Field Of Motor Control Research Articles

Efficient Nonlinear Model Predictive Path Tracking Control for Autonomous Vehicle: Investigating the Effects of Vehicle Dynamics Stiffness

Motion control is one of the three core modules of autonomous driving, and nonlinear model predictive control (NMPC) has recently attracted widespread attention in the field of motion control. Vehicle dynamics equations, as a widely used model, have a significant impact on the solution efficiency of NMPC due to their stiffness. This paper first theoretically analyzes the limitations on the discretized time step caused by the stiffness of the vehicle dynamics model equations when using existing common numerical methods to solve NMPC, thereby revealing the reasons for the low computational efficiency of NMPC. Then, an A-stable controller based on the finite element orthogonal collocation method is proposed, which greatly expands the stable domain range of the numerical solution process of NMPC, thus achieving the purpose of relaxing the discretized time step restrictions and improving the real-time performance of NMPC. Finally, through CarSim 8.0/Simulink 2021a co-simulation, it is verified that the vehicle dynamics model equations are with great stiffness when the vehicle speed is low, and the proposed controller can enhance the real-time performance of NMPC. As the vehicle speed increases, the stiffness of the vehicle dynamics model equation decreases. In addition to the superior capability in addressing the integration stability issues arising from the stiffness nature of the vehicle dynamics equations, the proposed NMPC controller also demonstrates higher accuracy across a broad range of vehicle speeds.

An Introduction to the Modular Forensic Handwriting Method

ABSTRACTThe forensic examination of handwriting utilizes complex human perceptual and cognitive processes that can be difficult to accurately and validly describe since, for the most part, these processes are unseen. The series of articles published in this WIREs special issue present the agreed general approach that government practitioners in Australia use in the majority of the handwriting comparisons that they carry out. The systematic modular approach is informed by research and developments in the fields of motor control of handwriting, human factors, validation of handwriting examination, and evidence evaluation and reporting. The method is based around a process outline which structures the comparison process and provides the reader with a guide to the tasks to undertake and issues to consider at the different stages commonly worked through in practical handwriting examinations. Where decision or assessment points occur within the course of the method, a series of modules have been developed which describe the nature of the matter under consideration and address relevant theoretical and practical issues. Each module (published as an article in this special issue) is, as far as is practical, independent of other modules in the method. This will facilitate changes in the process over time that may result from theoretical, practical or technological advances in the field. The document as a whole can be used by practitioners actively involved in casework, or individuals such as researchers, academics, and legal professionals with an interest in understanding or exploring aspects of the practice of forensic handwriting examination.

Neuro-Musculoskeletal Modeling for Online Estimation of Continuous Wrist Movements from Motor Unit Activities.

Decoding movement intentions from motor unit (MU) activities remains an ongoing challenge, which restricts our comprehension of the intricate transition mechanism from microscopic neural drive to macroscopic movements. This study presents an innovative neuro-musculoskeletal (NMS) model driven by MU activities for online estimation of continuous wrist movements. The proposed model employs a physiological and comprehensive utilization of MU firings and waveforms, thus facilitating the localization of MUs to muscle-tendon units (MTU) as well as the computation of MU-specific neural excitation. Subsequently, the MU-specific neural excitation was integrated to form the MTU-specific neural excitation, which were then inputted into a musculoskeletal model to accomplish the joint angle estimation. To assess the effectiveness of this model, high-density surface electromyography and angular data were collected from the forearms of eight subjects during their performance of wrist flexion-extension task. Two pieces of 8 × 8 electrode arrays and a motion capture system were employed for data acquisition. Following offline model calibration with a global optimization algorithm, online angle estimation results demonstrated a significant superiority of the proposed model over the state-of-the-art NMS models (p < 0.05), yielding the lowest normalized root mean square error (0.10 ± 0.02) and the highest determination coefficient (0.87 ± 0.06). This study provides a novel idea for the decoding of joint movements from MU activities. The research findings hold the potential to advance the development of NMS models towards the control of multiple degrees of freedom, with promising applications in the fields of motor control, biomechanics, and neuro-rehabilitation engineering.

Brain-wide dynamics linking sensation to action during decision-making.

Perceptual decisions rely on learned associations between sensory evidence and appropriate actions, involving the filtering and integration of relevant inputs to prepare and execute timely responses1,2. Despite the distributed nature of task-relevant representations3-10, it remains unclear how transformations between sensory input, evidence integration, motor planning and execution are orchestrated across brain areas and dimensions of neural activity. Here we addressed this question by recording brain-wide neural activity in mice learning to report changes in ambiguous visual input. After learning, evidence integration emerged across most brain areas in sparse neural populations that drive movement-preparatory activity. Visual responses evolved from transient activations in sensory areas to sustained representations in frontal-motor cortex, thalamus, basal ganglia, midbrain and cerebellum, enabling parallel evidence accumulation. In areas that accumulate evidence, shared population activity patterns encode visual evidence and movement preparation, distinct from movement-execution dynamics. Activity in movement-preparatory subspace is driven by neurons integrating evidence, which collapses at movement onset, allowing the integration process to reset. Across premotor regions, evidence-integration timescales were independent of intrinsic regional dynamics, and thus depended on task experience. In summary, learning aligns evidence accumulation to action preparation in activity dynamics across dozens of brain regions. This leads to highly distributed and parallelized sensorimotor transformations during decision-making. Our work unifies concepts from decision-making and motor control fields into a brain-wide framework for understanding how sensory evidence controls actions.

Correcting Ryle’s Mistake: Motor Redundancy and the Embodied Intelligence of Habits

Embodied cognition and enactive approaches have criticized the associationist, also called mechanistic, view of habits. Motivated by the enactive account of habits and research on the field of Motor Control, I argue that Ryle was both wrong and right about habits and their relation to intelligent behavior. Ryle was wrong in claiming that habits fall short of intelligent behavior. But Ryle was also right, he correctly puts habits in a continuity that goes from dispositions in general to what he considers bonafide intelligent behavior or knowing-how. After some background, the Rylean distinction between habits and intelligent behavior is explored. I then move to Ryle’s mistake and its roots, tacitly endorsing the idea of instinct as innate behavior. In doing so, he neglected that variability and modifiability are the rules and not the exceptions in behavior. Research on the phenomena of motor redundancy of animal behavior, the enactive account of habits, and a dynamic account of intelligence are mobilized to correct his mistake. My proposed account explains Ryle’s mistake, why it is a mistake, and why it is an understandable mistake coming from someone with his perspective. The last section of the paper re-articulates the continuum between simple dispositions, habits, and highly skilled behavior. I reconceived the continuum of intelligence as including not only our habits, but all animal behavior.

Muscle Synergy Analysis as a Tool for Assessing the Effectiveness of Gait Rehabilitation Therapies: A Methodological Review and Perspective.

According to the modular hypothesis for the control of movement, muscles are recruited in synergies, which capture muscle coordination in space, time, or both. In the last two decades, muscle synergy analysis has become a well-established framework in the motor control field and for the characterization of motor impairments in neurological patients. Altered modular control during a locomotion task has been often proposed as a potential quantitative metric for characterizing pathological conditions. Therefore, the purpose of this systematic review is to analyze the recent literature that used a muscle synergy analysis of neurological patients' locomotion as an indicator of motor rehabilitation therapy effectiveness, encompassing the key methodological elements to date. Searches for the relevant literature were made in Web of Science, PubMed, and Scopus. Most of the 15 full-text articles which were retrieved and included in this review identified an effect of the rehabilitation intervention on muscle synergies. However, the used experimental and methodological approaches varied across studies. Despite the scarcity of studies that investigated the effect of rehabilitation on muscle synergies, this review supports the utility of muscle synergies as a marker of the effectiveness of rehabilitative therapy and highlights the challenges and open issues that future works need to address to introduce the muscle synergies in the clinical practice and decisional process.

The organization of action: Contemporary relevance of Turvey’s approach to motor behavior

In this article, I will list the major contributions that Michael Turvey has made to the field of motor control from the perspective of my own research in the area. There are multiple research programs that I have carried out over the last twenty-five years that have all been influenced by the ecological and dynamical systems accounts that were pioneered by Michael Turvey. In this article I will highlight significant developments in 1) contribution of movement to timing and time perception 2) the contribution of the motor system in beat perception in music 3) postural sway dynamics 4) the role of detuning in bimanual coordination and finally 5) the control of unstable objects. In all these lines of work, I will specifically provide instances of how Michael Turvey’s ideas inspired subsequent research in my lab and elsewhere.

Afferents to Action: Cortical Proprioceptive Processing Assessed with Corticokinematic Coherence Specifically Relates to Gross Motor Skills.

Voluntary motor control is thought to be predicated on the ability to efficiently integrate and process somatosensory afferent information. However, current approaches in the field of motor control have not factored in objective markers of how the brain tracks incoming somatosensory information. Here, we asked whether motor performance relates to such markers obtained with an analysis of the coupling between peripheral kinematics and cortical oscillations during continuous movements, best known as corticokinematic coherence (CKC). Motor performance was evaluated by measuring both gross and fine motor skills using the Box and Blocks Test (BBT) and the Purdue Pegboard Test (PPT), respectively, and with a biomechanics measure of coordination. A total of 61 participants completed the BBT, while equipped with electroencephalography and electromyography, and the PPT. We evaluated CKC, from the signals collected during the BBT, as the coherence between movement rhythmicity and brain activity, and coordination as the cross-correlation between muscle activity. CKC at movements' first harmonic was positively associated with BBT scores (r = 0.41, p = 0.001), and alone showed no relationship with PPT scores (r = 0.07, p = 0.60), but in synergy with BBT scores, participants with lower PPT scores had higher CKC than expected based on their BBT score. Coordination was not associated with motor performance or CKC (p > 0.05). These findings demonstrate that cortical somatosensory processing in the form of strengthened brain-peripheral coupling is specifically associated with better gross motor skills and thus may be considered as a valuable addition to classical tests of proprioception acuity.

Motoneuron-driven computational muscle modelling with motor unit resolution and subject-specific musculoskeletal anatomy.

The computational simulation of human voluntary muscle contraction is possible with EMG-driven Hill-type models of whole muscles. Despite impactful applications in numerous fields, the neuromechanical information and the physiological accuracy such models provide remain limited because of multiscale simplifications that limit comprehensive description of muscle internal dynamics during contraction. We addressed this limitation by developing a novel motoneuron-driven neuromuscular model, that describes the force-generating dynamics of a population of individual motor units, each of which was described with a Hill-type actuator and controlled by a dedicated experimentally derived motoneuronal control. In forward simulation of human voluntary muscle contraction, the model transforms a vector of motoneuron spike trains decoded from high-density EMG signals into a vector of motor unit forces that sum into the predicted whole muscle force. The motoneuronal control provides comprehensive and separate descriptions of the dynamics of motor unit recruitment and discharge and decodes the subject's intention. The neuromuscular model is subject-specific, muscle-specific, includes an advanced and physiological description of motor unit activation dynamics, and is validated against an experimental muscle force. Accurate force predictions were obtained when the vector of experimental neural controls was representative of the discharge activity of the complete motor unit pool. This was achieved with large and dense grids of EMG electrodes during medium-force contractions or with computational methods that physiologically estimate the discharge activity of the motor units that were not identified experimentally. This neuromuscular model advances the state-of-the-art of neuromuscular modelling, bringing together the fields of motor control and musculoskeletal modelling, and finding applications in neuromuscular control and human-machine interfacing research.

Modeling and Simulation of the Stepping Motor Operation Curve

To improve the open-loop control performance of stepper motors, research was conducted on their operating curves. The acceleration-velocity characteristic equations of three common operating curves were established and compared with the torque frequency characteristic curves of stepper motors. Based on this, an improved segmented correction fitting S-shaped (SCFS) curve design method based on a high torque utilization sine curve was proposed to solve the problem of rotor blockage and overshoot caused by uneven acceleration during the operation of stepper motors. In the simulation of the stepper motor load motion system, the SCFS curve was compared with common curves. The simulation results showed that the position tracking error was reduced by approximately 82% compared to the trapezoidal curve and approximately 70% compared to the exponential curve. Research has found that this method is suitable for the design of high-precision motor operating curves and has a high application value in the field of precision motion control.

Mechanisms of sensorimotor adaptation in a hierarchical state feedback control model of speech.

Upon perceiving sensory errors during movements, the human sensorimotor system updates future movements to compensate for the errors, a phenomenon called sensorimotor adaptation. One component of this adaptation is thought to be driven by sensory prediction errors-discrepancies between predicted and actual sensory feedback. However, the mechanisms by which prediction errors drive adaptation remain unclear. Here, auditory prediction error-based mechanisms involved in speech auditory-motor adaptation were examined via the feedback aware control of tasks in speech (FACTS) model. Consistent with theoretical perspectives in both non-speech and speech motor control, the hierarchical architecture of FACTS relies on both the higher-level task (vocal tract constrictions) as well as lower-level articulatory state representations. Importantly, FACTS also computes sensory prediction errors as a part of its state feedback control mechanism, a well-established framework in the field of motor control. We explored potential adaptation mechanisms and found that adaptive behavior was present only when prediction errors updated the articulatory-to-task state transformation. In contrast, designs in which prediction errors updated forward sensory prediction models alone did not generate adaptation. Thus, FACTS demonstrated that 1) prediction errors can drive adaptation through task-level updates, and 2) adaptation is likely driven by updates to task-level control rather than (only) to forward predictive models. Additionally, simulating adaptation with FACTS generated a number of important hypotheses regarding previously reported phenomena such as identifying the source(s) of incomplete adaptation and driving factor(s) for changes in the second formant frequency during adaptation to the first formant perturbation. The proposed model design paves the way for a hierarchical state feedback control framework to be examined in the context of sensorimotor adaptation in both speech and non-speech effector systems.

Deep reinforcement learning-based controller for dynamic positioning of an unmanned surface vehicle

Dynamic positioning (DP) system is of great significance for the unmanned surface vehicle (USV) to achieve fully autonomous navigation. Traditional control schemes have problems such as model accuracy, parameter tuning, and complex design. In addition, although the deep reinforcement learning (DRL) is widely used in the field of vessel motion control, the learning efficiency is not high, and insufficient robustness in the face of changing environmental. In order to improve the anti-disturbance ability, robustness and convergence speed of the controller during training, a deep reinforcement learning control method based on priority experience replay (PER) is proposed for dynamic positioning of the USV. The mathematical models are established based on the kinematic and dynamic of the USV. Markov decision process (MDP) models are constructed according to the DP tasks. The simulation results show that compared with other DRL algorithms, the proposed method has higher reward value, faster convergence speed, higher control precision and smoother control output.

Motor neural networks in the leech

Leeches display robust motor patterns and exhibit a relatively simple nervous system where neurons are unambiguously identified. This brief article focuses on Hirudo verbana and summarizes how research in this organism has contributed to insights in the field of motor control, where networks have been studied from population down to individual neuron perspectives.

Nonlinear voltage error analysis and control of resonant high precision converter

The current nonlinear distortion of power converter can cause motion trajectory error and positioning error of motor, and affect the nano motion and positioning accuracy of ultra-precision system. The resonant pole-based dual buck soft-switching power converter (RPDBSPC) topology does not​ suffer current distortion caused by blanking time, and can support the high-frequency operation. It is suitable for the field of high dynamic and high-precision motion control. In order to realize the low nonlinear distortion current, the dual buck soft-switching topology should possess high linear output characteristics, and its nonlinear voltage error needs to be suppressed. This paper analyzes the operation principle of resonant power converter, and establishes its state space average model. Based on this, the nonlinear voltage error related resonant process of soft-switching topology is researched. Combined with the resonant equivalent circuit under unbalanced voltage sharing condition and the nonlinear characteristics of switching output capacitance, the expressions of nonlinear voltage error and zero voltage time are deduced, and a nonlinear voltage error suppression method based on zero voltage time control is proposed, which effectively reduces the current nonlinear distortion of resonant power converter.

Intramuscle Synergies: Their Place in the Neural Control Hierarchy.

We accept a definition of synergy introduced by Nikolai Bernstein and develop it for various actions, from those involving the whole body to those involving a single muscle. Furthermore, we use two major theoretical developments in the field of motor control-the idea of hierarchical control with spatial referent coordinates and the uncontrolled manifold hypothesis-to discuss recent studies of synergies within spaces of individual motor units (MUs) recorded within a single muscle. During the accurate finger force production tasks, MUs within hand extrinsic muscles form robust groups, with parallel scaling of the firing frequencies. The loading factors at individual MUs within each of the two main groups link them to the reciprocal and coactivation commands. Furthermore, groups are recruited in a task-specific way with gains that covary to stabilize muscle force. Such force-stabilizing synergies are seen in MUs recorded in the agonist and antagonist muscles but not in the spaces of MUs combined over the two muscles. These observations reflect inherent trade-offs between synergies at different levels of a control hierarchy. MU-based synergies do not show effects of hand dominance, whereas such effects are seen in multifinger synergies. Involuntary, reflex-based, force changes are stabilized by intramuscle synergies but not by multifinger synergies. These observations suggest that multifinger (multimuscle synergies) are based primarily on supraspinal circuitry, whereas intramuscle synergies reflect spinal circuitry. Studies of intra- and multimuscle synergies promise a powerful tool for exploring changes in spinal and supraspinal circuitry across patient populations.

Significance of Artificial Intelligence in the Production of Effective Output in Power Electronics

The power electronics (PE) industry is expected to play a significant role in the development of energy conservation and global industrialization trends of the 21st century. Due to the technological advancements that have occurred in the field, such as transportation and communication, the need for efficient and quality products is becoming more prevalent. The importance of power electronics is acknowledged in the automated industries that are constantly striving to improve their efficiency and effectiveness. Due to the increasing global energy consumption, the need for more energy-efficient technologies is also becoming more prevalent. Around 87% of our energy is derived from fossil fuels, while 6% is generated from nuclear power plants and 7% from renewable sources. Due to the increasing concerns about the environment and safety issues associated with nuclear plants and fossil fuels, the need for energy conservation is becoming more prevalent. This is also expected to be achieved through the development of power electronics. In the coming decades, the development of artificial intelligence (AI) tools, such as neural network, expert system, and fuzzy logic, is expected to bring a new era to the field of motion control and power electronics. Despite the technological advancements that have occurred in the field, these tools have not yet reached the power electronics sectors. In this paper, the AI tools and their applications in the field of power electronics and motion control are discussed.

Model Predictive Current Control With Model-Aid Extended State Observer Compensation for PMSM Drive

Model predictive current controller is a popular and effective technique to provide fast dynamic response in the field of motor control. However, conventional predictive controllers are susceptible to deteriorating control performance when model mismatch exists, such as changes in motor parameters due to the temperature variations. Therefore, this article proposes a precise model-aid extended state observer (MAESO) compensation-based real-time model predictive current controller with enhanced parameter robustness performance and high bandwidth. The predictive controller is converted into the form of multiparameter quadratic programming for online solution using numerical computational method and the constraints are linearized. In addition, the disturbances estimated by MAESO are fed back to the controller in the form of parameters for cycle-by-cycle compensation without extra controller design. Comparative simulations and experiments under different operating conditions are carried out to verify the effectiveness and superiority of the proposed method.

Innovation of EtherCAT adaptive synchronization control in embedded CNC

SummaryEtherCAT is a real‐time Ethernet protocol and has been widely used in the field of motion control owing to its high speed (100 or 1000 Mbps), low processor occupancy, and good synchronization performance in slaves. However, the master–slave synchronization method is blank in the EtherCAT protocol. The study proposes a novel master–slave synchronization method that relies on the stable sync0 of reference slave by adjusting the trigger moment of master interpolation period to settle packet loss caused by EtherCAT master–slave un‐synchronization, adaptively and dynamically. Furthermore, the proposed method improved EtherCAT to a whole new level, indicating that the EtherCAT master no longer depended on the real‐time operating system (RTOS). In addition, a synchronization predictive compensation mechanism was adopted to eliminate the compensation lag defect of existing synchronization methods. Compared with conventional studies, the synchronization method improved compensation efficiency, settled inaccurate compensation with evaluation derived from different working frequencies, and eliminated accumulative error in clock. Finally, the proposed method added almost no computation and communication load and only required eight or 16 bytes to modify the EtherCAT frame coding in the interpolation period. Experiments were carried out on machine tools to demonstrate the advantage of the proposed method in improving the synchronization performance, with an average communication jitter of only 32–71 ns.

Re-examining the integration of routine and adaptive expertise: there is no such thing as routine from a motor control perspective.

The study of adaptive expertise in health professions education has focused almost exclusively on cognitive skills, largely ignoring the processes of adaptation in the performance of precision technical skills. We present a focused review of literature to argue that repetitive practice is much less repetitive than often perceived. Our main thesis is that all skilled movement reflects components of adaptive expertise. Through an overview of perspectives from the field of motor control and learning, we emphasize the interplay between the inherent noisiness of the human motor architecture and the stability of motor skill performances. Ultimately, we challenge the very idea of routine. Our goal is threefold: to reconcile common misconceptions about the rote nature of routine precision skill performance, to offer educators principles to enhance adaptive expertise as an outcome of precision skill training, and to expand the conversation between 'routine' and 'adaptive' forms of expertise in health professions education.

NA-CPG: A robust and stable rhythm generator for robot motion control

Central pattern generators (CPGs) have been widely applied in robot motion control for the spontaneous output of coherent periodic rhythms. However, the underlying CPG network exhibits good convergence performance only within a certain range of parameter spaces, and the coupling of oscillators affects the network output accuracy in complex topological relationships. Moreover, CPGs may diverge when parameters change drastically, and the divergence is irreversible, which is catastrophic for the control of robot motion. Therefore, normalized asymmetric CPGs (NA-CPGs) that normalize the amplitude parameters of Hopf-based CPGs and add a constraint function and a frequency regulation mechanism are proposed. NA-CPGs can realize parameter decoupling, precise amplitude output, and stable and rapid convergence, as well as asymmetric output waveforms. Thus, it can effectively cope with large parameter changes to avoid network oscillations and divergence. To optimize the parameters of the NA-CPG model, a reinforcement-learning-based online optimization method is further proposed. Meanwhile, a biomimetic robotic fish is illustrated to realize the whole optimization process. Simulations demonstrated that the designed NA-CPGs exhibit stable, secure, and accurate network outputs, and the proposed optimization method effectively improves the swimming speed and reduces the lateral swing of the multijoint robotic fish by 6.7% and 41.7%, respectively. The proposed approach provides a significant improvement in CPG research and can be widely employed in the field of robot motion control.