Control System Architecture Research Articles

An Internet of Things—Supervisory Control and Data Acquisition (IoT-SCADA) Architecture for Photovoltaic System Monitoring, Control, and Inspection in Real Time

The Internet of Things (IoT) serves as a key component to enhance operational efficiency and decision-making in the context of supervisory control and data acquisition (SCADA) systems. Featuring the improved system robustness and real-time parameters, including images of the load, a new design of SCADA system monitoring for a photovoltaic (PV) system based on dual IoT platforms is proposed in this paper. Two voltage sensors collect the voltages of the PV module and the battery, while three current sensors accumulate the current data from the PV module, the battery, and the load. ESP32-E assembles the data and then transmits them to the Arduino Cloud via MQTT for real-time display and ESP32-S3 via HTTP. The relay and the load are controlled by ESP32-E to turn ON/OFF based on the battery voltage as well. In addition, ESP32-S3 forwards the received data to ThingSpeak for advanced analysis, data storage, and real-time display via HTTP. The load images are also displayed on a camera web server built by ESP32-S3. Successfully monitoring for over 20 days, the proposed system demonstrated its robustness and versatility even during the downtime of the Arduino Cloud, with a one-day voltage measurement ranging to a maximum of 13 V and current ranging from zero amperes to 4.42 amperes. To add to this system, it incorporates visual load monitoring features, which are unseen in traditional systems.

Research and Design of Motion Control System Based on Visual Navigation AGV

Automated Guided Vehicles (AGVs) have emerged as a crucial element of intelligent logistics and automated warehousing systems, with enormous potential for a variety of uses, thanks to the quick development of manufacturing and logistics. Given its advantages over other AGV navigation techniques—such as precise location, inexpensive hardware costs, and flexible routing—visual navigation has drawn more attention from researchers. This article examines the visual navigation of AGVs' motion control system within the framework of a smart manufacturing workshop. In this study, the AGV motion control system's general design is described. The algorithm for visual placement is designed and presented second. The study then looks into a fuzzy PID control self-tuning technique. AGV path-tracking error models are created, and MATLAB simulation is used to compare fuzzy PID and traditional PID models and show the advantages and disadvantages of each control technique. The motion control system's functional implementation is finally covered. The external device interface circuits are constructed, and the hardware platform is chosen using the high-performance multi-core video processor, TMS320DM8148. The software architecture of the motion control system is built, and the development of the visual positioning and path-tracking algorithms, along with other functional modules, is finished, all while utilizing the performance benefits of ARM and DSP processors.

Design of and Experiment with a Dual-Arm Apple Harvesting Robot System

Robotic harvesting has become an urgent need for the development of the apple industry, due to the sharp decline in agricultural labor. At present, harvesting apples using robots in unstructured orchard environments remains a significant challenge. This paper focuses on addressing the challenges of perception, localization, and dual-arm coordination in harvesting robots and presents a dual-arm apple harvesting robot system. First, the paper introduces the integration of the robot’s hardware and software systems, as well as the control system architecture, and describes the robot’s workflow. Secondly, combining a dual-vision perception system, the paper adopts a fruit recognition method based on a multi-task network model and a frustum-based fruit localization approach to identify and localize fruits. Finally, to improve collaboration efficiency, a multi-arm task planning method based on a genetic algorithm is used to optimize the target harvesting sequence for each arm. Field experiments were conducted in an orchard to evaluate the overall performance of the robot system. The field trials demonstrated that the robot system achieved an overall harvest success rate of 76.97%, with an average fruit picking time of 7.29 s per fruit and a fruit damage rate of only 5.56%.

Design, simulation and testing of U-type actuator-based single-coil active-magnetic-bearing

This work represents an experimental investigation using testing and modeling for a single-axis U-type Active-Magnetic-Bearing (AMB). To obtain the inductance profile and resistance value respectively, the AC and DC tests are conducted. A load cell test is performed to measure the magnetic attraction force. Further, the U-type actuator’s inductance and force are estimated using finite element analysis software. Using a variety of controllers in MATLAB-Simulink, the control system architecture is demonstrated and contrasted. According to the design data, the hardware system is configured. Finally, the high-speed test is performed satisfactorily for the proposed system at 16656 rpm.

A Reconfigurable Architecture for Industrial Control Systems: Overview and Challenges

The closed architecture and stand-alone operation model of traditional industrial control systems limit their ability to leverage ubiquitous infrastructure resources for more flexible and intelligent development. This restriction hinders their ability to rapidly, economically, and sustainably respond to mass customization demands. Existing proposals for open and networked architectures have failed to break the vicious cycle of closed architectures and stand-alone operation models because they do not address the core issue: the tight coupling among the control, infrastructure, and actuator domains. This paper proposes a reconfigurable architecture that decouples these domains, structuring the control system across three planes: control, infrastructure, and actuator. The computer numerical control (CNC) system serves as a primary example to illustrate this reconfigurable architecture. After reviewing open and networked architectures and discussing the characteristics of this reconfigurable architecture, this paper identifies three key challenges: deterministic control functionality, the decoupling of control modules from infrastructures, and the management of control modules, infrastructures, and actuators. Each challenge is examined in detail, and potential solutions are proposed based on emerging technologies.

Basic principals of the tiltrotors flight control system architecture and algorithms

Basic principals of the tiltrotors flight control system architecture and algorithms

Co-simulation Architecture and Platform Establishment Method for Cloud-Based Predictive Cruise Control System

Co-simulation Architecture and Platform Establishment Method for Cloud-Based Predictive Cruise Control System

Velocity-free event-triggered control for a class of uncertain axis-motion systems with prescribed performance

This paper investigates the fixed-time prescribed performance control problem for a class of axis-motion servo systems subject to the external disturbances and parameter uncertainties. To eliminate the requirement for velocity measurements in the control process, an auxiliary observer is first designed to reconstruct the velocity signal of the controlled plant. Subsequently, an error transformation procedure incorporating a prescribed performance index is integrated into the control system architecture. To overcome the limitations of periodic control design, a robust event-triggered controller is developed, ensuring that the dynamic error of the closed-loop system exhibits fixed-time convergence. Through rigorous theoretical analysis and proofs, it is demonstrated that the proposed event-triggered strategy guarantees Zeno-behavior-free operation. Finally, simulation results are presented to validate the effectiveness of the proposed control algorithm.

Knowledge graph-enhanced multi-agent reinforcement learning for adaptive scheduling in smart manufacturing

AbstractSelf-organizing manufacturing network has emerged as a viable solution for adaptive manufacturing control within the mass personalization paradigm. This approach involves three critical elements: system modeling and control architecture, interoperable communication, and adaptive manufacturing control. However, current research often separates interoperable communication from adaptive manufacturing control as isolated areas of study. To address this gap, this paper introduces Knowledge Graph-enhanced Multi-Agent Reinforcement Learning (MARL) method that integrates interoperable communication via Knowledge Graphs with adaptive manufacturing control through Reinforcement Learning. We hypothesize that implicit domain knowledge obtained from historical production job allocation records can guide each agent to learn more effective scheduling policies with accelerated learning rates. This is based on the premise that machine assignment preferences effectively could reduce the Reinforcement Learning search space. Specifically, we redesign machine agents with new observation, action, reward, and cooperation mechanisms considering the preference of machines, building upon our previous MARL base model. The scheduling policies are trained under extensive simulation experiments that consider manufacturing requirements. During the training process, our approach demonstrates improved training speed compared with individual Reinforcement Learning methods under the same training hyperparameters. The obtained scheduling policies generated by our Knowledge Graph-enhanced MARL also outperform both individual Reinforcement Learning methods and heuristic rules under dynamic manufacturing settings.

A novel system architecture of intelligent adaptive cruise control for safety aspects

Following industrial and safety standards for autonomous vehicles, Adaptive Cruise Control (ACC) is a widely employed Advanced Driving Assistance System (ADAS) feature in modern vehicles. ACC currently facilitates speed control based on the driver's desired speed value. This study introduces a significant advancement: the Intelligent Adaptive Cruise Control (IACC) feature, accompanied by the development of a control system architecture poised to make noteworthy contributions in scientific, economic, and social dimensions through its integration into autonomous vehicles. The design incorporates crucial elements such as Traffic Sign and Limit Recognition (TSLR), ADAS features, and Global Positioning System (GPS) data, primarily enhancing driver safety through these supportive features. The main focus revolves around designing a system architecture that accommodates these new features to ensure safe driving. The creation of the IACC system architecture is approached using Model-Based System Engineering (MBSE). Through this MBSE methodology, system-level diagrams were crafted, and security considerations were systematically addressed. Several scenarios were devised to evaluate the contributions and were subsequently tested and analyzed. The architecture places particular emphasis on the security aspects of IACC. Leveraging the TSLR feature, the system interprets traffic signs and acquires speed limit data from external sources, preventing the vehicle's speed from exceeding the specified limit. The comparison between the set speed value and the speed limit ensures adherence to safety parameters. In such scenarios, the system enhances driver support on winding roads by utilizing GPS data to recognize the vehicle in front. This approach significantly elevates the reliability of the IACC feature, particularly in terms of safety sensitivity, when compared to other adaptive cruise control concepts.

Architectural underpinnings of stochastic intergenerational homeostasis.

Living systems are naturally complex and adaptive and offer unique insights into the strategies for achieving and sustaining stochastic homeostasis in different conditions. Here we focus on homeostasis in the context of stochastic growth and division of individual bacterial cells. We take advantage of high-precision long-term dynamical data that have recently been used to extract emergent simplicities and to articulate empirical intra- and intergenerational scaling laws governing these stochastic dynamics. From these data, we identify the core motif in the mechanistic coupling between division and growth, which naturally yields these precise rules, thus also bridging the intra- and intergenerational phenomenologies. By developing and utilizing techniques for solving a broad class of first-passage processes, we derive the exact analytic necessary and sufficient condition for sustaining stochastic intergenerational cell-size homeostasis within this framework. Furthermore, we provide predictions for the precision kinematics of cell-size homeostasis and the shape of the interdivision time distribution, which are compellingly borne out by the high-precision data. Taken together, these results provide insights into the functional architecture of control systems that yield robust yet flexible stochastic homeostasis.

Conversion of a Coaxial Rotorcraft to a UAV—Lessons Learned

A coaxial helicopter with a maximum take-off weight of 600 kg was converted to an unmanned aerial vehicle. A minimally invasive robotic actuator system was developed, which can be retrofitted onto the copilot seat of the rotorcraft in a short period of time to enable automatic flight. The automatic flight control robot includes electromechanical actuators, which are connected to the cockpit inceptors and control the helicopter. Most of the sensors and avionic components were integrated into the modular robotic system for faster integration into the rotorcraft. The mechanical design of the control system, the development of the robot control software, and the control system architecture are described in this paper. Furthermore, the multi-body simulation of the robotic system and the estimation of the linear low-order actuator models from hover-frame flight test data are discussed. The developed technologies in this study are not specific to a coaxial helicopter and can be applied to the conversion of any crewed flight vehicle with mechanical controls to unmanned or fly-by-wire. This agile development of a full-size flying test-bed can accelerate the testing of advanced flight control laws, as well as advanced air mobility-related functions.

Neural algorithm for optimization of multidimensional object controller parameters

Optimal control of multivariable systems is a complex dynamic process that minimizes the cost function to obtain the optimal control strategy. Unfortunately, for nonlinear systems, it is not possible to use the traditional linear quadratic regulator (LQR), which would be optimal over the entire range of parameter variation. The problem of nonlinear multivariable systems and their optimal control is very momentous. The solution presented in this paper is based on the application of Reinforcement Learning (RL) networks in controlling a five-degree-of-freedom overhead crane system. Additionally, unlike the classical approach, the algorithm is adapted to directly analyze tabular data of inputs and outputs of the controlled model instead of analyzing its state as feedback (model-free). Implementing the new control structure for the multivariable system improved control quality compared to the classical LQR controller with linearization at the operating point. In addition to quality, the resource indicators, which in the LQR controller are represented by the matrix R, have been significantly improved. The architecture of the neural control system is presented, ensuring that over the entire range of nonlinearity, the quality of control is preserved while reducing the cost of its resource intensity. Obtaining optimal control with reduced resources for its implementation induces a wide range of applications of such neural control in engineering systems. The effectiveness of the proposed control system has been demonstrated in simulation studies. The simulation results present the system’s excellent control performance and adaptability over the entire range of object nonlinearity. The neural algorithm resulted in significantly shorter adjustment time and better control quality with significantly less system resource consumption and increased system dynamics.

A Model-Based Strategy for Active Balancing and SoC and SoH Estimations of an Automotive Battery Management System

This paper presents a novel integrated control architecture for automotive battery management systems (BMSs). The primary focus is on estimating the state of charge (SoC) and the state of health (SoH) of a battery pack made of sixteen parallel-connected modules (PCMs), while actively balancing the system. A key challenge in this architecture lies in the interdependence of the three algorithms, where the output of one influences the others. To address this control problem and obtain a solution suitable for embedded applications, the proposed algorithms rely on an equivalent circuit model. Specifically, the SoCs of each module are computed by a bank of extended Kalman filters (EKFs); with respect to the SoH functionality, the internal resistances of the modules are estimated via a linear filtering approach, while the capacities are computed through a total least squares algorithm. Finally, a model predictive control (MPC) was employed for the active balancing. The proposed controller was calibrated with Samsung INR18650-20R lithium-ion cells data. The control system was validated in a simulation environment through typical automotive dynamic scenarios, in the presence of measurement noise, modeling uncertainties, and battery degradation.

3D criteria to improve real-time pressure control in water distribution network system architecture

ABSTRACT Real-time control (RTC) methods have been developed for over one decade to regulate service pressure and reduce leakage using pressure control valves in water distribution networks (WDN). The present study investigates control node selection frameworks and RTC system architecture for the field-oriented control of a rural WDN. The unique topology of the case-study area, the small size of the area, and high-pressure variety increased the considerations of the RTC system. A computer code was developed based on the method of characteristics for the hydraulic simulation of the network. The code could analyze unsteady flows through sloped pipes, implement pressure-based analysis, and regulate control valves based on the target node. Leakage reduction, pressure fluctuation reduction, and cavitation prevention were used as three criteria and constraints in the selection of control nodes. It was found that the optimal strategy would reduce the real water loss from 25 to 10%. Furthermore, key parameters in remote sensing were evaluated to minimize the number of sensors in order to further simplify the control algorithm and RTC system architecture through an artificial neural network (ANN) approach. The control function with a convergence rate of 99% was introduced.

Preliminary architecture design for human-in-the-loop control of robotic equipment in remote handling tasks: Case study on the NEFERTARI project

In this work, we present a general control architecture for robotic systems dedicated to the remote handling of in-vessel components in fusion machines. This control architecture will be tested for the inspection and maintenance of the first wall components of the RFX-mod2 experiment, in the scope of the New Equipment For the Experimental Research and Technological Advancement for the Rfx Infrastructure (NEFERTARI) project. The architecture is split into low-level and high-level layers. The former is used for the low-level control of the robotics systems and to manage safety; the latter is used for the high-level planning and implements several sub-modules, such as a Virtual Reality (VR) environment for the visualisation of the robot’s digital twin. Moreover, an Application Programming Interface (API) is intended to connect the two layers, and the communication between modules and layers is provided by the ROS2 framework. A typical usage of such a framework involves a human operator who is teleoperating the real robot, data from motor encoders are used as inputs for the dynamic model module. This module is used to compute the forward dynamics in real-time, providing an accurate simulation of the robot (digital twin) in the virtual environment.

Full Envelope Flight Control System Design and Optimization for a Tilt-Wing Aircraft

Novel vertical take-off and landing advanced air mobility aircraft which are overactuated and transition between vertical and forward flight modes pose unique challenges for the design of safe and robust full‐envelope fly‐by‐wire flight control systems. This paper presents a methodology for designing and optimizing a control system architecture for a tilt-wing urban air mobility concept. It features the total energy control system algorithm, which is extended to be applicable to hover and transitioning flight and explicit model following inner loops. Control system parameters are optimized using a genetic algorithm optimization scheme, subject to constraints on closed-loop dynamic stability and control response characteristics. Control system performance is demonstrated by simulation of lateral, longitudinal, and directional maneuvering for hover, transition, and forward flight conditions, followed by simulation of departure and arrival transitions while tracking a prescribed flightpath and speed profile in calm and turbulent air. The results demonstrate the effectiveness of the proposed control system architecture and the demonstrated optimization methodology.

Synaptic architecture of leg and wing premotor control networks in Drosophila.

Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles1. MN activity is coordinated by complex premotor networks that facilitate the contribution of individual muscles to many different behaviours2-6. Here we use connectomics7 to analyse the wiring logic of premotor circuits controlling the Drosophila leg and wing. We find that both premotor networks cluster into modules that link MNs innervating muscles with related functions. Within most leg motor modules, the synaptic weights of each premotor neuron are proportional to the size of their target MNs, establishing a circuit basis for hierarchical MN recruitment. By contrast, wing premotor networks lack proportional synaptic connectivity, which may enable more flexible recruitment of wing steering muscles. Through comparison of the architecture of distinct motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.

Underwater Remotely Operated Vehicle Control System Ar-chitecture

Underwater Remotely Operated Vehicle Control System Ar-chitecture

Decentralized, norm-free, and adaptive event-triggered distributed control of nonholonomic mobile robots

The overarching contribution of this paper is a novel event-triggered distributed control architecture for multiagent systems that are composed of a team of nonholonomic mobile robots. Specifically, the robot dynamics are first feedback linearised to equivalently represent each robot with double integrator dynamics and the event-triggering law is then presented. This law is characterised by being decentralised (depending only on own signals of a robot), norm-free (independent of distances for reducing robot-to-robot data transmissions), and adaptive (estimating signals without using global information). Moreover, its system-theo- retical analysis is provided, which holds for both the sampled data exchange method and the solution-predictor curve-based data exchange method. In contrast to the well-studied sampled data exchange, the solution-predictor curve method further reduces robot-to-robot data transmissions by making each agent to store a generic form of this curve and exchange its parameters when an event occurs for approximating the solution trajectory of each robot. Finally, an illustrative numerical example is included to demonstrate the overall architecture.