response ability of the PMLSM, and reduces the velocity tracking error. The superiority of the control system is verified by simulation and experiment. Compared with the traditional dual proportional–integral (PI) control system and SMO, the new control strategy can improve the dynamic response performance of the PMLSM, enhance the stability, and track the speed information of the PMLSM with low error to reduce the chatter.