Control Hardware Research Articles

Enhancing Electro-Hydraulic Load Simulator Performance Through Variable Arm Length and Particle Swarm-Optimized Controllers

This study presents a novel approach to improve the loading characteristics of an electro-hydraulic load simulator by introducing a variable arm length mechanism and employing advanced control strategies rooted in Particle Swarm Optimization (PSO). The design enhancements focus on optimizing the physical connection between the steering gear system and the load system, resulting in an electro-hydraulic load simulator equipped with adjustable arm length. Through a meticulous analysis of the ideal pivot position for the rocker lever, a fuzzy Proportional-Integral-Derivative (PID) controller based on PSO has been developed for the steering gear system. In recognition of the PSO algorithm’s susceptibility to local optima convergence, a fuzzy PID controller employing Single-Particle Random Search (SSPSO) has also been devised for the load system. To assess the effectiveness of this innovative hardware configuration and controller implementation in mitigating over-suppression, comprehensive joint simulation experiments were conducted. The results unequivocally indicate that the incorporation of the variable arm length structure, along with the PSO-based fuzzy PID controller optimized using binding factors, substantially enhances the dynamic loading performance of the electro-hydraulic load simulator. This enhancement translates into superior loading accuracy and overall performance metrics, establishing this research as a valuable reference for future investigations in this field.

Research on Fault Diagnosis System of IOT for Oil Well Pump Based on Machine Learning

Abstract In order to realize automatic prediction and processing of remote fault diagnosis of oil well pumps distributed in different regions by crude oil production enterprises, a fault diagnosis system for oil well pumps based on machine learning was researched and designed. This fault diagnosis system is composed of three layers (perception layer, network layer and control application layer) Internet of Things structure. The function and characteristics of each layer are analyzed in this paper, and the hardware composition and control principle of sensor nodes and aggregation nodes of the measurement and control system are discussed and gives the node microcontroller program design flow chart and the main module content of the IoT central computer software design. This paper focuses on the principle of machine learning for fault diagnosis and prediction, and deeply explains the algorithm steps of using LSTM for fault diagnosis of oil well pumps. The enterprise application experiment results show that, compared with the traditional manual well patrol fault diagnosis method, this system has the advantages of convenient operation and maintenance, low labor intensity, high timeliness and accuracy of fault diagnosis, which can better reduce equipment maintenance costs for enterprises.

Process Optimization and Design of an Automation Controller for a Multidisciplinary Combat Engineering System

Design of an automation controller for a “Electro-Hydro-Mechanical Object Laying System” is presented in this paper, which is a multidisciplinary equipment consisting of Electromechanical and Hydraulic Actuators and large number of sensors for process feedback. There are complex mechanisms and processes involved in this system, which are required to be operated/executed in sequential and parallel manner in real time. The operation of spatially distributed Electromechanical & Hydraulic actuators with feedbacks from multiple type of sensors are required to be synchronized for multiple activities at a faster rate along with safely handling of the objects. All the activities are automated with minimum human intervention to avoid risk to the crew. This paper mainly focuses on electronic controller hardware design for military environment and process optimization to achieve faster object laying rate.

Emulation of IEEE STD 421.5/Industrial Excitation Systems Using a Micro-Alternator's Exciter

Micro-alternators, having similar parameters as turbo-alternators, play a significant role in the experimental evaluation of power system dynamics. Early thyristor excitation systems of micro-alternators have limited re-configurable options. Three possible control structures, two closed-loop control structures proposed in the literature and one open-loop control structure proposed in this paper, including a time constant regulator to mimic large machine time constants, are evaluated to emulate any IEEE 421.5 standard excitation system model or interface an industrial excitation system controller hardware in the loop (CHIL). It is shown that the communication delay introduced in CHIL does not impact the closed-loop stability within the bandwidth requirements of the excitation systems. One of the closed-loop structures, which is more generalized, and the open-loop structure performances are compared for emulating IEEE AC4C, DC1C, and ST1C excitation systems on a laboratory micro-machine based single machine infinite bus test system with buck exciter and H-bridge chopper (HBC) exciter using EMTP simulations under small and large disturbances. HBC exciter is used in the experimental validation with both the closed-loop and the open-loop control structures. The proposed open-loop structure is easy to implement and performs better than the closed-loop structure.

Driving in the Rain: A Survey toward Visibility Estimation through Windshields

Rain can significantly impair the driver’s sight and affect his performance when driving in wet conditions. Evaluation of driver visibility in harsh weather, such as rain, has garnered considerable research since the advent of autonomous vehicles and the emergence of intelligent transportation systems. In recent years, advances in computer vision and machine learning led to a significant number of new approaches to address this challenge. However, the literature is fragmented and should be reorganised and analysed to progress in this field. There is still no comprehensive survey article that summarises driver visibility methodologies, including classic and recent data-driven/model-driven approaches on the windshield in rainy conditions, and compares their generalisation performance fairly. Most ADAS and AD systems are based on object detection. Thus, rain visibility plays a key role in the efficiency of ADAS/AD functions used in semi- or fully autonomous driving. This study fills this gap by reviewing current state-of-the-art solutions in rain visibility estimation used to reconstruct the driver’s view for object detection-based autonomous driving. These solutions are classified as rain visibility estimation systems that work on (1) the perception components of the ADAS/AD function, (2) the control and other hardware components of the ADAS/AD function, and (3) the visualisation and other software components of the ADAS/AD function. Limitations and unsolved challenges are also highlighted for further research.

Systematic hyperparameter selection in Machine Learning-based engine control to minimize calibration effort

For automotive powertrain control systems, the calibration effort is exploding due to growing system complexity and increasingly strict legal requirements for greenhouse gas and real-world pollutant emissions. These powertrain systems are characterized by their highly dynamic operation, so transient performance is key. Currently applied control methods require tuning of an increasing number of look-up tables and of parameters in the applied models. Especially for transient control this state-of-the-art calibration process is unsystematic and requires a large development effort. Also, embedding models in a controller can set challenging requirements to production control hardware. In this work, we assess the potential of Machine Learning to dramatically reduce the calibration effort in transient air path control development. This is not only done for the existing benchmark controller, but also for a new preview controller. In order to efficiently realize preview, a strategy is proposed where the existing reference signal is shifted in time. These reference signals are then modeled as a function of engine torque demand using a Long Short-Term Memory (LSTM) neural network, which can capture the dynamic input–output relationship. A multi-objective optimization problem is defined to systematically select hyperparameters that optimize the trade-off between model accuracy, system performance, calibration effort and computational requirements. This problem is solved using an exhaustive search approach. The control system performance is validated over a transient driving cycle. For the LSTM-based controllers, the proposed calibration approach achieves a significant reduction of 71% in the control calibration effort compared to the benchmark process. The expert effort and turbocharger experiments used in calibrating transient compensation maps in physics-based feedforward controller are replaced by little simulation time and parametrization effort in ML-based controller, which requires significantly less expert effort and system knowledge compared to benchmark process. The best trade-off between multi-objective cost terms is achieved with one layer and 32 cells LSTM neural network for both non-preview and preview control. For non-preview control, a comparable control system performance is achieved with the LSTM-based controller, while 5% reduction in cumulative NOx emissions and similar fuel consumption is achieved with preview controller.

Hardware-in-the-loop testing of control of a precooled desiccant air-cooling system

Increasing the energy efficiency of cooling in buildings is an important component of the management of global energy consumption. A super-efficient cooling system based on the evaporation concept has been developed, and initial simulation results using the MATLAB/Simulink software tool have already been published by our team. In this paper, we present the results of hardware-in-the-loop (HIL) testing of the real-time controller for the cooler. HIL testing is an engineering process in which the actual controller hardware and software are implemented and interfaced with a real-time simulated model of the controlled system. Using HIL testing, many real-world problems can be fixed before testing on the actual prototype. The controller design is implemented on a small-footprint industrial PC with CODESYS RTE and application code, while control software is implemented using IEC 61131-3 programming languages. Similarly, a real-time thermodynamic and input–output variable-based model of the room, environment, and cooler and its mechanical components (sensors and actuators) are modeled using another industrial PC with the same software tools. HIL test results show very good agreement with the offline simulations.

5-axes Levitation of a Rotor by Active Magnetic Bearings Employing Direct Output Feedback Control

In recent years the application of active magnetic bearings (AMB), as supporting element as well as vibration controller for rotors, has attracted considerable attention from researchers. In this work an attempt has been made for the complete levitation of a rotor weighing 4 kg, supported on two radial active magnetic bearings and two axial passive magnetic bearings (PMB) resulting in 5-axes levitation. The active bearings working in the mode of attraction make the system inherently unstable. Herein, a direct output feedback control scheme based on proportional and derivative of position signal has been employed to achieve stability. The active magnetic bearings consist of electromagnetic actuators, proximity probes for position measurement, feedback controller and current feedback type power amplifier. The passive bearings consisting of axially magnetized circular permanent magnets work in the mode of repulsion. The design of the electromagnetic actuator has been carried out considering load carrying capacity of the AMB, core loss and the resistive loss in the coil, so that the temperature rise in the core as well as in the coil is within allowable limit. Design of the feedback controller is based on direct output feedback control scheme, which makes the controller design easier and implementation of the controller hardware becomes simpler. The feedback controller parameters have been selected appropriately to generate required stiffness and damping in the AMBs for stable levitation of the rotor. The rotor has been driven up to a speed of 2000 rpm.

Intelligent Complementary Terminal Sliding Mode Using Multiloop Neural Network for Active Power Filter

This paper presents a new intelligent sliding mode control approach to effectively achieve harmonic suppression of an active power filter (APF). An intelligent complementary terminal sliding mode controller (CTSMC) is proposed to improve the control accuracy of current loop and deal with the lumped disturbances in the system. Complementary terminal sliding mode control combines the features of complementary sliding mode control (CSMC) and terminal sliding mode control (TSMC) and does not depend on an accurate dynamic model. In addition, in order to reduce chattering, a multi-loop neural network (MLNN) whose parameter learning laws of MLNN are derived based on the Lyapunov laws are proposed to approximate the unknown nonlinear function term in the APF dynamic model, thus reducing the burden of sliding mode control. Finally, within the computing power of control board, detailed simulation and hardware experiments are carried out to demonstrate it has better harmonic suppression, steady-state and dynamic performance compared with the existing methods.

The Ground Test System of Double Layer Air Floating Platform and Its Application

The suspension test system within the high microgravity scientific experiment cabinet is equipped with double-layer vibration isolation, effectively isolating external disturbances and creating a high microgravity environment within the inner body. Based on this characteristic, this paper proposes a double-layer plane air platform ground test system design. By using a high-precision acceleration sensor and gyroscope of the suspension test platform, the system coordinates the measurement of the double-layer air floating platform. The control mode and algorithm for double-layer vibration isolation in the suspension test platform are simulated through ground physics. Ground verification results from the double-layer plane air flotation test system show that the double-layer attitude control hardware of the suspension test platform meets the requirements, and the algorithm control effect is good, which provides sufficient experimental evidence for the normal operation of the suspension test system in orbit.

Design and Development of Ground Wire Repair Robot for Transmission Line

To ensure the normal and stable operation of the power system, it is important to repair broken and loose strand lines in a timely manner. In view of the limitations and shortcomings in repairing broken and loose strands of overhead transmission lines, this paper designs a set of robots that can automatically repair overhead transmission lines online. First, research and design for the mechanical system of the ground wire repair device for overhead transmission lines, including the walking system and the pre-twisted wire winding system; then develop corresponding software and hardware control system, including the data, image transmitting, receiving modules and the operating system of the device, and develop corresponding control devices, mobile phone software and other wireless terminal control systems. The ground wire repair device of the overhead transmission line developed by the project meets the basic requirements for the repair of broken and loose strands of the line, solved a series of problems caused by current manual repair methods, improves the automation of the operation, and has the significance of promotion and application.

Control Design and Flight Test of Aerodynamics-Driven Monoplane–Biplane Morphing Aircraft

Increasing the aspect ratio of the wing of an aircraft reduces drag and increases its range and endurance. However, a large wingspan complicates takeoff and landing. Therefore, an aerodynamics-driven monoplane–biplane morphing (MBM) aircraft was proposed, which cruises as a high-aspect-ratio monoplane and takes off and lands as a biplane, switching between two patterns during flight. This concept of MBM was validated in our previous work via wind tunnel experiments. We further develop it beyond this phase and introduce its applications under real flight conditions here. We constructed a test plane with a wingspan of 6 m and conducted a series of flight experiments on it. To examine its performance and facilitate its initial flight test, we built aerodynamic and dynamic models of the aircraft to analyze its morphing behavior through simulations. The corresponding controller algorithm and hardware system were also developed. The successful implementation of these systems enables bidirectional morphing during flight from a large-span monoplane to a half-span biplane. The test results, comprising data and photographs, validated the success of these trials. The concept of MBM, the corresponding models, and the control algorithm for morphing were thus verified.

Metabolic cost of walking with electromechanical ankle exoskeletons under proportional myoelectric control on a treadmill and outdoors.

Lower limb robotic exoskeletons are often studied in the context of steady state treadmill walking in a laboratory environment. However, the end goal for exoskeletons is to be used in real world, complex environments. To reach the point that exoskeletons are openly adopted into our everyday lives, we need to understand how the human and robot interact outside of a laboratory. Metabolic cost is often viewed as a gold standard metric for measuring exoskeleton performance but is rarely used to evaluate performance at non steady state walking outside of a laboratory. In this study, we tested the effects of robotic ankle exoskeletons under proportional myoelectric control on the cost of transport of walking both inside on a treadmill and outside overground. We hypothesized that walking with the exoskeletons would lead to a lower cost of transport compared to walking without them both on a treadmill and outside. We saw no significant increases or decreases in cost of transport or exoskeleton mechanics when walking with the exoskeletons compared to walking without them both on a treadmill and outside. We saw a strong negative correlation between walking speed and cost of transport when walking with and without the exoskeletons. In the future, research should consider how performing more difficult tasks, such as incline and loaded walking, affects the cost of transport while walking with and without robotic ankle exoskeletons. The value of this study to the literature is that it emphasizes the importance of both hardware dynamics and controller design towards reducing metabolic cost of transport with robotic ankle exoskeletons. When comparing our results to other studies using the same hardware with different controllers or very similar controllers with different hardware, there are a wide range of outcomes as to metabolic benefit.

Design of a Multi-Mode Hybrid Micro-Gripper for Surface Mount Technology Component Assembly.

In the last few decades, industrial sectors such as smart manufacturing and aerospace have rapidly developed, contributing to the increase in production of more complex electronic boards based on SMT (Surface Mount Technology). The assembly phases in manufacturing these electronic products require the availability of technological solutions able to deal with many heterogeneous products and components. The small batch production and pre-production are often executed manually or with semi-automated stations. The commercial automated machines currently available offer high performance, but they are highly rigid. Therefore, a great effort is needed to obtain machines and devices with improved reconfigurability and flexibility for minimizing the set-up time and processing the high heterogeneity of components. These high-level objectives can be achieved acting in different ways. Indeed, a work station can be seen as a set of devices able to interact and cooperate to perform a specific task. Therefore, the reconfigurability of a work station can be achieved through reconfigurable and flexible devices and their hardware and software integration and control For this reason, significant efforts should be focused on the conception and development of innovative devices to cope with the continuous downscaling and increasing variety of the products in this growing field. In this context, this paper presents the design and development of a multi-mode hybrid micro-gripper devoted to manipulate and assemble a wide range of micro- and meso-SMT components with different dimensions and proprieties. It exploits two different handling technologies: the vacuum and friction.

Nonlinear Inflatable Actuators for Distributed Control in Soft Robots.

As soft robotic systems grow in complexity and functionality, the size and stiffness of the needed control hardware severely limits their application potential. Alternatively, functionality can be embodied within actuator characteristics, drastically reducing the amount of peripherals. Functions such as memory, computation, and energy storage then result from the intrinsic mechanical behavior of precisely designed structures. Here, actuators are introduced with tunable characteristics to generate complex actuation sequences from a single input. Intricate sequences are made possible by harnessing hysteron characteristics encoded in the buckling of a cone-shaped shell incorporated in the actuator design. A large variety of such characteristics are generated by varying the actuator geometry. This dependency is mapped and used for creating a tool to determine the actuator geometry that yields a desired characteristic. Using this tool, a system with six actuators is created that plays the final movement of Beethoven's Ninth Symphony with a single pressure supply.

Design and Implementation on Multi-Function Smart Wheelchair

With the increase of population aging, chronic diseases and accidental injuries, more and more people are facing the plight of diminished or even lost walking ability. As a kind of mobile service robot, the smart wheelchair has strong environmental adaptability, smooth motion control and friendly human-computer interaction experience, and is an indispensable tool in rehabilitation engineering and elderly assistance engineering, which has important research value and social significance. In this paper, the system structure framework of the multi-function smart wheelchair and the specific technical scheme of each module are formulated based on the modular design idea, and the intelligent control hardware platform with Arduino UNO controller as the core is built to complete the control software writing, and an intelligent wheelchair for the elderly and disabled guardianship is designed. This multi-function smart wheelchair combined with IoT technology can realize distance navigation, obstacle avoidance, medication reminder, sign measurement, remote monitoring, GPS positioning, and upload information through OneNET cloud platform, which can view the location of the wheelchair and the safety status of the elderly from the map in real time in the cell phone APP and PC backstage, in order to improve the control and monitoring capability of the wheelchair.

Inverse Kinematics of an Anthropomorphic 6R Robot Manipulator Based on a Simple Geometric Approach for Embedded Systems

This manuscript presents an efficient algorithm for solving the inverse kinematics problem of a 6R robot manipulator to be deployed on embedded control hardware. The proposed method utilizes the geometric relationship between the end-effector and the base of the manipulator, resulting in a computationally efficient solution. The approach aims to minimize computational complexity and memory consumption while maintaining the accuracy and real-time performance demonstrated by simulations and verified by experimental results on an embedded system. Furthermore, the manipulator is analyzed in terms of singularities, limits, the workspace, and general solvability. Due to the simplicity of the algorithm, a platform-independent implementation is possible. As a result, the average calculation time is reduced by a factor of five to eight and the average error is decreased by a factor of fifty compared to a powerful analytical solver.

Stability Guaranteed Active Noise Control: Algorithms and Applications

Stability of the controller, robust stability of the active noise control (ANC) system, and limited output power of the hardware control board or actuators are crucial issues for ANC applications. Violation of these requirements results in output saturation, and hence nonlinear distortion by overloading the actuators. As a consequence, noise reduction performance of an ANC system will be dramatically degraded. A novel frequency-weighted leaky filtered-x least-mean-square (FxLMS) algorithm is developed in this article to fulfill these requirements. The proposed algorithm is an improvement of the traditional leaky FxLMS algorithm, where a frequency-weighted matrix is designed to replace the corresponding scalar weight. The frequency-weighted matrix, which is achieved by solving a constrained optimization problem, consists of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\it N$</tex-math> </inline-formula> frequency-weighted factors. By the frequency-weighted factors, the frequency response of the control filter is only suppressed at specific frequencies where the requirements on stability and/or limited output power are violated. Thus, the proposed control algorithm achieves better noise reduction performance than the traditional leaky FxLMS algorithm, which unnecessarily suppresses the control filter at all the frequencies. Simulations and experiments are carried out in a duct ANC system to verify the effectiveness of the proposed algorithm for both the feedforward and feedback ANC systems.

Magnetic Poles Position Detection of Permanent Magnet Linear Synchronous Motor Using Four Linear Hall Effect Sensors

Magnetic pole position detection is the core of the closed-loop control system of the permanent magnet linear synchronous motor (PMLSM), and its position estimation accuracy directly affects control performance and dynamic response speed. In order to solve the problem of the increased estimation error of magnetic pole position caused by magnetic field distortion at the end of PMLSM while also considering the cost of control hardware, the paper uses four linear Hall sensors as magnetic pole position detection components and adopts an optimized estimation algorithm to improve the dynamic performance of the motor. Firstly, a numerical simulation of the magnetic field of poles was conducted using Ansoft Maxwell software, and combined with theoretical analysis, the optimal installation position range of four linear Hall orthogonal placements relative to the motor was obtained. Meanwhile, based on the existing vector tracking position observer, an improved observer detection model is proposed. The Matlab/Simulink software was used to compare the Hall-based detection model with the Hall-based improved observer detection model, verifying the feasibility of the improved detection algorithm. Finally, the rationality of the spatial layout design of linear Hall and the feasibility of improving the estimation algorithm were verified through experiments.

Robust and Efficient Hamiltonian Learning

With the fast development of quantum technology, the sizes of both digital and analog quantum systems increase drastically. In order to have better control and understanding of the quantum hardware, an important task is to characterize the interaction, i.e., to learn the Hamiltonian, which determines both static and dynamic properties of the system. Conventional Hamiltonian learning methods either require costly process tomography or adopt impractical assumptions, such as prior information on the Hamiltonian structure and the ground or thermal states of the system. In this work, we present a robust and efficient Hamiltonian learning method that circumvents these limitations based only on mild assumptions. The proposed method can efficiently learn any Hamiltonian that is sparse on the Pauli basis using only short-time dynamics and local operations without any information on the Hamiltonian or preparing any eigenstates or thermal states. The method has a scalable complexity and a vanishing failure probability regarding the qubit number. Meanwhile, it performs robustly given the presence of state preparation and measurement errors and resiliently against a certain amount of circuit and shot noise. We numerically test the scaling and the estimation accuracy of the method for transverse field Ising Hamiltonian with random interaction strengths and molecular Hamiltonians, both with varying sizes and manually added noise. All these results verify the robustness and efficacy of the method, paving the way for a systematic understanding of the dynamics of large quantum systems.