Motion Control Research Articles

Motion Planning and Control of a Morphing Quadrotor in Restricted Scenarios

Motion Planning and Control of a Morphing Quadrotor in Restricted Scenarios

Speed control of PM brushless DC motor using sensorless hybrid controller

In most industrial processes, rising productivity necessitates increased demand on electrical motors in order to minimize costs and improve drive system efficiency. The sensorless technique is preferred. Brushless DC (BLDC) motors compete with a wide range of different motor types in the motion control industry. The nonlinearity of BLDC motor characteristics is challenging to manage with a traditional proportional integral derivative (PID) controller. The PID controller's fuzzy logic allowed it to tune itself while operating online. Hybrid approaches outperform stand-alone algorithms because they can overcome their weaknesses without losing their advantage. The purpose of this study is to present a fuzzy PI+D controller that is simulated over a wide range of reference speeds, loads, and parameter variations. This paper presents a model of a sensorless BLDC motor with a speed controller. The responses of the rotor speed, electromagnetic torque, stator back electromotive force (EMF), and stator currents are effectively monitored. The findings from the simulation indicate that the hybrid controller presented in this study exhibits resilience to rapid load torque and parameter fluctuation. Furthermore, it demonstrates superior dynamic performance and exhibits notable enhancements in speed tracking and system stability. The performance of the system is enhanced through the utilization of the proposed hybrid intelligent controller.

Smooth Motion Control Policy of Wall-Pressing Pipeline Robots

Smooth Motion Control Policy of Wall-Pressing Pipeline Robots

Development of multi-channel magnetic flux leakage testing system for bridge cables

Magnetic flux leakage (MFL) is one of the most popular techniques for detecting broken wire in bridge cables. MFL sensor array has attracted a lot of attention in recent years due to its capacity to measure the spatial distribution of the magnetic field of bridge cable. More sensors in the array lead to increased useful information extraction from the MFL field, allowing for more precise quantitative evaluation of bridge cable damage. This study reports the development of a cable MFL testing system capable of synchronous acquiring up to 512 channels of MFL sensors signal. Radial sensor arrays (RSA) composed of vertically placed magnetic sensors spaced at equal intervals are developed to be arranged around the bridge cable. They record the spatial distribution of the magnetic leakage field of the broken wire by collecting the MFL signal at various lift-offs. To solve the problem of multi- channel MFL signal synchronous acquisition, this study develops a MFL signal acquisition card based on multiplexing technology, capable of synchronous acquisition of up to 512 channels of MFL signal. Multiplexers are used to switch sensor channels, followed by multi-channel A/D conversion chips to convert the transmitted data. The converted data are temporally stored in the First-In-First-Out (FIFO) buffer of the Field Programmable Gate Array (FPGA) and transferred to the computer through the network port for display, storage, and signal processing. Finally, the motion control circuit, mechanical structure, and control software of the bridge cable MFL testing system are fully customized. Experiment results reveal that the system can be used in multi-channel MFL testing, laying the foundation for further multi-channel signal processing and bridge cable health evaluation.

Innovative High Degree of Freedom Single-Multipoint Incremental Forming System for Manufacturing Curved Thin-Walled Components

This study proposed a new design and control framework for a novel high degree of freedom single-multipoint incremental forming (HDoF-SMIF) system to manufacture curved thin-walled components. First, a novel HDoF-SMIF system was designed based on the principles of HDoF-SMIF technology (including the description of the mechanical structure and network topology). Following that, to realize the process control of the HDoF-SMIF process, this study introduced a novel control strategy for the tool deflection angle specific to robotic incremental forming. It also developed a synergistic motion control approach for the lifting platform and an accelerated computation method to determine the forming parameters for a multipoint reconfigurable die. Then, a novel HDoF-SMIF principle prototype and control program were built for the designed HDoF-SMIF system structure and control method. Finally, the feasibility of the HDoF-SMIF system was verified with the forming test on the domed component, and the geometrical accuracy and wall thickness distribution of the samples were measured using a 3D optical scanner system to evaluate the forming quality of the established HDoF-SMIF system. The results showed that compared to the conventional single-point incremental forming (SPIF) process, the developed HDoF-SMIF system can provide a more uniform thickness distribution of the formed part with a lower overall thickness thinning rate. Furthermore, by increasing the tool deflection angle with a 1mm elastomeric interpolator, the overall part's forming accuracy and surface quality can be effectively improved.

Motion Control of Magnetic-Controlled Spiral Microrobots for In-vitro Plaque Removal

Motion Control of Magnetic-Controlled Spiral Microrobots for <i>In-vitro</i> Plaque Removal

Helping Hands for Handicaps

Abstract: We have more advanced wheelchairs in today's generation. it includes joystick wheelchair, head motion control wheelchair, gesture control wheelchair etc. But with the advance technology it is expensive too, which is not affordable for most of the middle-class families. In order to provide an advanced wheelchair in an affordable price, we have to use less components to reduce the cost so that a person can buy it with a price less than the today’s basic bikes price. To achieve this, we used some basic electronic components such as Arduino MINI and NANO, gyro sensor, Node MCU, Motors etc. To get extra security we integrated some calling and GPS location features too. The person sitting in the wheelchair can control the wheelchair through hand gestures. Additionally, whenever the person sitting in the wheelchair faces any problem, such as if the wheelchair falls, the caretaker will receive a notification and through the GPS location, the caretaker will be able to reach their location.

Indirect method for solving non-linear optimal control of a non-rectilinear motion of a rocket with variable mass

In this paper, an optimal trajectory of the rocket angle with a variable mass will be calculated by considering the aerodynamic forces, the acceleration of gravity and moves with a non-rectilinear motion from a initial state to a final state with a known altitude. The aim is to optimize the lateral offset of the rocket. For this, we formulate an optimal control problem where the rocket angle is the control. In order to solve the problem, let applied Shooting method's based on the Pontryagin's maximum principle, and study the precision and a duration time. Finally, we validate the results by using MATLAB software.

Whole Body Control of Mobile Manipulators With Series Elastic Actuators for Cart Pushing Tasks.

Human-centered environments provide affordance for the use of two-handed mobile manipulators. Yet robots designed to function in and physically interact with such environments are not yet capable of meeting human users' requirements. This work proposes a whole body control framework of a two-handed mobile manipulator driven by series elastic actuators (SEAs) for cart pushing tasks. A whole body dynamic model for an integrated mobile platform and on-board arms is revealed, which takes into account the interaction forces with the cart. Then, the explicit force/position control of the mobile manipulator is performed. It enables the robot to interact dynamically with the environment while providing motion, i.e., the manipulators provide both output force control and motion control for pushing a cart. To cope with the highly nonlinear system dynamics and parameter variation of a SEA-driven mobile manipulator, this work proposes an adaptive robust controller based on a novel integral barrier Lyapunov function for cart pushing tasks by considering model uncertainty. The proposed controller enables the mobile manipulator to complete cart pushing tasks by regulating the position and output force of the mobile base and arms. The experimental results show the effectiveness of this approach in cart pushing tasks.

Information-Theoretic Bayesian Inference for Multi-Agent Localization and Tracking of an RF Target with Unknown Waveform

Abstract Information-theoretic waypoint planning}and machine learning through Bayesian inference are exploited to track and localize a dynamically-moving radio-frequency (RF) emitter with unknown waveform (uncooperative target). The target-state estimator handles non-Gaussian distributions while mutual information is exploited to coordinate the motion control of a network of mobile sensors (agents) to minimize measurement uncertainty. The mutual information is computed for pairs of sensors through a four-permutation with replacement process. The information surfaces are fused to create a composite map, which is utilized by agents to plan their motion for more efficient and effective target estimation and tracking. Simulations and physical experiments involving micro-aerial vehicles with time difference of arrival (TDOA) measurements are performed to evaluate the performance of the algorithm. Results show that when two or three agents are used, the algorithm outperforms state-of-the-art methods. Results also show that for four or more agents, the performance is as competitive as an idealized static sensor network.

A Light-powered single-stranded DNA molecular motor with colour-selective single-step control.

To-down control of small motion is possible through top-down controlled molecular motors in replacement of larger actuators like MEMS or NEMS (micro- or nano-electromechanical systems) in the current precision technology. Improving top-down control of molecular motors to every single step is desirable for this purpose, and also for synchronization of motor actions for amplified effects. Here we report a designed single-stranded DNA molecular motor powered by alternated ultraviolet and visible light for processive track-walking, with the two light colours each locking the motor in a full directional step to allow saturated driving but no overstepping. This novel nano-optomechanical driving mechanism pushes the top-down control of molecular motors down to every single step, thus providing a key technical capability to advance the molecular motor-based precision technology and also motor synchronization for amplified effects.

A Light‐powered single‐stranded DNA molecular motor with colour‐selective single‐step control

To‐down control of small motion is possible through top‐down controlled molecular motors in replacement of larger actuators like MEMS or NEMS (micro‐ or nano‐electromechanical systems) in the current precision technology. Improving top‐down control of molecular motors to every single step is desirable for this purpose, and also for synchronization of motor actions for amplified effects. Here we report a designed single‐stranded DNA molecular motor powered by alternated ultraviolet and visible light for processive track‐walking, with the two light colours each locking the motor in a full directional step to allow saturated driving but no overstepping. This novel nano‐optomechanical driving mechanism pushes the top‐down control of molecular motors down to every single step, thus providing a key technical capability to advance the molecular motor‐based precision technology and also motor synchronization for amplified effects.

Research on 2D Animation Simulation Based on Artificial Intelligence and Biomechanical Modeling

Animation techniques have been completely transformed by the union of Artificial Intelligence (AI) and biomechanical modeling, particularly in 2D animation. This study looks at a combination of AI and biomechanics to address the challenges of simulating 2D animation. Current approaches in 2D animation often struggle to achieve lifelike and fluid movements, especially when representing complex motion or interaction. These traditional techniques rely on manual keyframing or physics simulation, which may be time-consuming and do not provide the rich detail needed for realism in animations. To meet these aspects, this study suggested 2D animation using Artificial Intelligence with Biomechanical Modeling (2D-AI-BM). Our approach thus harnesses Deep Neural Network (DNN) for moving forecasts and improvement using biopsychological principles to help us imitate natural human actions better. In addition to character animation, it could apply to interactive storytelling and educational simulations. As a result, animators get more control over motion generation while drastically reducing the necessity for manual intervention through this fusion of AI and biomechanics, which smoothens the production pipeline for animations. This paper considers several important metrics to evaluate the proposed approach’s effectiveness, including user satisfaction, computational efficiency, motion smoothness and realism. Comparative studies with classical animation methods showed that the method generates realistic movements on 2D characters while saving time during production. The numerical findings exemplify that the recommended 2D-AI-BM model improves an accuracy rate of 97.4%, computational efficiency ratio of 96.3%, motion control ratio of 95.4%, pose detection ratio of 94.8% and scalability ratio of 93.2% compared to other popular techniques.

Research on sensing recognition principle and motion control of industrial robot

Research on sensing recognition principle and motion control of industrial robot

Development of Sensory Virtual Reality Interface Using EMG Signal-Based Grip Strength Reflection System

In virtual reality (VR), a factor that can maximize user immersion is the development of an intuitive and sensory interaction method. Physical devices such as controllers or data gloves of existing VR devices are used to control the movement intentions of the user, but their shortfall is that grip strength and detailed muscle strength cannot be reflected. Therefore, this study intended to establish a more sensory VR environment compared to existing methods by reflecting the grip strength of the flexor digitorum profundus of the user of the VR content. In this experiment, the muscle activity of the flexor digitorum profundus was obtained from six subjects based on surface electromyography, and four objects with differing intensity were created within a VR program in which the objects were made to be destroyed depending on muscle activity. As a result, satisfaction was improved because the users could sensitively interact with the objects inside the VR environment, and the intended motion control of the user was reflected in the VR content.

An adaptive discretized RNN algorithm for posture collaboration motion control of constrained dual-arm robots

An adaptive discretized RNN algorithm for posture collaboration motion control of constrained dual-arm robots

Pose detection and docking control for autonomous dynamic docking mechanism with non-cooperative targets

Purpose This study aims to address the issue of random movement and non coordination between docking mechanisms and locking mechanisms, and proposes a comprehensive dynamic docking control architecture that integrates perception, planning, and motion control. Design/methodology/approach Firstly, the proposed dynamic docking control architecture uses laser sensors and a charge-coupled device camera to perceive the pose of the target. The sensor data are mapped to a high-dimensional potential field space and fused to reduce interference caused by detection noise. Next, a new potential function based on multi-dimensional space is developed for docking path planning, which enables the docking mechanism based on Stewart platform to rapidly converge to the target axis of the locking mechanism, which improves the adaptability and terminal docking accuracy of the docking state. Finally, to achieve precise tracking and flexible docking in the final stage, the system combines a self-impedance controller and an impedance control algorithm based on the planned trajectory. Findings Extensive simulations and experiments have been conducted to validate the effectiveness of the dynamic docking system and its control architecture. The results indicate that even if the target moves randomly, the system can successfully achieve accurate, stable and flexible dynamic docking. Originality/value This research can provide technical guidance and reference for docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

Research on the Positioning Method of Steel Belt Anchor Holes Applied in Coal Mine Underground

In order to improve the automation and safety of underground steel belt support in coal mines, a method for the intelligent identification and positioning of steel belt anchor holes in roadway support using inductive sensors is proposed. Using STM32F407ZGT6 as the main control chip, tasks such as data acquisition and processing, motor motion control, etc., are assigned based on the real-time operating system FreeRTOS. Using the XY mobile platform equipped with inductive sensors to detect steel belts, The collected data includes coordinate values and voltage values. Adaptive threshold generation and correction strategies are used for threshold segmentation and extraction of anchor hole boundary points. The principle of Hough circle transformation is used to fit the extracted boundary points into circles. The results show that this method can perform anchor hole positioning with a positioning error of within 5 mm, meeting the design requirements.

Autonomous Alignment and Docking Control for a Self-Reconfigurable Modular Mobile Robotic System

This paper presents the path planning and motion control of a self-reconfigurable mobile robot system, focusing on module-to-module autonomous docking and alignment tasks. STORM, which stands for Self-configurable and Transformable Omni-Directional Robotic Modules, features a unique mode-switching ability and novel docking mechanism design. This enables the modules that make up STORM to dock with each other and form a variety configurations in or to perform a large array of tasks. The path planning and motion control presented here consists of two parallel schemes. A Lyapunov function-based precision controller is proposed to align the target docking mechanisms in a small range of the target position. Then, an optimization-based path planning algorithm is proposed to help find the fastest path and determine when to switch its locomotion mode in a much larger range. Both numerical simulations and real-world experiments were carried out to validate these proposed controllers.

An adaptive PID controller for path following of autonomous underwater vehicle based on Soft Actor–Critic

In recent years, autonomous underwater vehicles (AUVs) have witnessed rapid development, and its motion control has garnered increasing attention. Meanwhile, in industries, PID controllers are still wildly employed by most AUVs due to their simplicity, ease of deployment, and a certain level of robustness. However, they are facing significant challenges in parameter tuning, especially when dealing with various control missions and changing external environments. Deep reinforcement learning, as a data-driven approach, has gradually made its impact in AUV control. However, its lack of interpretability has hindered its deployment in relevant experiments. To address these issues, this paper proposed an adaptive PID controller for path following of AUVs based on the Soft Actor–Critic (SAC). This controller combines the interpretability of PID with the intelligence of reinforcement learning. A simulation platform was established and compared with other typical control methods, demonstrating the superiority of the proposed controller. Finally, the feasibility of the proposed SAC-PID controller was validated by lake trials. The results showed that the SAC-PID controller significantly outperformed the PID and Proximal Policy Optimization (PPO) PID controllers in multiple dimensions, such as control precision and convergence speed.