Precise Control Performance Research Articles

Metal-Organic-Framework-Derived CuO-ZnO@CN Hollow Nanoreactors: Precise Structural Control and Efficient Catalytic Performance.

Hollow carbon-nitrogen nanoreactors constitute a class of porous materials that have widespread application owing to their large inner cavities, low densities, core-shell interfaces, and enrichment effects. Direct carbonization of precursors is the simplest and most economical method to prepare porous carbon-nitrogen materials; however, this method requires high temperatures, thus yielding nonoxide structures. In this study, CuO-ZnO@CN (CN: carbon-nitrogen layers) is prepared using the two-step heating of zeolitic imidazolium skeleton-8 (ZIF-8) coated with CuO-ZnO precursors. During carbonization, the ZIF-8 nanoparticles are converted into carbon-nitrogen layers at high temperatures. Next, a heating process based on the autocatalytic effect of Cu can be used to etch the hollow structure prepared by the carbon-nitrogen layers. The CuO-ZnO@CN hollow composites fabricated using this method exhibit excellent catalytic properties for the ethynylation of formaldehyde. The proposed strategy can be used to develop techniques for syntheses of readily reducible carbon oxide claddings and their composites.

An efficient and noticeable wet etching method for PZT and its application in scanning mirrors

This study introduces an innovative wet etching method for magnetron sputtering lead zirconate titanate (PZT) thin films, leveraging a solution of HBF4 as the etchant and HCl for intermediate product removal. Compared to traditional dry etching techniques, the focus is on improving cost efficiency and etching performance. Wet etching offers numerous advantages, including lower costs, faster etching rates, minimal lateral etching, and smoother etch profiles, making it a viable alternative for fabricating MEMS piezoelectric devices. The effectiveness of this method was substantiated through the production and performance assessment of a 1D scanning mirror, demonstrating that the wet etched PZT retained its piezoelectric properties and was efficiently integrated into MEMS applications without adverse effects on its piezoelectric functionality. Results confirm that this wet etching approach not only maintains the structural integrity of PZT films but also supports the manufacturing of MEMS devices with precise dimensional control and reliable operational performance.

Integrated Motion Control Strategy Considering Parameter Robustness for Distributed Vehicle by Wire

<div>To address the issues of functional conflicts in execution subsystems and the deterioration of control performance due to model parameter uncertainties in the motion control of distributed vehicle by wire, this article proposes an integrated control strategy considering parameter robustness. This strategy aims to compensate for model mismatch, resolve functional conflicts, and achieve motion coordination. Based on the over-actuation characteristics of distributed vehicle by wire, this article constructs the dynamic model and utilizes the tire cornering properties along with phase portraits to delineate the working regions of the execution subsystems. To deal with model parameter uncertainties and mismatch, tube-based model predictive control (tube-based MPC) is applied to the control strategy design, which compensates for model deviations through state feedback and constructs a robust positively invariant set (RPI) to constrain the system state. Correspondingly, the weights of control inputs are adjusted adaptively, according to the working regions, to optimize the coordination logic of integrated control. In order to verify the effectiveness and feasibility of the strategy, extreme driving condition tests are executed on hardware-in-the-loop (HIL) and real vehicle test platforms. The test results indicate that the strategy proposed in this article is able to reduce the sideslip angle and tracking error of yaw rate, improve driving stability under extreme conditions through integrated control, and especially, it can still maintain precise stability control performance under severe model mismatch, exhibiting strong robustness facing parameter uncertainties.</div>

Spin-state regulation by secondary coordination sphere for improved oxygen evolution activity of LaCo1-xNixO3 perovskite

The evolution of oxygen evolution reaction (OER) remains a pivotal challenge in the realm of oxygen electrocatalysis. Recently, the proposition of regulating spin states has emerged as a novel avenue for enhancing the efficiency of electrocatalytic reactions. Presently, accurately modulating the metal active center into immediate spin (IS) remains a formidable challenge. Here, our research has achieved a breakthrough in precise spin state control and catalytic performance enhancement of Co3+ through the utilization of Ni-substituted LaCo1-xNixO3 perovskite. This achievement is primarily attributed to the fine regulation of both the secondary coordination sphere (SCS) and primary coordination sphere (PCS), i.e., Co6-y-[Co]-Niy (y = 0–6) and Co-O lengths, respectively. Our findings reveal that LaCo7/9Ni2/9O3 consisted of the SCS (y ≤ 2) with the eg1 fillings of IS Co3+ exhibits an optimal OER activity. Additionally, adjusting the Co-O bond length in PCS to approximately 1.89 Å proves to be more conducive to the transition of Co3+ spin states from high spin (HS) and low spin (LS) to IS. These discoveries present new approach to precisely modulating the spin state, offering promising prospects for the development of high-efficiency OER catalysts.

Physics‐Informed Neural Networks to Model and Control Robots: A Theoretical and Experimental Investigation

This work concerns the application of physics‐informed neural networks to the modeling and control of complex robotic systems. Achieving this goal requires extending physics‐informed neural networks to handle nonconservative effects. These learned models are proposed to combine with model‐based controllers originally developed with first‐principle models in mind. By combining standard and new techniques, precise control performance can be achieved while proving theoretical stability bounds. These validations include real‐world experiments of motion prediction with a soft robot and trajectory tracking with a Franka Emika Panda manipulator.

Deep Reinforcement Learning Framework-Based Flow Rate Rejection Control of Soft Magnetic Miniature Robots.

Soft magnetic miniature robots (SMMRs) have potential biomedical applications due to their flexible size and mobility to access confined environments. However, navigating the robot to a goal site with precise control performance and high repeatability in unstructured environments, especially in flow rate conditions, still remains a challenge. In this study, drawing inspiration from the control requirements of drug delivery and release to the goal lesion site in the presence of dynamic biofluids, we propose a flow rate rejection control strategy based on a deep reinforcement learning (DRL) framework to actuate an SMMR to achieve goal-reaching and hovering in fluidic tubes. To this end, an SMMR is first fabricated, which can be operated by an external magnetic field to realize its desired functionalities. Subsequently, a simulator is constructed based on neural networks to map the relationship between the applied magnetic field and robot locomotion states. With minimal prior knowledge about the environment and dynamics, a gated recurrent unit (GRU)-based DRL algorithm is formulated by considering the designed history state-action and estimated flow rates. In addition, the randomization technique is applied during training to distill the general control policy for the physical SMMR. The results of numerical simulations and experiments are illustrated to demonstrate the robustness and efficacy of the presented control framework. Finally, in-depth analyses and discussions indicate the potentiality of DRL for soft magnetic robots in biomedical applications.

Modelling and analysis for the dynamic behavior of 6-DOF motion platform used for simulating aerospace mechanical environment

As high carrying capacity, high frequency response and high precision control performance are presented, the 6 degree of freedom (DOF) motion platform is chosen as the aerospace device to implement mechanical environment simulation experiment on the ground. In this paper, a dynamic model of the 6 DOF motion platform is firstly developed based on the Newton-Euler method. Then the attitude determination and the driving force are analyzed by solving the dynamic equations. Furthermore, a parametric modelling method for the 6 DOF motion platform is proposed and applied to obtain the expansion amounts and the driving forces of the 6 hydraulic rods in the motion platform by adopting the Adams software. Results show a good agreement with the computation results of the theory dynamic model, which demonstratesthe validity of the proposed modelling method for analysis of the dynamic behavior of the 6-DOF motion platform.

DESIGN OF BLDC MOTOR BASED POSITION CONTROL DRIVE

The objective of this work is to analyse an accurate position control of the BLDC motor using Field Oriented Control (FOC) algorithm. The BLDC motor, owing to its operational principle, boasts faster responses and higher torque, along with reduced noise, fewer spark-related issues, and lower maintenance requirements than classical DC motors. Field-Oriented Control (FOC) is an advanced control algorithm initially developed and utilized for induction and synchronous motor drives. By implementing a suitable coordinate system transformation, it becomes feasible to autonomously regulate the machine's flux and torque, akin to controlling a DC motor. To control its position accurately, you need to manipulate the stator currents and voltages. The FOC, also known as vector control, is a sophisticated control algorithm used to control BLDC motors. Field- Oriented Control (FOC) allows for the individualistic control of torque and flux. It offers a rapid dynamic response, exceeding the demands of applications such as washing machines. FOC eliminates torque ripple and enables precise motor control, delivering smooth and accurate performance across a wide range of speeds. In this project we use the FOC algorithm to identify the position control of BLDC motor. FOC delivers superior dynamic response in contrast to traditional control techniques. After performing the circuit in MATLAB and stimulating the circuit Analyze a waveform/ output. Before implementing the control system in a real BLDC motor, it is essential to simulate and test it thoroughly. Tools like MATLAB/Simulink are often used for simulation. KEYWORDS: BLDC; FOC; Hall effect sensor; Motor; Position

Active control for vehicle suspension using a self-powered dual-function active electromagnetic damper

Concerning vehicle suspension techniques, active suspension tends to offer the most precise control performance compared to passive and semi-active alternatives. However, due to its high-power consumption, its practical use is often limited. This study aims to develop an innovative self-powered active suspension by assessing the performance and feasibility of a dual-function active electromagnetic damper (DF-AEMD), which is capable of providing simultaneous force tracking and energy harvesting functions. In addition to outlining its working mechanism, the DF-AEMD is utilized to track the full-loop target control force calculated from the classic linear quadratic regulator in a self-powered manner. From the force tracking standpoint, the DF-AEMD can not only provide damping forces, but also generate actuation forces, and thus achieve better tracking accuracies of target control force and control performances for vehicle suspension than the traditional semi-active damper; while from the energy harvesting aspect, sufficient energy can be harvested simultaneously to realize the self-powered active control. Systematic simulations are conducted to discuss the relationship between control performance and energy harvesting efficiency of the DF-AEMD.

Robust Bounded Control for Fuzzy Mechanical Systems: Fuzzy Optimal Design and Inequality Constraint Reorganize

This paper presents a fuzzy optimization design for the tank bidirectional stabilization system with a fully-electric actuator. The uncertainty in the system is (possibly fast) time-varying but lies in the fuzzy set. First, a machine-electric coupled state-space equation that can accurately describe the state transition of the system is established by considering the assembly relationship among components. Second, inequality constraints are proposed to reduce the range of steady-state error fluctuations. By constraint reorganization, a new system containing only equality constraints is constructed to replace the original system. The control problem for systems with inequality constraints is solved. Third, fuzzy set theory is used to characterize the “fuzzy” features of the uncertainty in the system more accurately, and robust control with parameters to be determined is proposed. The optimal control parameter is selected by establishing and minimizing a fuzzy performance index, which weighs the effectiveness and cost of control. Thus, the contradiction between short time and high precision control performance due to the fast movement of the tank is solved. This paper is the first to consider the tank bidirectional stabilization system with a fully-electric actuator as a fuzzy mechanical system with inequality constraints and conduct an optimization design after proposing an error-controllable control strategy.

Application of AI and ML

Artificial inteligence(AI) and machine learning(ML) are arguably the most distruptive technologies in the modern world. The possible applications in robotics and automation are virtually limitless. Thus, this paper explores uses in two key areas: servo motors and the kinematics and dynamics models of robotics and drones. Reliable studies providing insights into these topics were used to develop the report. The study found that AI and ML enable precise control, obstacle avoidance, and adaptive performance in servo motors and motion planning, snesor fusion, fault detection, and system identification in kinematics and dynamics models. These findings confirm the vitality of AI and ML for the functionality of intelligent robots and drones used across different industries, such as construction and manufacturing.

Coupled effect of pulsed current and ultrasonic vibration on deformation behavior of Inconel 718 sheet: phenomena and modeling

Multi-energy fields provide higher dimensional precision control and desirable performance for microforming processes. The constitutive model under low strain level was established to describe the coupled effect of pulsed current and ultrasonic vibration based on the Kocks-Mecking (K-M) model. Combined with the crystallographic theory, the model introduced acoustic softening effect and electroplastic effect, and described the interaction between pulsed current and ultrasonic vibration under coupled field from the perspective of energy absorption. In the research, the uniaxial tensile test of Inconel 718 sheet with thickness of 0.3 mm was carried out. The ranges of the current density and ultrasonic energy density were 20.9–50.9 A mm−2 and 15.84–57.64 J cm−3, respectively. The results showed that: the facilitation of ultrasonic vibration on dislocation generation was limited with the increase of strain, and the energy generated by ultrasonic vibration was more reflected in the change of Helmholtz free energy; when the energy generated by the current at athermal condition tended to be saturated, the increase of heat became the key to improve the electroplasticity, and the heating model caused by Joule heating effect was established. The constitutive model of coupling electric pulse and ultrasonic treatment was established, combining the characteristics of ultrasonic vibration and pulsed current; the flow stress was described by using the parameters of ultrasonic energy density and current density; The trigonometric function was used to quantitatively describe the competitive relationship between ultrasonic vibration and pulsed current in external work. Two sets of experiments were used to demonstrate the reliability of the model.

Predefined Time Active Disturbance Rejection for Nonholonomic Mobile Robots

This article studies the fast path tracking problem for nonholonomic mobile robots with unknown slipping and skidding. Firstly, considering the steering problem, a new mathematical model of nonholonomic mobile robot is derived. Secondly, to estimate the unknown slipping and skidding of a nonholonomic mobile robot quickly and accurately, new predefined time observers, which can attain a predefined settling time, are established. Thirdly, based on the observers and the sliding mode approaches, predefined time active controllers are proposed to achieve high precision control performance of the nonholonomic mobile robot tracking. The method proposed in this article can achieve uniformly global stability within a predefined time, which makes the adjustment of the convergence time of the nonholonomic mobile robot easier and convenient. Finally, the simulation results validated the theoretical results.

Composite anti-unwinding attitude control of spacecraft under actuator saturation and angular velocity limits

Spacecraft attitude control employs quaternion representation to avoid singularity issues and obtain global maneuver stability. However, it has redundancy associated, resulting in dual equilibrium points. The attitude control may confront the unwinding phenomenon due to the presence of the equilibria. In this paper, an anti-unwinding controller for spacecraft stabilization in the presence of external disturbances and inertial uncertainties, angular velocity limit, actuator saturation and faults is presented. Specifically, a modified extended state observer (ESO) is designed to obtain total disturbance estimates of the rigid-body spacecraft. Auxiliary parameters are added in the design to avoid initial high estimates in the proposed ESO design resulting in faster convergence. The proposed disturbance observer is to release the assumption that requires the estimated disturbance to be constant or varying at slow rates. Using the ESO estimates, a particular back-stepping-SMC (ESO-BTSMC) controller is devised to formulate anti-unwinding control law for spacecraft stabilization. Lyapunov stability theory is employed to prove closed-loop stability of system and estimation error convergence. Comparative simulations are performed to demonstrate the performance of the proposed control scheme. It is found that the proposed control scheme gives smooth and precise control performance along with faster transient response. Additionally, it also alleviates the chattering phenomenon.

Research on Precise Flow Control and Anti-interference Performance of Load Sensitive Multi-way Valve with After-valve Compensation

Regarding a load-sensitive system with after-valve compensation, in the case of a single action or composite action, theoretically, the flow of each channel neither varies with the load pressure of this channel, nor is it affected by the flow of other channels, and the anti-interference performance is good. In fact, due to the influence of factors such as the flow passage of the valve body, the matching between the pressure compensation valve and the main spool, its flow control and anti-interference performance are difficult to achieve good results, which has a great impact on the synchronization, rapid action impact and micro motion characteristics of the main engine of construction machinery. This paper studies the simplification and analysis of the system pressure drop diagram, the matching characteristics between the pressure compensation valve and the main valve, accurate modeling and simulation technology, solves the problems of flow fine control and anti-interference performance, improves the flow control accuracy of a single action and compound action, and enhances the anti-interference performance. The real vehicle test shows that the self-developed multi-way valve for compensating for load sensitivity behind the valve is installed on the 6T small excavation. The initial position of the excavator’s track is aligned along a straight line. After the test runs for 20 meters, the track offset is measured. The fast forward offset is only 20mm, and the slow forward offset is only 7mm.

Precise Stop Control and Experimental Validation for Metro Train Overcoming Delays and Nonlinearities

Automatic Train Operator (ATO) is a system equipped on metro trains and controls train operation. One of the main functions of ATO is precise stop control, which aims to ensure that a train stops at a designated position on the platform with errors within a predefined specification. Precise stop control is an important function because trains stopping outside of the specification adversely affect passenger safety and punctuality of operation schedule. It is difficult to achieve high accuracy and precision for an ATO since trains are heavy, train control feedback loops involve several steps that cause various levels of time delay, and the actuation has nonlinear characteristics. Moreover, although existing studies propose algorithms to overcome some of the said challenges, few include experimental validations because access to the experimental environment is extremely limited. In this work, we address each one of the challenges and propose a mitigation algorithm, respectively. Moreover, unlike other studies, we experimental validate on an actual train the proposed algorithms. The precise stop control performance is shown to be superior to that of existing system.

Precision Motion Control of Robotized Industrial Hydraulic Excavators via Data-Driven Model Inversion

This work proposes a novel precision motion control framework of robotized industrial hydraulic excavators via data-driven model inversion. Rather than employing a single neural network to approximate the whole excavator dynamics, including input delays and dead-zones, we construct a physics-inspired data-driven model with a modular structure. The data-driven model is then inverted in a modular fashion which benefits the training speed. The data-driven model and its inversion are trained offline in a supervised manner using the real operational data since online learning methods can damage the machine and surroundings. The entire motion control framework consists of the data-driven model inversion that compensates for the excavator dynamics and the proportional control that determines the input of the model inversion to enhance the robustness. The framework is experimentally validated with a commercial 38-ton class hydraulic excavator for digging and grading tasks, achieving a precise control performance (i.e., root-mean-square of the path following error under <inline-formula><tex-math notation="LaTeX">$2 \;[\rm cm]$</tex-math></inline-formula>) even under severe soil interactions.

Binocular visual function and fixational control in patients with macular disease: A review.

For normally sighted observers, the centre of the macula—the fovea—provides the sharpest vision and serves as the reference point for the oculomotor system. Typically, healthy observers have precise oculomotor control and binocular visual performance that is superior to monocular performance. These functions are disturbed in patients with macular disease who lose foveal vision. An adaptation to central vision loss is the development of a preferred retinal locus (PRL) in the functional eccentric retina, which is determined with a fixation task during monocular viewing. Macular disease often affects the two eyes unequally, but its impact on binocular function and fixational control is poorly understood. Given that patients’ natural viewing condition is binocular, the aim of this article was to review current research on binocular visual function and fixational oculomotor control in macular disease. Our findings reveal that there is no overall binocular gain across a range of visual functions, although clear evidence exists for subgroups of patients who exhibit binocular summation or binocular inhibition, depending on the clinical characteristics of their two eyes. The monocular PRL of the better eye has different characteristics from that of the worse eye, but during binocular viewing the PRL of the better eye drives fixational control and may serve as the new reference position for the oculomotor system. We conclude that evaluating binocular function in patients with macular disease reveals important clinical aspects that otherwise cannot be determined solely from examining monocular functions, and can lead to better disease management and interventions.

유전 알고리즘을 이용한 최적 서보 제어 기반의 다입 · 출력 가변속 오일쿨러 시스템의 정밀한 온도제어

A variable speed refrigeration system (VSRS) has been applied to various industrial fields because of its excellent energy saving performance and partial load response capability. In this paper, an optimal servo controller for a variable speed oil cooler system was investigated to obtain precise temperature control performance against disturbance and model uncertainty. A state space model for controller design was derived from empirical transfer function. The controller was designed as a servo type to follow the step command strictly while minimizing an evaluation function of the secondary form for state variable and input energy. Especially, the weighting matrix, a key parameter in an optimal servo controller design, was automatically designed to meet design specifications using genetic algorithm (GA). The validity of the suggested controller was confirmed through MATLAB-based simulations and actual experiments. Moreover, the optimality and ease of design of the proposed optimal servo controller were demonstrated by comparing results with a conventional PI controller.

Decoupling Control of a Multiaxis Hydraulic Servo Shaking Table Based on Dynamic Model

Hydraulic servo shaking table is an essential testing facility to simulate the actual vibration situation in real time. As a parallel mechanism, multiaxis hydraulic servo shaking table shows strong coupling characteristic among different degrees of freedom. When the multiaxis hydraulic shaking table moves to one direction, some unnecessary related motions will appear in other directions, which seriously affect the control performance. An effective approach to decouple motions in command direction and in unnecessary related directions is an urgent need for a higher precision control performance. In this work, the coupling phenomena and reasons of the multiaxis hydraulic servo table are analyzed based on dynamic model of a multiaxis hydraulic servo shaking table. In this regard, multiaxis hydraulic servo shaking table with strong coupling within the physical space is transformed into a set of single-input single-output systems that are independent of each other in the modal space. A decoupling control strategy is proposed in modal space to restrain the coupling motions. Simulation and experimental results show that the proposed control strategy can effectively improve the control performance and the decoupling effect.