Closed-loop Test Research Articles

Psychometric evaluation of high-resolution electrotactile interface for conveying 3D spatial information

This study presents a detailed psychometric evaluation of a novel high-resolution electrotactile interface, which is developed to provide users with 3D spatial information and facilitate enhanced interaction with a Supernumerary Robotic Limb (SRL). The research introduces a novel electrotactile system that employs a multi-pad electrode configuration on the thigh, aimed at delivering intuitive feedback to users about the position of the SRL in a three-dimensional space. The interface's effectiveness was assessed through a series of psychometric tests, including static spatial discrimination, target-reaching with spatial feedback, frequency discrimination, and combined spatial/frequency modulation. The key findings demonstrate that participants could differentiate between 30 electrode pads with an average success rate of 62.7% when they were activated statically, while in the dynamic target-reaching task, the success rate increased to 88.1%. Frequency discrimination tests further revealed that four frequency levels could be distinguished with 86.0% success rate in single-pad feedback while the performance decreased to 74.3% in multi-pad distributed feedback. Finaly, in the closed-loop test with mixed spatial and frequency modulation, participants achieved an overall success rate of 78.8% in target reaching across 10 × 4 discrete 2D space. These results highlight the interface’s capability to transmit high-resolution spatial information through electrotactile feedback, offering a foundation for future applications in tactile-based navigation and control systems.

Recoverable and plastic strains generated by forward and reverse martensitic transformations under external stress in NiTi SMA wires

Although superelastic NiTi shape memory alloy wire displays very high resistance to plastic deformation in austenite and martensite phases, incremental plastic strains are recorded whenever the cubic to monoclinic martensitic transformation (MT) proceeds under external stress leading to functional fatigue degradation. Therefore, special closed-loop thermomechanical loading tests were performed to shed light on the mechanism by which the incremental plastic strain are generated. These tests revealed that both forward and reverse MTs occurring above certain stress thresholds generate plastic strains specific for the [temperature, stress] conditions under which the MTs occurred. While the forward MT upon cooling does not produce plastic strain or permanent lattice defects up to 500 MPa stress, the reverse MT upon heating starts to generate them from 100 MPa. While plastic strain generated by the forward MT merely elongates the wire, plastic strain generated by the reverse MT also reduces the recoverable strain. Since the reverse MT upon heating generates plastic strains at lower external stresses than the forward MT upon cooling, it is largely responsible for cyclic instability of NiTi actuators. The characteristic thresholds and magnitudes of plastic strains generated by the forward and reverse MTs define the functional fatigue limits for specific NiTi wires.

Effects of input gradient regularization on neural networks time-series forecasting of thermal power systems

This study proposes using neural networks, specifically gated recurrent unit (GRU), long-short-term memory (LSTM), and transformer networks, to improve control strategies in a 450 MW coal-fired power plant. However, neural networks face issues of becoming overly dependent on just a few variables to make predictions, which negatively impacts control decisions that rely on the model to determine the value of all manipulated variables. The paper introduces regularization techniques, including noise injection and input gradient regularization, during the training phase. The work presents novel contributions in adapting neural networks to control industrial systems and applying regularization techniques from computer vision to industrial process control. Results demonstrate the effectiveness of input gradient regularization in reducing model dependence on subsets of variables, emphasizing the balance between fidelity and controllability. Further exploration is recommended, including the development of recurrent transformers, closed-loop control testing, and a sensitivity analysis on computer models to provide further insight.

Keck Primary Mirror Closed-loop Segment Control Using a Vector-Zernike Wavefront Sensor

We present the first on-sky segmented primary mirror closed-loop piston control using a Zernike wavefront sensor (ZWFS) installed on the Keck II telescope. Segment cophasing errors are a primary contributor to contrast limits on Keck and will be necessary to correct for the next generation of space missions and ground-based extremely large telescopes, which will all have segmented primary mirrors. The goal of the ZWFS installed on Keck is to monitor and correct primary mirror cophasing errors in parallel with science observations. The ZWFS is ideal for measuring phase discontinuities such as segment cophasing errors and is one of the most sensitive WFSs, but has limited dynamic range. The vector-ZWFS at Keck works on the adaptive-optics-corrected wavefront and consists of a metasurface focal plane mask that imposes two different phase shifts on the core of the point-spread function to two orthogonal light polarizations, producing two pupil images. This design extends the dynamic range compared with the scalar ZWFS. The primary mirror segment pistons were controlled in closed loop using the ZWFS, improving the Strehl ratio on the NIRC2 science camera by up to 10 percentage points. We analyze the performance of the closed-loop tests, the impact on NIRC2 science data, and discuss the ZWFS measurements.

A Direct Approach for Local Quasi-Geoid Modeling Based on Spherical Radial Basis Functions Using a Noisy Satellite-Only Global Gravity Field Model

A Direct Approach for Local Quasi-Geoid Modeling Based on Spherical Radial Basis Functions Using a Noisy Satellite-Only Global Gravity Field Model

Design of Improved Ground Closed-loop Test System Based on Variable-stability Flight Control Computer

With the development of civil aviation, ground simulation test has gradually become a key link in the transformation of basic theory to engineering application. By forming a closed-loop link between the variable stability aircraft (VSA) dynamics model and the aircraft rudder surface, sensors, etc., the variable-stability ground closed-loop test system can simulate the aircraft flight state and response characteristics in the ground environment. However, the current ground test system suffers from a wide range of signals, complex signal simulation methods, and low system integration. In this study, an improved modular ground closed-loop test system for VSA is designed from three aspects: signal acquisition and simulation of airborne equipment, parameter monitoring and data processing, and fault simulation subsystem, which improves its compatibility and integrates with the software hardware of the variable-stability flight control computer and other air-borne equipment. After the system interconnection test, the generalized ground closed-loop test system designed in this study can replace the original system more efficiently, and its signal connection has inheritability, and provides a design method reference for other related researches of the variable-stability flight control computer.

The Idea of an Integration Interface for Model-based Software Development's Processor-in-the-Loop (PiL) Simulation

Model-based design (MBD) has become a cornerstone in the development of embedded software, particularly in the automotive industry. Processor-in-the- Loop (PiL) simulation bridges the gap between virtual simulation environments and real hardware, enabling early verification and validation of control algorithms running on target processors. The design and implementation of toolchain is required for target specific code generation, compilation, and execution. Developing a toolchain specifically for the target architecture is crucial to preventing errors and ensuring smooth production. The configuration and designing of toolchain are one time effort of all Simulink model which want to be test on board. This research investigates the importance of PiL for embedded systems in the automobile area. It describes the construction of a toolchain that integrates PiL simulation with Simulink, a popular MBD tool. The paper discusses the unique integration of TRACE32, a debugger and code analysis tool, with Simulink for testing programmes on Infineon devices. TPT makes use of graphical test models that are easy to understand and have the capacity to automate complex closed loop tests in real time. It was Daimler Software Technology Research that initially developed TPT. Nowadays, vendors and automakers employ it in their development projects for production vehicles.

Experiences in developing empirical harvest strategies for the Indonesian tropical tuna fisheries

Harvest strategies, also referred to as management procedures, are foundational to evidence-based fisheries decision making. Together with closed-loop simulation testing, termed Management Strategy Evaluation, of potential harvest strategies, they are considered to be best practice. However, the use of these approaches is still mostly limited to a small minority of high-value, data-rich stocks in developed economies. The Indonesian tropical tuna fisheries provide a rare real-world experience where formal empirical harvest strategies were being developed for data-limited fisheries within a developing economy as an operational management tool. The Harvest Strategy Framework is a departure from a previously open-access fishery for tropical tuna in Indonesian archipelagic waters. It outlines the necessary actions to operationalize the harvest strategy, including fisheries monitoring, harvest control rules, associated management measures to meet the management objectives, and immediate actions to reduce the levels of catch as a precautionary measure until the harvest strategy is fully implemented. It was developed over a 9-year national consultative process, commencing in 2014 and culminating in the official adoption of the framework in June 2023. This paper outlines key processes and challenges encountered in stakeholder engagement and technical capacity development within the intricate governance context of Indonesia. We argue that the engagement processes have cemented a future direction towards sustainable and best practice fisheries management in Indonesia and the effective co-production process has potential to show the way for other fisheries in coastal developing states who face similar challenges in managing their national and internationally shared stocks.

Practical System Identification and Incremental Control Design for a Subscale Fixed-Wing Aircraft

An incremental differential proportional integral (iDPI) control law using eigenstructure assignment gain design is tested in flight on a subscale platform to validate its suitability for fixed-wing flight control. A kinematic relation for the aerodynamic side-slip angle rate is developed to apply a pseudo full state feedback. In order to perform the gain design and assessment, a plant model is estimated using flight test data from gyro, accelerometer, airspeed and surface deflection measurements during sine-sweep excitations. Transfer function models for the actuators and surface deflections are identified both in-flight and on the ground for several different actuators and control surfaces using hall sensor surface deflection measurements. The analysis reveals a large variation in bandwidth between the different types of servo motors. Flight test results are presented which demonstrates that the plant model estimates based on tests with good frequency excitation, high bandwidth actuators and surface deflection measurements can be used to reasonably predict the closed-loop dynamic behavior of the aircraft. The closed-loop flight test results of the iDPi control law show good performance and lays the groundwork for further development.

FI-NPI: Exploring Optimal Control in Parallel Platform Systems

Typically, the current and speed loop closure of servo motor of the parallel platform is accomplished with incremental PI regulation. The control method has strong robustness, but the parameter tuning process is cumbersome, and it is difficult to achieve the optimal control state. In order to further optimize the performance, this paper proposes a double-loop control structure based on fuzzy integral and neuron proportional integral (FI-NPI). The structure makes full use of the control advantages of the fuzzy controller and integrator to improve the performance of speed closed-loop control. And through the feedforward branch, the speed error is used as the teacher signal for neuron supervised learning, which improves the effect of current closed-loop control. Through comparative simulation experiments, this paper verifies that the FI-NPI controller has a faster dynamic response speed than the traditional PI controller. Finally, in this paper, the FI-NPI controller is implemented in C language in the servo-driven lower computer, and the speed closed-loop test of the BLDC motor is carried out. The experimental results show that the FI-NPI double-loop controller is better than the traditional double-PI controller in performance indicators such as convergence rate and RMSE, which confirms that the FI-NPI double-loop controller is more suitable for BLDC servo control.

Experimental characterization of a mode-localized acceleration sensor integrating electrostatically coupled resonators

A novel mode-localized acceleration sensor employing an electrostatically coupled resonator and integrating a lever with proof mass is micromachined using standard silicon on insulator (SOI) technology. In order to determine the linear dynamic range of the sensor, a reduced order model is developed while assuming that the resonators vibrate below the critical amplitude. Then, open-loop and closed-loop testing platforms are established to measure the performance of the linearly operating accelerometer in a vacuum environment (less than 5 Pa). Moreover, the corresponding amplifier circuit based on the capacitive detection principle is designed in order to extract and amplify the current signal from the resonators. The obtained results show that the accelerometer sensitivity can be increased by three orders of magnitude when using the relative shift of amplitude ratio as the output metric instead of the relative shift of frequency, and the experimental measurements are consistent with the theoretical predictions. Remarkably, the Allan standard deviation of the mode-localized acceleration sensor obtained from the closed-loop testing circuit is around 5.03 μg.

Differentiable Integrated Motion Prediction and Planning With Learnable Cost Function for Autonomous Driving.

Predicting the future states of surrounding traffic participants and planning a safe, smooth, and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs). There are two major issues with the current autonomous driving system: the prediction module is often separated from the planning module, and the cost function for planning is hard to specify and tune. To tackle these issues, we propose a differentiable integrated prediction and planning (DIPP) framework that can also learn the cost function from data. Specifically, our framework uses a differentiable nonlinear optimizer as the motion planner, which takes as input the predicted trajectories of surrounding agents given by the neural network and optimizes the trajectory for the AV, enabling all operations to be differentiable, including the cost function weights. The proposed framework is trained on a large-scale real-world driving dataset to imitate human driving trajectories in the entire driving scene and validated in both open-loop and closed-loop manners. The open-loop testing results reveal that the proposed method outperforms the baseline methods across a variety of metrics and delivers planning-centric prediction results, allowing the planning module to output trajectories close to those of human drivers. In closed-loop testing, the proposed method outperforms various baseline methods, showing the ability to handle complex urban driving scenarios and robustness against the distributional shift. Importantly, we find that joint training of planning and prediction modules achieves better performance than planning with a separate trained prediction module in both open-loop and closed-loop tests. Moreover, the ablation study indicates that the learnable components in the framework are essential to ensure planning stability and performance. Code and Supplementary Videos are available at https://mczhi.github.io/DIPP/.

A Compliant Self-Stabilization Nanopositioning Device With Modified Active–Passive Hybrid Vibration Isolation Strategy

Micro/mini light-emitting diodes (LEDs) display panel inspection and repairs have a high demand for vibration isolating devices to protect industrial-level atomic force microscopes (AFM scanning head) against vibrations. The motivation of this work is to combine the advantages of both passive and active vibration isolation strategies to improve inspection performance. The developed self-stabilization device achieves this objective with a design that incorporates a suspension-type passive vibration isolation unit and integrates it with the modified active–passive hybrid (MAPH) vibration isolation strategy using piezoelectric ceramics (PZT) and voice coil motors (VCM) as compensators. First, the design, modeling, and optimization of a self-stabilization device are presented based on the MAPH vibration isolation strategy. To satisfy the requirements of vibration isolation performance and a lightweight design, a multiobjective optimization task was conducted. Next, a tailor-made double compensating PID controller was designed to allow this mechanism to run in the MAPH method to effectively isolate vibrations. Finally, a series of validation experiments, including passive vibration isolation performance tests and MAPH closed-loop tests, were applied. From 1 to 500 Hz, more than 98% frequency domain achieved a vibration isolation rate of 90%, the vibration amplification effect of the passive vibration isolation was significantly suppressed, the steady-state positioning accuracy reached <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm 0.1\;\mu$</tex-math></inline-formula> m, load capacity was up to 2.5 kg, the attenuation ratio of the disturbances reached up to 70%, and the heat of the VCM was effectively reduced. All results comprehensively confirmed that the developed compliant MAPH vibration isolation system has achieved a satisfactory self-stabilization function.

Improvement of Guided Projectile Maneuverability Using Airframe-Controller Optimization

Guided projectiles allow one to augment the effect of artillery on the battlefield, but their lack of control authority limits the flexibility of such means of fire support. In this article, a new methodology is proposed to extend the maximum angle of attack at which a fin-stabilized guided projectile can be controlled, in spite of stringent actuator limitations. Plant-controller optimization is leveraged to maximize the control performance and mitigate the detrimental effects of aerodynamic nonlinearities on the projectile attitude dynamics. A nonlinear model is introduced to account for the effects of canard stall at the design stage and evaluate the performance of the system. Closed-loop wind-tunnel testing is performed to validate the aforementioned model and assess the performance of the optimal design against other guided projectile configurations. Numerical and experimental results show that the optimal projectile is faster for most angles of attack between 0 and 10 deg and features better disturbance rejection properties than its closest competitor.

Vortex-induced energy loss and pressure fluctuations in a closed sump under various conditions: An experimental investigation

The roof-attached vortices (RAVs), floor-attached vortices (FAVs), and complex combined submerged vortices (CSVs) excited in a closed pump sump will induce irregular pressure fluctuations that can decrease the energy efficiency of the pump and the stability of its input flow. This article presents an experimental investigation of the energy loss and pressure fluctuations induced by these vortices under various conditions. A transparent closed-loop test rig was used for high-speed visualization and pressure-fluctuation tests. The findings demonstrate that as the flow rate is increased, the head and efficiency of the pump device significantly decrease. A CSV, which is composed of an RAV and an FAV, has the highest head-loss value, reaching 0.21 m, with an efficiency decrease in up to 2.8%. The typical evolution stages of RAV are divided into newborn, developing, fully developed, and dissipative collapsing. The typical evolution stages of FAV are divided into germinating, growing, maintaining, decaying, and disappearance. The maximum diameter of the FAV core is approximately 0.05 times the maximum vortex-core scale of the RAV. Based on the proposed vortex-identification method, as the bellmouth height decreases, the strength of the FAV increases during the CSV period, and the degree of pressure fluctuation becomes more severe. In addition, regardless of the flow rate, the strength of the FAV structure is always much greater than that of the RAV.

Investigation of the driving characteristics of electric bicycles by means of multibody simulation

Electrically assisted bicycles lead to a change in the driving characteristics compared to conventional bicycles because of the additionally attached masses of motor and battery. In order to describe the resulting interaction of the electric drive components on the driving characteristics, this paper examines simulations of the driving behavior of bicycles with different positions of battery and motor in open- and closed-loop tests. The integration of human driving behavior by control loops allows the evaluation of driving characteristic under more realistic driving conditions compared to the existing studies of the eigenbehavior. The results are analyzed using characteristic values that describe the system behavior in a suitable way. Furthermore, a design of experiments is performed to classify the influence of the presented research results in relation to other system parameters, such as different types of bicycles and different rider postures.

Analysis of a Segmented Axial Active Magnetic Bearing for Multi-MW Compressor Applications

In high-speed compressor and turbine applications, active magnetic bearings are employed to increase reliability, reduce maintenance, and provide contact-free and oil-free operation. Evidently, the dynamic performance of active magnetic bearings must correspond to application-specific events like compressor surge or thrust disturbances. This article presents a segmented stator as one of the solutions to increase the bandwidth of axial active magnetic bearings (AMBs) and further improve the stability of the shaft system. We analyze the effects and discuss the tradeoffs of axial active magnetic bearing stator segmentation and slitting to alleviate bandwidth limitations. Finite element method design optimization and control plant modeling methods, open- and closed-loop tests, and identification of the overall thrust AMB–rotor control plant are studied.

Temporal validation and updating of a prediction model for the diagnosis of gestational diabetes mellitus

ObjectiveThe original Monash gestational diabetes mellitus (GDM) risk prediction in early pregnancy model is internationally externally validated and clinically implemented. We temporally validate and update this model in a contemporary population with a universal screening context and revised diagnostic criteria and ethnicity categories, thereby improving model performance and generalizability. Study Design and SettingThe updating dataset comprised of routinely collected health data for singleton pregnancies delivered in Melbourne, Australia from 2016 to 2018. Model predictors included age, body mass index, ethnicity, diabetes family history, GDM history, and poor obstetric outcome history. Model updating methods were recalibration-in-the-large (Model A), intercept and slope re-estimation (Model B), and coefficient revision using logistic regression (Model C1, original ethnicity categories; Model C2, revised ethnicity categories). Analysis included 10-fold cross-validation, assessment of performance measures (c-statistic, calibration-in-the-large, calibration slope, and expected-observed ratio), and a closed-loop testing procedure to compare models’ log-likelihood and akaike information criterion scores. ResultsIn 26,474 singleton pregnancies (4,756, 18% with GDM), the original model demonstrated reasonable temporal validation (c-statistic = 0.698) but suboptimal calibration (expected-observed ratio = 0.485). Updated model C2 was preferred, with a high c-statistic (0.732) and significantly better performance in closed testing. ConclusionWe demonstrated updating methods to sustain predictive performance in a contemporary population, highlighting the value and versatility of prediction models for guiding risk-stratified GDM care.

Generalized element-node complete model and its implementation for the optimal design of a piezo-actuated compliant amplifier

Compliant amplification mechanisms are widely applied to extend the stroke of stacked piezoelectric actuators. Accurate modeling of static and dynamic performances is crucial for the optimal design of complex compliant mechanisms. By generalizing the planar element-node model-based finite element method, this paper proposes a new modeling method capable of describing the spatial complete kinetostatics and dynamics for compliant mechanisms. On the basis of the widely reported complete compliance models for flexure hinges, a versatile stiffness model is established for the hinge with an arbitrary notch shape through the force equilibrium model. The generalized model is then demonstrated by applying for modeling and optimizing a compliant mechanism with dual-stage amplification. The verification through finite element simulations suggests that the maximum modeling error for the kinetostatic and first six resonant frequencies for the mechanisms with and without structural optimizations is less than 20%. Finally, the open-loop and closed-loop performance tests on the prototype with optimized parameters are conducted, demonstrating the effectiveness of the developed modeling and optimization methods.

Visualization of R134a cavitation flow in a rectangle Venturi tube and determination of thermal effects with cavity length and thickness

In order to study R134a cavitation behavior in a rectangle Venturi tube, high-speed photography combined with pressure, flow rate, and temperature measurements are performed to visualize the cavitation process and determine the cavity thickness and length through the tube in a closed-loop test system. The experimental results indicated that incipient cavitation of R134a is observed at the entrance of the tube throat when the cavitation number is below 2.50, regardless of the inlet temperature and pressure. The normalized cavity length is correlated with the cavitation number, and a critical cavitation number of 1.766 is defined to connect two linear functions. The throat and tail cavity thickness can be fitted as a cubic polynomial function with the cavitation number, and a transition cavitation number of 2.00 divides the growth rate of the thickness with the decrease of the cavitation number into fast and slow-growing parts. The temperature drop measured at the throat of the Venturi tube can be fitted to a cubic polynomial function about the cavitation number. Under the same cavitation number, the temperature drop decreases with the increase of the flow temperature. The B-factor can be fitted as a cavitation number quadratic polynomial function and a linear function of the tail cavity thickness. The transition cavitation number is defined by the transition point of the growth rate of the above function. The empirical correlation of B-factor is modified based on the throat and tail cavity thickness, and the cavitation thermal effect of R134a is accurately predicted. Combined with the inception cavitation number, the transition cavitation number, and the critical cavitation number, the shape of the cavity is divided into four regions: inception cavitation, attached cavitation, supercavitation, and fusion cavitation. The fast Fourier transformation analysis shows that the cavitation flow of R134a gradually changes from periodic flow to fully developed unsteady flow with the decrease of cavitation number.