Motion Control Research Articles

Wide Linearity Range 3D Magnetic Sensor and Angular Position Detector Based on a Single FePt Spin-Orbit Torque Device.

Three-dimensional (3D) vector magnetic sensors play a significant role in a variety of industries, especially in the automotive industry, which enables the control of precise position, angle, and rotation of motion elements. Traditional 3D magnetic sensors integrate multiple sensors with their sensing orientations along the three coordinate axes, leading to a large size and inevitable nonorthogonal misalignment. Here, we demonstrate a wide linearity range 3D magnetic sensor utilizing a single L10-FePt Hall-bar device, whose sensitivity is 291 VA-1 T-1 in the z-axis and 27 VA-1 T-1 in the in-plane axis. Based on the spin-orbit torque-dominated magnetization reversal, the linear response of anomalous Hall resistance within a large linear range (±200 Oe) for the x, y, and z components of magnetic fields has been obtained, respectively. Typically, it exhibits a relatively lower magnetic noise level of 7.9 nV at 1 Hz than previous results, improving measurement resolution at the low frequency. Furthermore, we provide a straightforward approach for noncontact angular position detection based on a single Hall-bar device, which shows great potential for application in rotational motion control.

Path planning of micromanipulators inside an SEM and 3D nanomanipulation of CNTs for nanodevice construction

The concept of a nano-laboratory inside an SEM involves performing a sequence of tasks in a continuous pattern. Robotic systems with nanoscale motion resolution facilitate in-situ manipulation and characterization of nanomaterials to assemble nanodevices inside SEMs. Precise motion control of micromanipulators is required at both macro and micro-nano scales to effectively execute multiple sequential experimental tasks in nanofabrication. However, managing the entire nanomanipulation setup is challenging due to the constricted workspace inside an SEM and the lack of proper process feedback information at that scale. This study explores the application of path planning algorithms to generate a collision-free motion path for the micromanipulators operating within the confined space of an SEM. A MATLAB-based computational tool is first developed using PRM and Dijkstra's path planning algorithms. Considering environmental constraints, the program generates an optimal motion path for the micromanipulators, facilitating automatic configuration changes within the SEM chamber. It ensures a seamless workflow and facilitates the smooth integration of additional experimental tools within the existing setup. Manipulation strategies using the nanorobotic setup are established based on the application of the developed path planning module. A pick-and-place 3D nanomanipulation technique of CNTs using cooperative control of dual micromanipulators has been demonstrated for nanodevice construction. Additionally, the electrical response of individually manipulated CNTs is recorded using a two-probe measurement technique.

Structural Design and Control Research of Multi-Segmented Biomimetic Millipede Robot

Due to their advantages of good stability, adaptability, and flexibility, multi-legged robots are increasingly important in fields such as rescue, military, and healthcare. This study focuses on the millipede, a multi-segmented organism, and designs a novel multi-segment biomimetic robot based on an in-depth investigation of the millipede’s biological characteristics and locomotion mechanisms. Key leg joints of millipede locomotion are targeted, and a mathematical model of the biomimetic robot’s leg joint structure is established for kinematic analysis. Furthermore, a central pattern generator (CPG) control strategy is studied for multi-jointed biomimetic millipede robots. Inspired by the millipede’s neural system, a simplified single-loop CPG network model is constructed, reducing the number of oscillators from 48 to 16. Experimental trials are conducted using a prototype to test walking in a wave-like gait, walking with a leg removed, and walking on complex terrain. The results demonstrate that under CPG waveform input conditions, the robot can walk stably, and the impact of a leg failure on overall locomotion is acceptable, with minimal speed loss observed when walking on complex terrain. The research on the structure and motion control algorithms of multi-jointed biomimetic robots lays a technical foundation, expanding their potential applications in exploring unknown environments, rescue missions, agriculture, and other fields.

A Dual-FSM GI LiDAR Imaging Control Method Based on Two-Dimensional Flexible Turntable Composite Axis Tracking

In this study, a tracking and pointing control system with a dual-FSM (fast steering mirror) two-dimensional flexible turntable composite axis is proposed. It is applied to the target-tracking accuracy control in a GI LiDAR (ghost imaging LiDAR) system. Ghost imaging is a multi-measurement imaging method; the dual-FSM GI LiDAR tracking and pointing imaging control system proposed in this study mainly solves the problems of the high-resolution remote sensing imaging of high-speed moving targets and various nonlinear disturbances when this technology is transformed into practical applications. Addressing the detrimental effects of nonlinear disturbances originating from internal flexible mechanisms and assorted external environmental factors on motion control’s velocity, stability, and tracking accuracy, a nonlinear active disturbance rejection control (NLADRC) method based on artificial neural networks is advanced. Additionally, to overcome the limitations imposed by receiving aperture constraints in GI LiDAR systems, a novel optical path design for the dual-FSM GI LiDAR tracking and imaging system is put forth. The implementation of the described methodologies culminated in the development of a dual-FSM GI LiDAR tracking and imaging system, which, upon thorough experimental validation, demonstrated significant improvements. Notably, it achieved an improvement in the coarse tracking accuracy from 193.29 μrad (3σ) to 87.21 μrad (3σ) and enhanced the tracking accuracy from 10.1 μrad (σ) to 1.5 μrad (σ) under specified operational parameters. Furthermore, the method notably diminished the overshoot during the target capture process from 28.85% to 12.8%, concurrently facilitating clear recognition of the target contour. This research contributes significantly to the advancement of GI LiDAR technology for practical application, showcasing the potential of the proposed control and design strategies in enhancing system performance in the face of complex disturbances.

Quantum control of classical motion: piston dynamics in a Rabi-coupled Bose-Einstein condensate

Abstract We develop a model and explore the dynamics of a hybrid classical-quantum system consisting of a classical piston and a self-interacting pseudospin 1/2 Bose-Einstein condensate with a time-dependent Rabi coupling. We investigate the mechanical work produced by the piston moving as a result of the quantum pressure of the condensate. The time-dependent Rabi field redistributes the condensate density between the spin components and, as a result, causes a time-dependent pressure acting on the piston. Correspondingly, the motion of the piston produces quantum evolution of the condensate mass- and spin density profiles. We show how by optimised design of the time-dependent direction of the Rabi field based on a quasi-stationary quantum pressure approximation, one can control both the position and velocity of the piston.

Direct torque control for PMSM based on the RBFNN surrogate model of electromagnetic torque and stator flux linkage

Direct Torque Control (DTC) is widely used in motion control of motors. Classical DTC utilizes the Park transformation(dq-model) or observer to estimate the stator fluxlinkage and electromagnetic torque. However, the dq-model and observer lack descriptions of the cogging torque and magnetic saturation in permanent magnet synchronous motors (PMSM). This paper proposes a novel method based on the surrogate model to estimate stator fluxlinkage and torque for PMSM DTC. First, a finite element model (FEM) was established based on the parameters of PMSM. Using the FEM and Latin hypercube sampling (LHS), a surrogate model for estimating the stator fluxlinkage and torque of PMSM was constructed. The stator fluxlinkage and torque surrogate model, active flux observer, and dq current model under different control methods are constructed, respectively. The servo control parameters were tuned using the extended symmetric optimum algorithm, and simulation and experiments were conducted. Finally, electromechanical coupling models with a ball screw feed system were built based on various control and feedback methods. Simulation and experiment results indicate that the surrogate model reduces torque ripple, stator fluxlinkage offset, and feed system tracking error.

Model-Predictive-Control-Based Centralized Disturbance Suppression Strategy for Distributed Drive Electric Vehicle

This paper presents a centralized disturbance suppression strategy for distributed drive electric vehicles which is based on model predictive direct motion control. This strategy is capable of addressing issues such as parameter uncertainties and external disturbances in vehicles. Firstly, the paper provides a brief introduction to model predictive direct motion control. Secondly, it analyzes the impact of vehicle parameter uncertainties and external disturbances on the mathematical model. Finally, a centralized disturbance suppression strategy based on a sliding mode observer is proposed. Simulation results demonstrate that this strategy exhibits excellent disturbance rejection capabilities.

Backstepping and Novel Sliding Mode Trajectory Tracking Controller for Wheeled Mobile Robots

A novel variable structure controller based on sliding mode is developed for addressing the trajectory tracking challenge encountered by wheeled mobile robots. Firstly, the trajectory tracking error model under the global coordinate system is established according to the kinematic model of the wheeled mobile robot. Secondly, the novel sliding mode algorithm and backstepping method are introduced to design the motion controller of the system, respectively. Different sliding mode surfaces are formulated to guarantee rapid and stable convergence of the system’s trajectory tracking error to zero. Ultimately, comparative simulation trials validate the controller’s ability to swiftly and consistently follow the reference trajectory. In contrast to traditional controllers, this controller shows rapid convergence, minimal error, and robustness.

ON-OFF Control of Marangoni Self-propulsion via A Supra-amphiphile Fuel and Switch.

Marangoni self-propulsion refers to motions of liquid or solids driven by a surface tension gradient, and has applications in soft robots/devices, cargo delivery, self-assembly etc. However, two problems remain to be addressed for motion control (e.g., ON-OFF) with conventional surfactants as Marangoni fuel: (1) limited motion lifetime due to saturated interfacial adsorption of surfactants; (2) in- situ motion stop is difficult once Marangoni flows are triggered. Instead of covalent surfactants, supra-amphiphiles with hydrophilic and hydrophobic parts linked noncovalently, hold promise to solve these problems owing to its dynamic and reversible surface activity responsively. Here, we propose a new concept of 'supra-amphiphile fuel and switch' based on the facile synthesis of disodium-4-azobenzene-amino-1,3-benzenedisulfonate (DABS) linked by a Schiff base, which has amphiphilicity for self-propulsion, hydrolyzes timely to avoid saturated adsorption and provides pH-responsive control over ON-OFF motion. The self-propulsion lifetime is extended by 50-fold with DABS and motion control is achieved. The mechanism is revealed with coupled interface chemistry involving two competitive processes of interfacial adsorption and hydrolysis of DABS based on both experiments and simulation. The concept of 'supra-amphiphile fuel and switch' provides an active solution to prolong and control Marangoni self-propulsive devices for the advance of intelligent material systems.

ON‐OFF Control of Marangoni Self‐propulsion via A Supra‐amphiphile Fuel and Switch

Marangoni self‐propulsion refers to motions of liquid or solids driven by a surface tension gradient, and has applications in soft robots/devices, cargo delivery, self‐assembly etc. However, two problems remain to be addressed for motion control (e.g., ON‐OFF) with conventional surfactants as Marangoni fuel: (1) limited motion lifetime due to saturated interfacial adsorption of surfactants; (2) in‐ situ motion stop is difficult once Marangoni flows are triggered. Instead of covalent surfactants, supra‐amphiphiles with hydrophilic and hydrophobic parts linked noncovalently, hold promise to solve these problems owing to its dynamic and reversible surface activity responsively. Here, we propose a new concept of ‘supra‐amphiphile fuel and switch’ based on the facile synthesis of disodium‐4‐azobenzene‐amino‐1,3‐benzenedisulfonate (DABS) linked by a Schiff base, which has amphiphilicity for self‐propulsion, hydrolyzes timely to avoid saturated adsorption and provides pH‐responsive control over ON‐OFF motion. The self‐propulsion lifetime is extended by 50‐fold with DABS and motion control is achieved. The mechanism is revealed with coupled interface chemistry involving two competitive processes of interfacial adsorption and hydrolysis of DABS based on both experiments and simulation. The concept of ‘supra‐amphiphile fuel and switch’ provides an active solution to prolong and control Marangoni self‐propulsive devices for the advance of intelligent material systems.

Guiding Lunar Landers: Harnessing Neural Networks for Dynamic Flight Control with Adaptive Inertia and Mass Characteristics

The autonomous control of landing procedures can provide the efficiency and precision that are vital for the successful, safe completion of space operations missions. Controlling a lander with this precision is challenging because the propellants, which will be expended during the operations, represent a significant fraction of the lander’s mass. The mass variation of each tank profoundly influences the inertia and mass characteristics as thrust is generated and complicates the precise control of the lander state. This factor is a crucial consideration in our research and methodology. The dynamics model for our lander was developed where the mass, inertia, and center of mass (COM) vary with time. A feed-forward neural network (NN) is incorporated into the dynamics to capture the time-varying inertia tensor and COM. Moreover, the propellant takes time to travel through the feed lines from the storage tanks to the engine; also, the solenoid valves require time to open and close. Therefore, there are time delays between the actuator and the engine response. To take into account these sources of variations, a combined time delay is also included in the control loop to evaluate the effect of delays by fluid and mechanisms on the performance of the controller. The time delay is estimated numerically by a Computational Fluid Dynamics (CFD) model. As part of the lander’s control mechanism, a thrust vector control (TVC) with two rotational gimbals and a reaction control system (RCS) are incorporated into the dynamics. Simple proportional, integral, and derivative (PID) controllers are designed to control the thrust, the gimbal angles of the TVC, and the torque required by the RCS to manipulate the lander’s rotation and altitude. A complex mission with several numerical examples is presented to verify the hover and rotational motion control.

A road surface reconstruction dataset for autonomous driving

Recent developments in intelligent robot systems, especially autonomous vehicles, put forward higher requirements for safety and comfort. Road conditions are crucial factors affecting the comprehensive performance of ground vehicles. Nonetheless, existing environment perception datasets for autonomous driving lack attention to road surface areas. In this paper, we introduce the road surface reconstruction dataset, providing multi-modal, high-resolution, and high-precision data collected by real-vehicle platform in diverse driving conditions. It covers common road types containing approximately 16,000 pairs of stereo images, point clouds, and ground-truth depth/disparity maps, with accurate data processing pipelines to ensure its quality. Preliminary evaluations reveal the effectiveness of our dataset and the challenge of the task, underscoring substantial opportunities of it as a valuable resource for advancing computer vision techniques. The reconstructed road structure and texture contribute to the analysis and prediction of vehicle responses for motion planning and control systems.

Control of a three-wheeled omnidirectional mobile robot via a mixed FTB/$$\mathcal H_\infty $$ approach

AbstractIn this paper, the problem of guidance and motion control of mobile robots is addressed and solved within the novel framework of the mixed finite-time/$$\mathcal H_\infty $$ H ∞ control theory of nonlinear quadratic systems (NLQSs). Starting from a NLQS describing the dynamics of omnidirectional mobile platforms, the main tasks performed for controlling in closed loop the motion of omnidirectional robots can be conveniently formulated as a mixed finite-time/$$\mathcal H_\infty $$ H ∞ control problem. A robust motion controller, which can effectively rejects disturbances deviating the robot platform from a planned path, can be designed after choosing a linear state-feedback structure for the controller. The synthesis problem is solved through some sufficient conditions contemplating both norm-bounded disturbances and sets constraining initial and terminal conditions, together with a finite-time bound on the output transient. Therefore, for all the allowable uncertainties, in presence of nonzero initial conditions and exogenous disturbance inputs which are possible within an unstructured environment, the motion control tasks can be accomplished through optimal $$\mathcal H_\infty $$ H ∞ performance by simultaneously guaranteeing that the NLQS, which governs in closed loop the robot platform, is finite-time bounded. Finally, the applicability and control performance of the design approach have been evaluated through numerical simulations.

Reference measures of lower-limb joint range of motion, muscle strength, and selective voluntary motor control of typically developing children aged 5–17 years

Background: Joint range of motion based on the neutral null method, muscle strength based on manual muscle testing, and selective voluntary motor control based on selective control assessment of the lower extremity are standard parameters of a pediatric three-dimensional clinical gait analysis. Lower-limb reference data of children are necessary to identify and quantify abnormalities, but these are limited and when present restricted to specific joints or muscles. Methods: This is the first study that encompasses the aforementioned parameters from a single group of 34 typically developing children aged 5–17 years. Left and right values were averaged for each participant, and then the mean and standard deviation calculated for the entire sample. The data set was tested for statistical significance ( p < 0.05). Results: Joint angle reference values are mostly consistent with previously published standards, although there is a large variability in the existing literature. All muscle strength distributions, except for M. quadriceps femoris, differ significantly from the maximum value of 5. The mean number of repetitions of heel-rise test is 12 ± 5. Selective voluntary motor control shows that all distributions, except for M. quadriceps femoris, differ significantly from the maximum value of 2. Conclusion: Since typically developing children do not match expectations and reference values from the available literature and clinical use, this study emphasizes the importance of normative data. Excessively high expectations lead to typically developing children being falsely underestimated and affected children being rated too low. This is of great relevance for therapists and clinicians. Level of evidence: 3.

Personalized Path-Tracking Approach Based on Reference Vector Field for Four-Wheel Driving and Steering Wire-Controlled Chassis

It is essential and forward-thinking to investigate the personalized use of four-wheel driving and steering wire-controlled unmanned chassis. This paper introduces a personalized path-tracking approach designed to adapt the vehicle’s control system to human-like characteristics, enhancing the fit and maximizing the potential of the chassis’ multi-directional driving and steering capabilities. By modifying the classic vehicle motion controller design, this approach aligns with individual driving habits, significantly improving upon traditional path-tracking control methods that rely solely on reference vector fields. First, the classic reference vector field’s logic was expanded upon, and it is shown that a personalized upgrade is feasible. Then, driving behavior data from multiple drivers were collected using a driving simulator. The fuzzy c-means clustering method was used to categorize drivers based on typical states that match vehicle path-tracking performance. Additionally, the random forest algorithm was used as the method for recognizing driving style. Subsequently, a personalized path-tracking control strategy based on the reference vector field was developed and a distributed execution architecture for four-wheel driving and steering wire-controlled unmanned chassis was established. Finally, the proposed personalized path-tracking approach was validated using a driving simulator. The results of the experimental tests demonstrated that the personalized path-tracking control approach not only fits well with various driving styles but also delivers high accuracy in driving style identification, making it highly suitable for application in four-wheel driving and steering wire-controlled chassis.

Physical Integrated Digital twin-based Interaction Mechanism of Artificial Intelligence Rehabilitation Robots Combining Visual Cognition and Motion Control

Physical Integrated Digital twin-based Interaction Mechanism of Artificial Intelligence Rehabilitation Robots Combining Visual Cognition and Motion Control

Embodied Intelligence: Bionic Robot Controller Integrating Environment Perception, Autonomous Planning, and Motion Control

Embodied Intelligence: Bionic Robot Controller Integrating Environment Perception, Autonomous Planning, and Motion Control

Free motion characteristics of the ventilated supercavitating vehicles

Supercavitation theory and technology are important methods for reducing the drag of underwater vehicles to achieve motion with high speed. A ventilated supercavitating vehicle can break the limitation of natural supercavitation on speed and ambient pressure and has broad application prospects. The presence of hydrodynamic forces only at the head and tail restricts the significant improvement of the motion performance of the vehicle. Therefore, this study improves the dynamic model of a supercavitating vehicle using a self-developed shear-layer gas loss model and simulates the vehicle’s level-free motions to reveal the motion characteristics and hydrodynamic distribution characteristics. Further, the effects of the cavitator as an important control surface on motion characteristics are analyzed by changing the cavitator diameter in numerical simulations. The results lay a theoretical foundation for designing control surfaces and motion control systems for the vehicle.

Data-driven calibration of decoupling matrix for MIMO precision motion stages

Decoupling control is a commonly employed technique for achieving high precision in multiple-input multiple-output (MIMO) motion control systems. A static decoupling matrix, which can be determined using geometric construction, is widely used due to its practicality and simplicity. However, inaccurate geometric parameters will lead to a coarse decoupling matrix, and result in interactions among the system axes and performance deterioration. To tackle this challenge, various attempts have been undertaken to calibrate the decoupling matrix. Data-driven on-line approaches have gained considerable attention for their ability to calibrate the decoupling matrix without interrupting the normal operation of the system. This paper presents a data-driven approach to calibrate the decoupling matrix for MIMO and linear time invariant (LTI) systems. Through some reasonable assumptions, a calibrated static decoupling matrix can be derived to improve the performance of the system. Moreover, considering the inevitable presence of measurement noise, the consistency of the proposed method has been analyzed. As a result, the instrument variable is introduced in the improved method to eliminate the impact of the measurement noise. Finally, the effectiveness and practicality of the proposed method are demonstrated through both numerical simulations and experiments carried out on an ultraprecision wafer stage.

Significant enhancement of optical absorption of graphene inside a metallic optical microcavity

The increase in the optical absorption of the graphene layers above the value of 2.3% of the bare layer in vacuum is of great interest for application in opto-electronics. A monolayer graphene sheet embedded in a stacked dielectric layers has been shown to reach an absorption up to 50%. Here, we theoretically show that the absorption could be significantly enhanced to greater than 50% via boosting the environmental permeability by using an empty optical cavity. This enhancement is nearly independent on the structure of the environment around the graphene sheet, except for the dimensional parameters of the cavity. The obtained results are crucial for motional control of graphene sheets by an all-optical method.