Motion Control Of Manipulators Research Articles

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.

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.

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

Neural Dynamics for Cooperative Motion Control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

Neural Dynamics for Cooperative Motion Control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

Optimizing Active Disturbance Rejection Control for a Stubble Breaking and Obstacle Avoiding Control System

In order to improve the obstacle avoidance control performance and anti-interference ability of a stubble breaking device of a no-tillage planter, a back-propagation neural network (BPNN)-optimized fuzzy active disturbance rejection control (ADRC) controller was designed to optimize the control performance of a servo motor. Firstly, a negative feedback mathematical model was established for the obstacle avoidance control system. Then, the nonlinear state error feedback (NLSEF) parameters in the fuzzy ADRC were intelligently optimized by the BPNN algorithm. In this way, a fuzzy ADRC controller based on BPNN optimization was formed to optimize the control process of a servo motor. Matlab/Simulink (R2022b) was used to complete the simulation model design and parameter adjustment. Consequently, the response time was 0.089 s using the BPNN fuzzy ADRC controller, which was shorter than the 0.303 s of the ADRC controller and the 0.100 s of the fuzzy ADRC controller. The overshoot was 0.1% using a BPNN fuzzy ADRC controller, which was less than the 2% of the ADRC controller and the 1% of the fuzzy ADRC controller. After noise signal interference was introduced into the control system, the regression steady state time of the BPNN fuzzy ADRC controller was 0.22 s, which was shorter than the 0.56 s of the ADRC controller and the 0.45 s of the fuzzy ADRC controller. A hardware-in-the-loop simulation experimental platform of the obstacle avoidance control system was constructed. The experiment results show that the servo motor control system has a fast dynamic response, small steady-state error and strong anti-interference ability for obstacle avoidance at the target height. Then, the control system error was within the allowable range. The servo motor control effect of the BPNN fuzzy ADRC was better than the ADRC and fuzzy ADRC. This optimized servo motor control method can provide a reference for improving the obstacle avoidance control effect problem of no-tillage seeders in stubble breaking operations on rocky desertification areas.

Dead-Time Compensation Using ADALINE for Reduced-Order Observer-Based Sensorless SynRM Drives

The inverter dead time effect is non-negligible for the control performance of sensorless synchronous reluctance motor (SynRM) drives at low speeds. In this paper, a reduced-order observer-based sensorless control method for SynRM drives combined with the adaptive linear neurons (ADALINE)-based dead-time compensation is proposed. The reduced-order observer-based sensorless control method is presented, for which is parameter tuning is easy. On this basis, the dead-time compensation strategy using ADALINE filters is proposed to reduce the voltage harmonics effect on the estimation performance of the reduced-order observer. With ADALINE filters, the sixth current harmonic can be successfully filtered out by compensating the voltage directly or fitting the current to compensate the voltage. In this way, the low-speed estimation performance of the reduced-order observer is improved. The effectiveness of the proposed method is verified on a 3 kW SynRM experimental platform.

Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review

The development of unmanned agricultural tractors (UAT) represents a significant step towards intelligent agricultural equipment. UAT technology is expected to lighten the workload of laborers and enhance the accuracy and efficiency of mechanized operations. Through the investigation of 123 relevant studies in the literature published in recent years, this article reviews three aspects of autonomous navigation technologies for UATs: perception, path planning and tracking, and motion control. The advantages and deficiencies of these technologies in the context of UATs are clarified by analyzing technical principles and the status of current research. We conduct summaries and analyses of existing unmanned navigation solutions for different application scenarios in order to identify current bottleneck issues. Based on the analysis of the applicability of autonomous navigation technologies in UATs, it can be seen that fruitful research progress has been achieved. The review also summarizes the common problems seen in current UAT technologies. The application of research to the sharing and integrating of multi-source data for autonomous navigation has so far been relatively weak. There is an urgent need for high-precision and high-stability sensing equipment. The universality of path planning methods and the efficiency and precision of path tracking need to be improved, and it is also necessary to develop highly reliable electrical control modules to enhance motion control performance. Overall, advanced sensors, high-performance intelligent algorithms, and reliable electrical control hardware are key factors in promoting the development of UAT technology.