Motion Control Of Manipulators Research Articles

Motor control performance-related modulation of beta-band EEG-sEMG coherence differs between general and local muscular exercise-induced fatigue.

Exercise-induced fatigue can reduce motor control performance and increase the risk of sporting injuries, which are related to functional coupling within the corticomotoneuronal pathway. However, the differences in functional coupling caused by general and local muscular exercise-induced fatigue are unknown. This study aimed to investigate the effects of exercise-induced fatigue on the beta-band (16-30Hz) functional coupling between the sensorimotor cortex (SM1) and muscles of the dominant lower limb under different fatigue protocols. Twenty-four healthy male participants were recruited to participate in randomized sessions of personalized constant speed running as general muscular exercise (GME) and maximum isokinetic ankle plantar-dorsiflexion as local muscular exercise (LME) to induce fatigue. These sessions were separated by 7days. The electroencephalogram (EEG) signals of SM1 (e.g., FC1, FCz, and Cz) and surface electromyography signals (sEMG) of four muscles (soleus, SOL; medial gastrocnemius, MG; later gastrocnemius, and LG; tibialis anterior, TA) were simultaneously recorded before and after fatigue during the ankle plantar-dorsiflexion task, which were used for beta-band coherence analyses. Following fatigue induced by GME, the EEG-sEMG coherence was significantly greater than that induced by LME (P < 0.04). Compared to pre-fatigue state, the coherence of FC1-SOL, FCz-SOL, and Cz-SOL increased significantly after general fatigue, while these coherences decreased significantly after local fatigue. Fatigue induced by GME indicates an enhancement in beta-band functional coupling between the SM1 and muscles of the dominant lower limb, which is related to higher motor control performance. In contrast, fatigue induced by LME diminishes the functional coupling.

Robust adaptive motion control for cable-driven aerial manipulators with input saturation

This article explores robust adaptive motion control for a cable-driven aerial manipulator in the presence of input saturation. The investigation begins by considering the physical attributes of the aerial manipulator and establishing a kinematic and dynamic model of the system with lumped disturbances. Following this, a new control law is formulated using the parameter adaptive technique, where the adaptive law offers continuous estimation of the virtual parameters. Moreover, a Gaussian error function is utilized to tackle the challenge of input saturation. The stability of the controller proposed is confirmed using Lyapunov theory. Ultimately, numerical simulations and experimental comparisons are conducted to validate the accuracy and effectiveness of the proposed control strategy.

Changes in the cortical GABAergic inhibitory system with ageing and ageing-related neurodegenerative diseases.

The human cortical inhibitory system is known to play a vital role for normal brain development, function, and plasticity. GABA is the most prominent inhibitory neurotransmitter in the CNS and is a key regulator not only for motor control and motor learning, but also for cognitive processes. With ageing and many neurodegenerative pathologies, a decline in GABAergic function in several cortical regions together with a reduced ability to task-specifically modulate and increase inhibition in the primary motor cortex has been observed. This decline in intracortical inhibition is associated with impaired motor control but also with diminished motor-cognitive (i.e. dual-tasking) and cognitive performance (e.g. executive functions). Furthermore, more general well-being such as sleep quality, stress resistance or non-specific pain perception are also associated with reduced GABA functioning. The current review highlights the interplay between changes in GABAergic function and changes in motor control, motor-cognitive and cognitive performance associated with healthy ageing, as well as in seniors with neurodegenerative diseases such as mild cognitive impairment. Furthermore, recent evidence highlighting the ability to up- or downregulate cortical inhibition by means of physical exercise programs is presented and discussed.

Optimal Fully Actuated System Approach-Based Trajectory Tracking Control for Robot Manipulators.

In this article, a trajectory tracking control strategy is proposed for robot manipulators via a fully actuated system (FAS) approach, which has shown its simplicity and flexibility for most of the nonlinear controller design. However, the motion control for robot manipulators is more complicated since unknown dynamical model, external disturbances, friction forces, and various physical constraints are required to be considered. Therefore, the FAS approach cannot be straightforwardly applied. To address these challenges, the dynamic model of robot manipulators is established via model identification methods. Furthermore, based on the identified model, an FAS composite control strategy with simple structure is designed, which is achieved by integrating a high-order disturbance observer (HODO) in the inner loop, with an FAS trajectory tracking controller in the outer loop. Specifically, the HODO is utilized for handling the uncertain dynamics and external disturbances. Moreover, the controller gains are optimized using a gradient-based optimal parameter tuning method (OPTM). By imposing joint angle constraints, joint angular velocity constraints, and input torque limits into the formulation, the OPTM also ensures the satisfaction of these physical constraints. Numerical simulations and experiments are provided to validate the performance of the proposed controller.

Enhanced Operation Mode Design and Motion Control of a Dual-Redundancy Electro-Hydrostatic Actuator

In dual-redundancy electro-hydrostatic actuators (EHAs), the dual pumps are mainly designed for safety, where the cylinder is controlled mainly by one pump while the other one is standby for redundancy. However, such a strategy is basically like a single-pump-controlled system, and the flow from the pump may be inaccurate when the cylinder moves slowly, which will affect the motion control performance. A new dual-redundancy EHA is designed, and a series of corresponding operation modes are developed, enabling differential operation of the dual pumps. With the proposed operation modes, the inevitable flow inaccuracy problem of the single pump can be addressed through the coordination control of the dual pumps. In order to achieve better motion tracking performance, a model-based backstepping controller is synthesized, where the nonlinearities and uncertainties of the EHA are handled by model compensation and robust feedback design. Comparative simulations with existing control methods for EHAs are conducted and better motion tracking precision is achieved, especially during low-speed motion.

A Comprehensive Review of Mobile Robot Navigation Using Deep Reinforcement Learning Algorithms in Crowded Environments

Navigation is a crucial challenge for mobile robots. Currently, deep reinforcement learning has attracted considerable attention and has witnessed substantial development owing to its robust performance and learning capabilities in real-world scenarios. Scientists leverage the advantages of deep neural networks, such as long short-term memory, recurrent neural networks, and convolutional neural networks, to integrate them into mobile robot navigation based on deep reinforcement learning. This integration aims to enhance the robot's motion control performance in both static and dynamic environments. This paper illustrates a comprehensive survey of deep reinforcement learning methods applied to mobile robot navigation systems in crowded environments, exploring various navigation frameworks based on deep reinforcement learning and their benefits over traditional simultaneous localization and mapping-based frameworks. Subsequently, we comprehensively compare and analyze the relationships and differences among three types of navigation: autonomous-based navigation, navigation based on simultaneous localization and mapping, and planning-based navigation. Moreover, the crowded environment includes static, dynamic, and a combination of obstacles in different typical application scenarios. Finally, we offer insights into the evolution of navigation based on deep reinforcement learning, addressing the problems and providing potential solutions associated with this emerging field.

Adaptive fault tolerant control of unmanned underwater glider with predefined-time stability

Actuator faults of the underwater gliders (UGs) not only jeopardize the motion control performance but may also threaten the sailing safety. This paper proposes an adaptive sliding mode fault-tolerant controller with predefined-time stability for the UGs, considering the modeling uncertainties and the unknown disturbances. Three types of actuator faults are developed and incorporated in the fault model of the UGs, which are utilized for the fault tolerant controller design. Then, the adaptive terms are utilized to estimate the modeling uncertainties and the unknown disturbances. An adaptive sliding mode fault tolerant controller is thus designed with the theoretical proof that the global stability can be achieved within a predefined time. The hardware-in-the-loop simulations are conducted to verify the effectiveness of the proposed fault tolerant controller. The simulation results show that the UG can achieve the stability of the pitching attitude under three kinds of actuator fault conditions.

Effect of integrated manual and verbal cueing on functional transfers in chronic stroke survivors: a randomized controlled study

Purpose The profession of physical therapy has historically relied on manual facilitation to improve motor control strategies and performance in persons rehabilitating from a stroke, yet there is insufficient evidence to support its use during functional task training. The purpose of this study was to determine the effects of integrated cueing (verbal and manual) and verbal cueing approaches during sit-to-stand training on midline alignment & muscle activation in chronic stroke survivors. Methods Twenty-one chronic right-brained stroke survivors with hemiplegia were randomly assigned to the Integrated Cueing or Verbal Only group and outcome measures were recorded using an 18-Camera Motion Capture System, force plates, and surface electromyography (EMG). Results Both groups demonstrated a significant improvement in symmetry toward the midline after thirty training repetitions. Significant improvements in muscle activation were found in two muscle groups on the affected side of the body in the Integrated Cueing group, gastrocnemius and rectus femoris. Conclusion Both the Verbal Only and Integrated Cueing groups made significant progress toward more symmetrical movement, yet more significant changes in the activation of hemiparetic extensor muscles were seen in the Integrated Cueing group. These findings support the use of manual cueing in movement activation and performance during the training of functional tasks. IMPLICATIONS FOR REHAB Physical therapists commonly use manual tactile cues to facilitate movement performance but there is currently insufficient evidence to support this use with stroke survivors. In a relatively small sample size, both verbal cues and integrated cues (verbal plus manual) improved symmetry during sit to stand in chronic right brain stroke survivors. Integrated cueing enhanced motor activation and performance during sit to stand better than verbal cues alone. Manual tactile cueing should be considered during functional task retraining.

Cognitive profiles and developmental variations in ADHD: A comparative analysis of childhood and adolescent diagnoses

This retrospective study investigates the cognitive profiles of individuals with ADHD, categorized by the age at which they were diagnosed—either during childhood or adolescence. The sample comprised 424 participants aged 6 to 20 years, with a predominance of males. Participants were assessed using a variety of neuropsychological standardized tests. The study found significant differences in cognitive performance between those diagnosed in childhood and those diagnosed in adolescence. Specifically, childhood diagnoses were associated with poorer performance in vigilance, selective attention, and motor control, while adolescent diagnoses were linked to lower scores in the environment’s perception of their flexibility, working memory, and planning. Binary logistic regression analyses indicated that the neuropsychological profile for the combined ADHD subtype did not vary by age group, in contrast with the inattentive subtype, in which different cognitive constructs were identified serving as significant predictors. Findings suggest that the cognitive challenges associated with ADHD evolve with development, highlighting the need for age-appropriate diagnostic criteria and interventions.

Anti‐Interference Sliding Mode Control of PMSM Based on Improved Salp Swarm Algorithm

AbstractAiming at the problem that the control performance of permanent magnet synchronous motor is easily affected by load disturbance during operation, a fractional‐order sliding mode control method combining fractal‐order sliding mode controller (FOSMC) and fractional‐order sliding mode load torque observer (FOSMO) is proposed. The parameters of this method are tuned using the improved salp swarm algorithm (ISSA). Initially, the fractional‐order sliding mode controller is designed by applying fractional‐order calculus theory to enhance the system's response speed. Subsequently, a fractional‐order sliding mode load torque observer is developed to improve the system's anti‐interference capability and robustness due to the slow response and low identification accuracy of traditional load torque observers. Lastly, the control parameters are self‐tuned using the improved salp swarm algorithm due to the difficulty of adjusting them manually. Simulation and experimental results demonstrate that the proposed control method exhibits good convergence, no overshoot, minimal buffeting, and strong robustness against uncertain factors like load disturbances. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Efficacy of Transcranial Direct Current Stimulation and Mirror Therapy for Rehabilitation of Spastic Quadriplegic Cerebral Palsy-A Pilot Study

Background: Advancements in medical science, including Transcranial Direct Current Stimulation (tDCS) and Mirror Therapy (MT), are being utilized to treat Cerebral Palsy (CP), a non-progressive condition that impacts neuromuscular development. Objective: To compare the effects of tDCS and MT for rehabilitation of spastic quadriplegic CP Methods: A Double-Blind Randomized Clinical Trial was conducted at the Department of Physical Therapy, Ghurki Hospital, Lahore, Pakistan. Thirty CP patients were randomly assigned by sealed envelopes to three equal groups: Group A (tDCS, MT), and Routine Physical Therapy (RPT)), Group B (tDCS and RPT), and Group C (MT and RPT). Participants underwent tDCS with MT for 30 minutes per session, five times weekly for two weeks. RPT was also provided for 20 minutes per session, five times weekly for 10 weeks. Motor Development (MD) was assessed using the Shoaib Sensorimotor Development Tool (SMDT), Motor Control (MC) by Fugl-Meyer Assessment (UE, LE), and Muscle Performance (MP) by isokinetic dynamometer at baseline, after two weeks, and after ten weeks of follow-up. Results: Group A demonstrated a more significant impact on rehabilitation in terms of motor development, motor control, and muscle performance compared to Groups B and C, based on mean comparisons after two and ten weeks. There was a statistically significant difference between the groups in MD (P = 0.01), MC (P = 0.02), and MP (P = 0.04). Conclusion: Transcranial direct current stimulation, either alone or in combination, has a more consistent impact on the rehabilitation of spastic quadriplegic cerebral palsy patients. Trial Registration: Registered in Iranian registry on 26-01-2024 (IRCT20231227060542N1) https://irct.behdasht.gov.ir/

Modeling of genetic algorithm tuned adaptive fuzzy fractional order PID speed control of permanent magnet synchronous motor for electric vehicle

This study presents a novel Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative (GA-AFFFOPID) controller for enhancing the speed control performance of permanent magnet synchronous motor (PMSM) drives in Electric Vehicles. The proposed GA-AFFFOPID controller, which combines the advantages of genetic algorithm optimization and adaptive fuzzy fractional-order PID control, represents a unique and innovative approach to address the control challenges associated with PMSM drives. Permanent magnet synchronous motor technology, known for its efficiency, compactness, reliability, and versatility in motion control applications, is increasingly adopted in electric vehicle drive systems. However, the inherent non-linearity, dynamics, and uncertainties of permanent magnet synchronous motors pose significant control challenges. The exceptional performance of the GA-AFFFOPID controller, demonstrated through its superior system dynamics, precise speed tracking, and robustness against parameter variations and sudden load disturbances, underscores the significant advancements enabled by the genetic algorithm optimization technique in improving the control performance of PMSM drives for electric vehicle applications. Comparative analysis with traditional control methods demonstrates the exceptional performance of the Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative controller. These findings highlight the significant performance improvements facilitated by the genetic algorithm optimization technique in enhancing the control performance of the adaptive fuzzy fractional order PID controller in PMSM drives for electric vehicle applications.

Asynchronous motor resistance thermal drift prediction and compensation strategy based on sparse search optimization of backpropagation (BP) neural network algorithm

This paper proposes a compensation strategy for thermal drift in motor resistance to enhance the control precision of induction motors. The compensation strategy primarily utilizes a sparse search algorithm (SSA) to adjust the initial parameters of a backpropagation (BP) neural network, establishing a refined thermal drift prediction and compensation model (SSA-BP). An 18 kW asynchronous motor is used as the test sample, with stator temperature and phase resistance data collected for model training and testing. The resistance prediction performance of the SSA-BP model is compared with that of BP and particle swarm optimization-BP models and the algorithm models are evaluated using metrics such as R², root mean square error, mean bias error, mean square error, and mean absolute percentage error. The comparison results demonstrate that the SSA-BP algorithm model exhibits higher prediction accuracy and generalization capability in the prediction of motor resistance thermal drift. Finally, by employing the SSA-BP thermal drift prediction compensation strategy in motor control, the tracking performance of d-axis and q-axis currents and speed feedback is superior to that before compensation, thereby optimizing the motor control performance.

A New Method for Displacement Modelling of Serial Robots Using Finite Screw

Kinematics is a hot topic in robotic research, serving as a foundational step in the synthesis and analysis of robots. Forward kinematics and inverse kinematics are the prerequisite and foundation for motion control, trajectory planning, dynamic simulation, and precision guarantee of robotic manipulators. Both of them depend on the displacement models. Compared with the previous work, finite screw is proven to be the simplest and nonredundant mathematical tool for displacement description. Thus, it is used for displacement modelling of serial robots in this paper. Firstly, a finite-screw-based method for formulating displacement model is proposed, which is applicable for any serial robot. Secondly, the procedures for forward and inverse kinematics by solving the formulated displacement equation are discussed. Then, two typical serial robots with three translations and two rotations are taken as examples to illustrate the proposed method. Finally, through Matlab simulation, the obtained analytical expressions of kinematics are verified. The main contribution of the proposed method is that finite-screw-based displacement model is highly related with instantaneous-screw-based kinematic and dynamic models, providing an integrated modelling and analysis methodology for robotic mechanisms.

Cortical activity during online motor control in children with and without developmental coordination disorder: a cross-sectional functional near-infrared spectroscopy study

BackgroundChildren with developmental coordination disorder (DCD) have impaired online motor control. Researchers posit that this impairment could be due to a deficit in utilizing the internal model control process. However, there is little neurological evidence to support this view because few neuroimaging studies have focused specifically on tasks involving online motor control. Therefore, the aim of this study was to investigate the differences in cortical hemodynamic activity during an online movement adjustment task between children with and without DCD.MethodsTwenty children with DCD (mean age: 9.88 ± 1.67 years; gender: 14M/6F) and twenty age-and-gender matched children with typical development (TD) (mean age: 9.87 ± 1.59 years; gender: 14M/6F) were recruited via convenience sampling. Participants performed a double-step reaching task under two conditions (with and without online adjustment of reaching). Cortical hemodynamic activity during task in ten regions of interest, including bilateral primary somatosensory cortex, primary motor cortex, premotor cortex, superior parietal cortex, and inferior parietal cortex was recorded using functional near-infrared spectroscopy. In the analyses, change in oxyhemoglobin (ΔHbO) concentration was used to characterize hemodynamic response. Two-way analyses of variance were conducted for each region of interest to compare hemodynamic responses between groups and conditions. Additionally, Pearson’s r correlations between hemodynamic response and task performance were performed.ResultsOutcome showed that children with DCD required significantly more time to correct their reaching movements compared to the control group (t = 3.948, P < 0.001). Furthermore, children with DCD have a significantly lower ΔHbO change in the left superior parietal cortex during movement correction, compared to children with TD (F = 4.482, P = 0.041). Additionally, a significant negative correlation (r = − 0.598, P < 0.001) was observed between the difference in movement time of reaching and the difference in ΔHbO between conditions in the left superior parietal cortex.ConclusionsThe findings of this study suggest that deficiencies in processing real-time sensory feedback, considering the function of the superior parietal cortex, might be related to the impaired online motor control observed in children with DCD. Interventions could target this issue to enhance their performance in online motor control.

Analytical Inverse Kinematics for 2-Segment Extensible Continuum Manipulators

Abstract Fast inverse kinematics (IK) algorithm is significant for real-time precise motion control of continuum and soft manipulator. In this paper, we studied an explicit expression of two-segment continuum manipulator based on the constant curvature assumptions and discussed the existence of the IK solutions. This model used pseudo-rigid-body method, unlike 6-axis rigid industrial robot arm, we found that the two-segment extensible continuum manipulators have limited rotation angle around the direction vector of its tip, i.e., this type manipulator has limited dexterity. Then we pre-constrained five degrees of freedom (DOFs) constrained and discussed the definition of the left DOF. The IK solving process is simplified to realize small calculation cost for most applications. This model will provide a robust and real-time way to control the two-segment extensible continuum manipulators.

Improved Active Disturbance Rejection Control for Permanent Magnet Synchronous Motor

To improve the control performance of a permanent magnet synchronous motor (PMSM) under external disturbances, an improved active disturbance rejection control (IADRC) algorithm is proposed. Since the nonlinear function in the conventional ADRC algorithm is not smooth enough at the breakpoints, which directly affects the control performance, an innovative nonlinear function is proposed to effectively improve the convergence and stability. On this basis, the proposed IADRC is constructed, and comparative simulation results with ADRC and other IADRC show that faster response speed, higher accuracy and stronger robustness are obtained.

Load torque observation and compensation for permanent magnet synchronous motor based on sliding mode observer

Background Permanent magnet synchronous motors (PMSM) are widely used in various industries. However, in practical applications, the time-varying nature of load torque may lead to speed fluctuations, negatively impacting the motor's control performance and stability. To mitigate these issues, this paper proposes a load torque observation method for PMSMs based on a sliding mode observer. Methods The sliding mode observer is designed to estimate the load torque and convert it into a current, which is fed back as a feedforward compensation to the q-axis current in the current closed-loop control system. The observer's dynamic performance is optimized using a genetic algorithm to minimize the Integral of Time-weighted Absolute Error (ITAE) between the observer and the actual system state. Experimental tests are conducted on a motor torque testing platform. After stabilizing the motor at 800rpm, a sudden torque of 0.5Nm is applied. Results Compared to the situation without load torque compensation, the motor speed fluctuations are reduced by approximately 60% after adding load torque compensation. Conclusions This enhancement improves the system's speed control performance during torque variations and increases the system's robustness and disturbance rejection capability.

Switching Current Predictive Control of a Permanent Magnet Synchronous Motor Based on the Exponential Moving Average Algorithm

To improve the dynamic and steady-state control performance of permanent magnet synchronous motors under the three-vector model predictive current control method, this study proposes a switching current predictive control method based on the exponential moving average algorithm, which evaluates the magnitude of the change of the q-axis current slope in real time to discriminate the motor’s operating conditions and selects the optimal control method for different operating conditions. Meanwhile, the traditional three-vector model predictive current control method is improved by introducing a comparison mechanism for the q-axis current slope to select the second effective voltage vector, avoiding the secondary optimization calculation of the value function and reducing the computational complexity of the traditional method. By comparing the proposed method with the traditional three-vector model predictive current control method, the experimental results prove that the proposed method improves the system’s dynamic response and steady-state performance.

Advanced Motion Control of Hydraulic Manipulator With Precise Compensation of Dynamic Friction

Advanced Motion Control of Hydraulic Manipulator With Precise Compensation of Dynamic Friction