Precise Force Research Articles

The evolution of the Amber additive protein force field: History, current status, and future.

Molecular dynamics simulations are pivotal in elucidating the intricate properties of biological molecules. Nonetheless, the reliability of their outcomes hinges on the precision of the molecular force field utilized. In this perspective, we present a comprehensive review of the developmental trajectory of the Amber additive protein force field, delving into researchers' persistent quest for higher precision force fields and the prevailing challenges. We detail the parameterization process of the Amber protein force fields, emphasizing the specific improvements and retained features in each version compared to their predecessors. Furthermore, we discuss the challenges that current force fields encounter in balancing the interactions of protein-protein, protein-water, and water-water in molecular dynamics simulations, as well as potential solutions to overcome these issues.

Variable-Parameter Impedance Control of Manipulator Based on RBFNN and Gradient Descent

During the interaction process of a manipulator executing a grasping task, to ensure no damage to the object, accurate force and position control of the manipulator’s end-effector must be concurrently implemented. To address the computationally intensive nature of current hybrid force/position control methods, a variable-parameter impedance control method for manipulators, utilizing a gradient descent method and Radial Basis Function Neural Network (RBFNN), is proposed. This method employs a position-based impedance control structure that integrates iterative learning control principles with a gradient descent method to dynamically adjust impedance parameters. Firstly, a sliding mode controller is designed for position control to mitigate uncertainties, including friction and unknown perturbations within the manipulator system. Secondly, the RBFNN, known for its nonlinear fitting capabilities, is employed to identify the system throughout the iterative process. Lastly, a gradient descent method adjusts the impedance parameters iteratively. Through simulation and experimentation, the efficacy of the proposed method in achieving precise force and position control is confirmed. Compared to traditional impedance control, manual adjustment of impedance parameters is unnecessary, and the method can adapt to tasks involving objects of varying stiffness, highlighting its superiority.

Radial mechanical properties of deoxyribonucleic acid molecules

Given the small diameter of deoxyribonucleic acid (DNA), the difficulty in studying its radial mechanical properties laid in the challenge of applying a precise and controlled small force. In this work, the radial mechanical properties of DNA were measured in the AFM. DNA adhesion properties were analyzed through force-distance curves and adhesion images. The adhesion force values applied on DNA obtained from the force-distance curves were consistent with those obtained from the adhesion images. The Young's modulus of DNA was determined by collecting the data of indentation depth and the force applied on DNA and using the Hertz model for calculation. At the same compression speed, the Young's moduli increased with increasing forces, but exhibited a nonlinear growth. This reflected the complex stress-strain behavior of DNA. The impact of speeds on mechanical properties of DNA was explored. Higher speed resulted in greater Young's moduli and adhesion. This study not only deepens the understanding the mechanical properties of DNA, but also provides a strategy for investigating the mechanical properties of other thin and soft materials.

GABAergic neurons from the ventral tegmental area represent and regulate force vectors.

The ventral tegmental area (VTA), a midbrain region associated with motivated behaviors, consists predominantly of dopaminergic (DA) neurons and GABAergic (GABA) neurons. Previous work has suggested that VTA GABA neurons provide a reward prediction, which is used in computing a reward prediction error. In this study, using in vivo electrophysiology and continuous quantification of force exertion in head-fixed mice, we discovered distinct populations of VTA GABA neurons that exhibited precise force tuning independently of learning, reward prediction, and outcome valence. Their activity usually preceded force exertion, and selective optogenetic manipulations of these neurons systematically modulated force exertion without influencing reward prediction. Together, these findings show that VTA GABA neurons continuously regulate force vectors during motivated behavior.

Small-scale magnetic soft robotic catheter for in-situ biomechanical force sensing

Miniaturized magnetic soft robotic catheters offer significant potential in minimally invasive surgery by enabling remote active steering and reduced radiation exposure. However, existing magnetic catheters are limited by the absence of in-situ biomechanical force sensing, which is crucial for controlling the contact force exerted on surrounding tissues during surgical procedures. Here, we report an in-situ force sensing strategy for small-scale magnetic robotic catheters. A coaxial integration of ring-shaped permanent and fibre-based force sensors at the catheter's distal end enables both active steering and precise force measurement. The force sensor is designed to be sensitive exclusively to contact forces perpendicular to its plane, achieving a sensitivity of 0.69 nm/kPa (or 0.38 nm/mN). By manipulating magnetic field patterns, the catheter can actively generate and control contact forces to tissues, using real-time feedback from the force sensor. We demonstrate the system's force-sensing and force-control capability in isolated organs and tissue phantom during passage, verifying the catheter's high force sensitivity and high steerability. The feedback-loop force control enhances procedural safety and efficacy for minimally invasive surgery, making it especially suitable for procedures such as transbronchial microwave ablation of lung nodules and cardiac ablation for atrial fibrillation.

Light‐Manipulated Millimeter‐Scale Swimming Robot for Precise Navigation

AbstractMicro‐swimming robots have been increasingly emphasized in the fields of targeted drug delivery and water surface cleaning, which mainly use open external structures or external environments to assist navigation, but there are still salient problems, such as the difficulty of stable capture and poor navigation accuracy. Theoretically, a light‐driven swimming robot can have a symmetrical embedded structure to constrain surface tension gradient forces and thermophoretic forces in a wide range of directions and convert them into precise directional forces. Therefore, a sustained‐capture swimming robot is developed with precise navigation consisting of TiO2/polypyrrole (PPy) composites containing a symmetric inner truncated‐cone hole structure and realized multiple controllable motion patterns. Excitingly, the capture motion of the TiO2/PPy inner truncated‐cone hole structure robot “TPHR” can be accurately controlled by programmable automation, with capture travel speeds of up to ≈23.64 mm s−1. Its excellent kinematic performance stems from the high photothermal conversion efficiency (≈47.83%) of the composite and the synergistic effect between thermo‐capillary and thermo‐convective flows. Furthermore, a patterning precision movement, water surface oil‐cleaning microstructure assembly, etc. are realized, reflecting the engineering application value of TPHR. This structure provides a novel strategy for the development of micro‐swimming robots with stable capture and sustainable motion.

A Conceptual Design of Deployable Antenna Mechanisms

Over the last decade, large-scale antennas have been developed to enhance precise blue force tracking and improve situational awareness. In general, such large-scale antennas, ranging from 1 to up to 10 m, need a specific mechanism that can reconfigure their shapes and morphologies, resulting in stowing and deploying upon the given environment. In parallel, it must be noted that such deployable mechanisms should accommodate a large aperture diameter while ensuring they are lightweight, robust, and structurally rigid to avoid undesired deformations due to the deployment. With these in mind, this work presents a large frustum-shaped deployable antenna mechanism with a large aperture diameter of 7.5 m. The deployable mechanism is composed of hierarchical bayes the radial direction at 30° intervals. Twelve bayes in total identify the overall morphology of the deployable antenna, which features a dodecagon. Specifically, the bay is composed of three linkage structures: a six-bar linkage mechanism, a V-folding mechanism, and a single pantograph mechanism. As a result of static and dynamic simulations, it is identified that the mechanism achieves an area-to-mass ratio of 5.003 m2/kg and a safety factor of 323.8 upon deployment. Conclusively, this work demonstrates a strong potential of the deployable antenna mechanism, providing high rigidity and large aperture diameter while ensuring high stability in space environments.

ADVANCES IN BONE REMODELING AND CLEAR ALIGNERS IN ORTHODONTICS

The use of clear aligners in orthodontics represents a significant advancement in dental treatment, allowing effective tooth movement with a less invasive approach compared to traditional fixed appliances. Clear aligners not only facilitate the alignment of teeth but also promote changes in the supporting alveolar bone. The success of these movements relies on the surrounding bone's response to mechanical stress during treatment, involving processes of bone resorption and deposition. Research has demonstrated that advancements in digital technology, such as 3D modeling and artificial intelligence, enhance the planning of tooth movements, making bone remodeling more predictable. Sophisticated algorithms simulate aligner forces on the alveolar bone, optimizing each treatment phase. However, limitations persist in complex cases where specific movements, like extrusion and rotation, require precise force control. Recent studies by Guo et al. (2023) and Abogabal et al. (2023) evaluated the effects of clear aligners on alveolar bone dimensions during orthodontic treatment, finding a greater occurrence of bone defects after treatment. They highlighted the differences in bone height changes between aligners and fixed appliances, indicating that clear aligners might have less impact on bone height. Innovative approaches such as low-intensity pulsed ultrasound (LIPUS) and high-frequency vibration (HFV) have also been investigated for their potential to enhance treatment outcomes. Research indicates that daily vibration may reduce the time required for dental correction while increasing levels of cytokines and markers for bone remodeling. Overall, the integration of digital tools and innovative techniques in orthodontics is crucial for improving treatment efficacy, addressing challenges in complex cases, and ultimately enhancing patient experience and satisfaction.

Design and application of inertia switch with the zero-time trigger sensor for projectile overload impulse

The paper introduces a novel inertial trigger sensor featuring a multi-layer stacked single-arm brass beam pendulum, aimed at applications in automotive and aerospace fields, etc. It employs the Lagrangian equation for dynamic modeling and finite element analysis to achieve precise inertia force measurements. The design incorporates advanced fabrication techniques and a high-precision latching mechanism that combines circuit self-locking with mechanical triggering. Validated through simulations and testing, The results of the live ammunition tests indicate that, apart from failures of the Inertia Switch due to errors in the manufacturing process, all other inertial switches functioned normally. This demonstrates that the inertial sensor provides an adjustable trigger time of 0–5 ms and reliably withstands extreme overloads of up to 15000 g. This work significantly enhances the performance of inertial sensors in high-stress environments.

Research on Direct-Drive Brake Actuator and Its Control Method for Electric Vehicles

<div>With the rapid development of electric vehicles, the need for improved reliability and safety performance of electric vehicle braking systems has become paramount. In response to this demand, a novel direct-drive brake-by-wire actuator based on linear motors was designed to address these challenges. This article presents the structure and principles of the proposed braking actuator. Leveraging the traditional electromechanical brake systems as a foundation, the prototype was modified and fabricated. Additionally, the control and drive system for the braking actuator was established using the TMS320F28335 digital signal processor. Moreover, the current-position dual closed-loop control algorithm was devised to regulate the braking force accurately. Experimental results demonstrate that the direct-drive brake-by-wire actuator exhibits rapid responsiveness and precise braking force modulation, showcasing promising prospects in the field of electric vehicle braking.</div>

Improved adaptive robust control of direct-drive pump-valve cooperative brake-by-wire unit with disturbance compensation

Abstract Aiming at the needs of high-level autonomous driving, a direct-drive pump-valve coordinated brake-by-wire unit was designed. To improve the response speed, control accuracy, and robustness of the brake-by-wire unit, an improved adaptive integral robust control method (AIRC-AD) based on a novel adaptive law and disturbance compensation was proposed. By integrating pressure tracking error information and overall parameter estimation error information, a novel parameter adaptive law with fast convergence speed was designed. A finite-time disturbance observer was designed to observe the overall system disturbance, improving the system’s anti-disturbance performance. Finally, the stability of the control method was proven using the Lyapunov stability proof method. The results show that under step target signals, sinusoidal target signals and triangular wave target signals, AIRC-AD has better response speed and control accuracy. In conditions such as increased permanent magnet temperature, increased coil resistance, or reduced flow area due to hydraulic pipeline blockage, AIRC-AD has better adaptability. The unit and the method can meet the demand for precise braking force control in high-level autonomous driving.

Digital Models and 3D Biomechanics Analysis in Orthodontics. Part 1: Vector Calculations

Biomechanics is essential for optimizing orthodontic appliances and controlling dental movement. Charles J. Burstone pioneered a three-dimensional (3D) approach in orthodontics, advocating for a shift beyond appliance-focused methods. Initially, biomechanics studies were constrained to two-dimensional (2D) analysis due to the complexities of 3D evaluation. Despite progress in computational tools and digital modeling, orthodontic biomechanics has largely maintained a 2D orientation. This paper advances orthodontic biomechanics into 3D, re-evaluating concepts previously limited to 2D frameworks. A dedicated software, DDP-Ortho (Ortolab, Poland), is introduced to enable orthodontists to analyze and resolve biomechanical challenges in 3D, facilitating appliance designs with precise 3D force systems. The representation and calculation of force vectors and moments in 3D are detailed, emphasizing the inherent complexity absent computational support. Key processes such as vector subtraction and addition, fundamental for assessing and refining orthodontic force systems, are explained. Additionally, the vector split (couple replacement) method, previously described in 2D, is extended to 3D, addressing the unique constraints and challenges of this approach. These tools promise to refine the accuracy and effectiveness of orthodontic treatments, setting the stage to examine the interactions between 3D force systems and dental movement, which will be addressed in a subsequent paper, to broaden the potential of contemporary orthodontic therapy.

Accurate simulations of moving flexible objects with an improved immersed boundary-lattice Boltzmann method

Accurately capturing boundaries is crucial for simulating fluid–structure interaction (FSI) problems involving flexible objects undergoing large deformations. This paper presents a coupling of the immersed boundary-lattice Boltzmann method with a node-based partly smoothed point interpolation method (NPS-PIM) to enhance the accuracy of simulating moving flexible bodies in FSI problems. The proposed method integrates a multiple relaxation time scheme and employs a force correction technique to address boundary capturing inaccuracies. The effect of virtual fluid is accounted for through a Lagrangian point approximation, ensuring precise FSI force calculations for unsteady solid motions. NPS-PIM is utilized as the solid solver, constructing a moderately softened model stiffness by combining the finite element method (FEM) with the node-based smoothed PIM (NS-PIM). Simulations of flow fields near flexible objects with large deformations demonstrate that the proposed approach reduces numerical errors, improves computational efficiency compared to traditional FSI models using FEM and NS-PIM, and accurately captures the behavior of moving flexible bodies and detailed flow fields.

Working memory load increases movement-related alpha and beta desynchronization

Working memory (WM) load has been well-documented to impair selective attention and inhibitory control. However, its effects on motor function remain insufficiently explored. To extend the existing literature, we investigated the impact of WM load on force control and movement-related brain activity. Sixteen healthy young participants performed a visual static force matching task using a pinch grip under varying WM loads. The task included low and high WM load conditions (memorizing one digit or six digits), and the precision level required to control force was adjusted by manipulating visual gain (low vs. high visual gains), with higher visual gain necessitating more precise force control. Peri-movement alpha and beta event-related desynchronization (ERD), along with force accuracy and steadiness, were measured using electroencephalography recorded over the central areas during the force control task. Results indicated that while force accuracy and steadiness significantly improved with higher visual gain, there was no significant effect of WM load on these measures. Alpha and beta ERD were greater under high than low visual gain, and also greater under high than low WM load. These findings suggest that in young adults, increased WM load leads to compensatory increases in sensorimotor cortical activity to mitigate potential declines in static force control performance that may result from the depletion of neural resources caused by WM load. Our findings extend current understanding of the interaction between WM and sensorimotor processes by offering new insights into how movement-related brain activity is influenced by heightened WM load.

Dynamic modeling method for constrained system with singular mass matrices

The dynamic model is beneficial for system control design, especially when it is related to precise force adjustment. Traditional modeling methods make it difficult to address multi-body systems with singular mass matrices or are computationally expensive. In this paper, an approach termed the Extended Rosenberg Embedding Method for dynamic modeling is presented. By incorporating the constraints directly into the Fundamental Equation, the proposed approach enables the description of the system motion in two separate equations, which can reduce the computational cost of the constrained dynamic model. This method provides a new way to establish motion equations, regardless of whether the system is subject to holonomic or non-holonomic constraints. Moreover, as the method does not impose direct requirements on the rank of the mass matrix, it is capable of handling the modeling of multi-body systems with singular mass matrices. The validity of the proposed method is substantiated through rigorous mathematical derivation, while its accuracy and computational efficiency are corroborated through the examination of two numerical examples.

Viscoelastic modeling based force control framework for enhancing operation safety of soft tissue for orthopedic surgical robots

In robot-assisted laminectomy, precise control of touch force on the spinal cord during the dissection of ossified ligaments is critical to prevent dura mater tearing and ensure surgical safety. However, the complex viscoelastic characteristics of tissue at the lesion site pose a significant challenge for accurately applying touch force. This paper proposes a safe touch force servo framework based on tissue model identification and preoperative optimization of the force controller. First, a soft tissue model is established using elastic and viscoelastic elements, and the transfer function from tissue deformation to touch force is derived. Subsequently, the path and parameters of a safe pre-touch are designed, and force information required for tissue model identification is obtained through the pre-touch experiment. The model order and specific parameter values are then identified using a differential evolution algorithm. Next, force control simulation is conducted based on the identified model, and a specifically designed loss function is introduced to achieve preoperative tuning of the force controller. Finally, step force control experiments on sheep spines validate the effectiveness of the proposed method. This paper provides a safe and available touch motion control framework that can be expandable to impose precise force on various vulnerable soft tissues, with the potential for widespread application.

Bending/force switching control of pneumatic soft actuator based on multi-sensor fusion

Abstract The soft actuator has unlimited freedom, and can realize the grasping of irregular or fragile objects. A pneumatic soft actuator is designed in this paper, which consists of a series of symmetrically distributed pneumatic chambers. The soft actuator consists of a top expansion layer composed of 20 chambers and a bottom restraint layer. The Yeoh model and the finite element method (FEM) were employed to analyze the bending of the soft actuator. To improve the tracking performance, a PID strategy of the endocrine system (ES-PID) based on the neuroendocrine thyroid hormone regulation mechanism was constructed to tune the bending deformation. To achieve steady and precise force control, a bending/force switching control algorithm based on multi-sensor fusion is designed to realize high control accuracy. The bending/force switching control strategy mainly consists of two feedback loops for bending control, a force control, and a switching control. In the experimental process, the bending control is the primary control, and the force control is the primary control when the switching condition is satisfied. In order to prevent the occurrence of a false switching action, a switching factor is constructed using the method of successive comparison of neighboring values. Finally, several experiments are carried out to verify the effectiveness and feasibility of the control algorithm.

Prediction of feed force with machine learning algorithms in boring of AISI P20 plastic mold steel

Feed force plays a critical role in machining performance, including efficiency, hole quality, energy consumption, and tool life. While feed force is typically measured using dynamometers, these devices are often impractical in real-world experiments due to their complexity and cost. In particular, boring operations, which are essential for enlarging holes within tight tolerances, depend on precise feed force control to maintain high-quality hole outcomes. Achieving this precision requires reliable pre-process feed force estimation. However, traditional analytical methods for calculating feed force in boring operations often fall short, largely due to factors such as in-hole dynamics, the inclination of the rake surface, and the slenderness of the boring bar. This study aims to apply single and hybrid machine learning methods to accurately model and predict feed force in the boring process. Unlike previous research, which focused on feature selection, we propose the use of all possible combinations of input feature subsets to determine the optimal input features. The results indicate that feed force can be predicted effectively using these optimized feature combinations. Such accurate feed force prediction is crucial for effective process optimization, material selection, and process planning.

Force control for the robot-assisted laparoscopic ultrasound scanning system

Laparoscopic ultrasound scanning, an acritical diagnostic imaging method for liver diseases, necessitates precise contact force control that is typically dependent on skilled physicians. Given its high mobility and controllability, the robot is anticipated to supplant physicians in performing laparoscopic ultrasound scans. Consequently, this study introduces a laparoscopic ultrasound scanning system paired with a learning-based force controller. Compared with traditional methods, our approach offers precise control over the contact force between the ultrasound probe and human organ surfaces during scanning, reducing the learning curve for surgeons in laparoscopic liver ultrasound scanning. Acknowledging the complex nonlinear mechanical model involved when the laparoscopic ultrasound probe contacts human organs and tissues, we propose a learning-based method utilizing a long short-term memory network to estimate the contact force. This method allows for the precise estimation of contact force with human organ tissues by analyzing tendon force. The results indicate that the proposed LSTM neural network can accurately fit the mechanical model of the robotic catheter, with a root mean square error of 3.44 mN. Finally, we conducted a force control experiment for ultrasound scanning on a silicone liver model. The results indicate that the proposed method can achieve relatively stable force control for laparoscopic ultrasound scanning, with a maximum error of 2.41 mN during the transient phase of system control and a maximum absolute error of 1.8 mN in the steady state.

Novel bio-inspired soft actuators for upper-limb exoskeletons: design, fabrication and feasibility study.

Soft robots have been increasingly utilized as sophisticated tools in physical rehabilitation, particularly for assisting patients with neuromotor impairments. However, many soft robotics for rehabilitation applications are characterized by limitations such as slow response times, restricted range of motion, and low output force. There are also limited studies on the precise position and force control of wearable soft actuators. Furthermore, not many studies articulate how bellow-structured actuator designs quantitatively contribute to the robots' capability. This study introduces a paradigm of upper limb soft actuator design. This paradigm comprises two actuators: the Lobster-Inspired Silicone Pneumatic Robot (LISPER) for the elbow and the Scallop-Shaped Pneumatic Robot (SCASPER) for the shoulder. LISPER is characterized by higher bandwidth, increased output force/torque, and high linearity. SCASPER is characterized by high output force/torque and simplified fabrication processes. Comprehensive analytical models that describe the relationship between pressure, bending angles, and output force for both actuators were presented so the geometric configuration of the actuators can be set to modify the range of motion and output forces. The preliminary test on a dummy arm is conducted to test the capability of the actuators.