Dynamic Control System Research Articles

Design and verification of high-precision dynamic temperature control system

In view of the fact that the current temperature environment simulation equipment is difficult to realize the temperature offset test of temperature-sensitive scientific payloads in deep space exploration, a high-precision dynamic temperature control system was designed and built, and then tested and verified. The system uses liquid nitrogen cold plate as the cold source, and realizes high-precision static 300 mm × 300 mm and dynamic temperature control on the contact surface of the payload through electric heating temperature control, heat pipe heat transfer and firmware heat conduction. The test results show that in the case of scientific payload-10 ∘C ∼ 45 ∘CWithin the working temperature range, the contact surface between the temperature control system and the scientificpayload can achieve high-precision temperature stability control, and the temperature fluctuation is better than ± 0.001 ∘C; at the same time, it can also realize ± 0.05 ∘C/1000 s ∼ 1 ∘C/1000 slinear control of the heating and cooling rate of the scientific payload contact surface. Compared with the temperature curve set by the temperature offset test, the maximum errors of the whole temperaturetracking are 0.003 ∘C and respectively 0.04 ∘C . The established temperature control system has the ability of high-precision temperature dynamic offset test, and has considerable application prospects in tasks such as deep space exploration payload development test.

A single-cell 3D dynamic volume control system for chondrocytes.

In articular cartilage, zone-specific cellular morphology is a typical characteristic of cartilage tissue, which is related with chondrocyte function, inflammation and osteoarthritis (OA). Chondrocyte hypertrophic phenotype is a criticle physiological process which indicates a hallmark of chondrocyte terminal differentiation and bone formation. Thus, developing a in vitro cell culture system for dynamic regulation of single chondrocyte volume at a three-dimensional (3D) level is particularly necessary for understanding how physical cues of matrix microenvironment regulate chondrocyte fate and the degeneration of articular cartilage. Here, based on the soft lithography techniques, we have constructed well-defined single-cell 3D dynamic volume control system to recapitulate the physiological matrix microenvironment of single chondrocyte niche. The results of finite element analysis indicated that the stress and strain distribution in the cell culture region is homogeneous during the stretching process. Additionally, 3D dynamic volume expansion and compression of single cells in physiological or hyperphysiological can be realized in this cell culture system. Our device for single-cell 3D dynamic culture provides a microphysiological culture system for chondrocytes to explore the mechanisms of cartilage hypertrophy, as well as develops a new paradigm for functional cartilage tissue engineering and regenerative medicine.

Research on the potential of integrated vehicle control with closed-loop phase portrait analysis

The phase portrait is an important tool for analyzing vehicle stable region widely utilized in vehicle dynamic control systems. At handling limits, traditional open-loop vehicle stable region constraints will restrict the vehicle control ability, while the closed-loop actuator interventions can help states outside the open-loop stable region converge back to stable origin, which is more suitable for constraining vehicle states under extreme working conditions. This article systematically analyzes the potential of integrated vehicle control with closed-loop phase portrait. Firstly, a vehicle dynamic model considering actuator dynamics and tire transient characteristics is established. Then, a numerical optimal control method for calculating closed-loop controllability region is introduced. The controllability region under different friction coefficients, vehicle speeds, convergence time, vehicle steering response characteristics, actuator time constants, and tire transient characteristics are discussed and analyzed. In terms of different control inputs combinations, this article displays the controllability regions under different front steering angles, rear steering angles, direct yaw moments, and their synergistic performances. Control input laws derived from numerical optimization are summarized in two specific directions. Finally, a comparative analysis of open-loop stable regions and closed-loop controllability regions confirms the superiority of the proposed method. Potential applications of the systematic analysis are also discussed.

An artificial intelligence based self-adaptive dynamic process control system for enhancing in-situ bioremediation of benzene-contaminated groundwater – Part I methods

This study develops a new AI-based self-adaptive dynamic process control (SADPC) system based on stepwise inference combined with genetic algorithm optimization technologies, including a filtered-clustering inference prediction model (FCI simulator), a stepwise inference controller (SI emulator), a model predictive control controller, and two optimizers. This system effectively reflects the dynamics and complexity of the bioremediation process and controls the remediation system based on the feedback information. To achieve this goal, a statistical model for simulating the enhanced bioremediation process through the FCI simulator is proposed, which can predict the resulting contamination situation based on the previous contamination situation and pumping/injecting flow rate. Then a bridge is established through the SI emulator, which can generate a control action based on a given contamination situation. This suggested decision makers that guidelines and policies on remediation-oriented SADPC systems could be tentatively investigated, developed, and applied in the future effort.

Self-learning and autonomously adapting manufacturing equipment for the circular factory

Abstract The integration of both linear and circular processes in one production system poses significant challenges. In particular, the reprocessing of end-of-life products is associated with uncertainties at all levels of the production system, from the initial planning and control through to the executing production hardware and intralogistics. To address these challenges, this article presents approaches for self-learning and autonomously adapting production equipment for the Circular Factory. Initially, hardware and software solutions are developed to cover the necessary processes. Reprocessing is covered by modular and reconfigurable manufacturing cells, which also include new process chains such as the combination of additive-subtractive processes. The provided capabilities must be applied to ever new products, for example by transferring human procedures for unknown products to the production equipment. Lastly, an overall robust and dynamic production planning and control system is developed that maintains continuous operation even in unforeseen situations. The resulting highly dynamic overall system is connected by an autonomous intralogistics system.

U-control – a universal platform for control system design with inversion/cancellation of nonlinearity, dynamic and coupling through model-based to model-free procedures

This paper presents the key components in the Universal(U)-control framework for designing dynamic control systems from model-based to model-free paradigms, in which the pillars include U-control system configuration, open-loop dynamic inversion, closed-loop dynamic inversion, general model-free cancellation of nonlinear-dynamic-coupling effect, model-free-sliding-mode control (MFSMC) and model-free composite nonlinear feedback (CNF) control. This paper is devoted to delivering intuitive and easy-to-understand explanations for the involved approaches with a series of schematic diagrams. Readers can refer to the corresponding publications for more details on the theoretical analyses. In addition, some showcase examples are provided in this paper to facilitate the understanding of certain important concepts.

Retraction notice: Emotion detection on webpages using biosensors integrated to a window-based dynamic control system

Retraction notice: Emotion detection on webpages using biosensors integrated to a window-based dynamic control system

Research on energy consumption optimization of predictive cruise control considering the state of the leading vehicle

To comprehensively assess the economic performance of electric vehicles (EVs) cruise planning and mitigate conflicts with leading vehicles, a dynamic programming-based predictive cruise speed optimization control system was developed. This system comprises two layers: an upper V2X-based LSTM-DP cruise speed planning system and a lower adaptive cruise control system based on sliding mode control (SMC), and the mode switching strategy of the speed and distance tracking controller is designed. Initially, a segmented uniform variable motion algorithm processed the historical driving data of the leading vehicle, significantly enhancing the LSTM neural network's acceleration prediction capability, particularly on inclines, with an improvement in accuracy of 37.8 %. Subsequently, an adaptive cruise tracking controller was implemented to track the planned vehicle speed. Simulation tests on actual roads demonstrated that the LSTM-DP approach not only ensures driving safety but also markedly improves fuel efficiency over conventional constant speed cruising methods when planning is effective. Moreover, even in scenarios where planning fails, the LSTM-DP strategy can still achieve energy savings of up to 37 % without compromising the safety distance between vehicles. Finally, hardware-in-the-loop (HIL) testing confirmed the system's efficacy under real-time operational conditions.

Data-driven plant-model mismatch detection for dynamic matrix control systems using sum-of-norms regularization

This article addresses the plant-model mismatch detection problem for linear multiple-input and multiple-output systems operating under the constrained dynamic matrix control (DMC) with the assumption of unknown noise models. An autocovariance-based mismatch detection method that uses sum-of-norms regularization is proposed, aiming to detect parameter jumps and estimate the noise model separately. The intention of introducing regularization is not only to be able to segment the mismatch so that the mismatch is piece-wise constant in time, but also to make the method robust to colored noise. Moreover, a method to alleviate mis-detection caused by unknown operating conditions is proposed. We show that the method can detect significant jumps in parameters and thus provide a priori knowledge for system re-identification and timing of updating the model. Finally, the feasibility of the proposed method under closed-loop conditions is analyzed from a stochastic perspective and demonstrated with illustrative examples.

Comparison between Genetic Algorithms of Proportional–Integral–Derivative and Linear Quadratic Regulator Controllers, and Fuzzy Logic Controllers for Cruise Control System

One of the most significant and widely used features currently in autonomous vehicles is the cruise control system that not only deals with constant vehicle velocities but also aims to optimize the safety and comfortability of drivers and passengers. The accuracy and precision of system responses are responsible for cruise control system efficiency via control techniques and algorithms. This study presents the dynamic cruise control system model, then investigates a genetic algorithm of the proportional–integral–derivative (PID) controller with the linear quadratic regulator (LQR) based on four fitness functions, the mean squared error (MSE), the integral squared error (ISE), the integral time squared error (ITSE) and the integral time absolute error (ITAE). Then, the response of the two controllers, PID and LQR, with the genetic algorithm was compared to the response performance of the fuzzy and fuzzy integral (Fuzzy-I) controllers. The MATLAB 2024a program simulation was employed to represent the system time response of each proposed controller. The output simulation of these controllers shows that the type of system stability response was related to the type of controller implemented. The results show that the Fuzzy-I controller outperforms the other proposed controllers according to the least Jmin function, which represents the minimum summation of the overshoot, settling time, and steady-state error of the cruise control system. This study demonstrates the effectiveness of driving accuracy, safety, and comfortability during acceleration and deceleration due to the smoothness and stability of the Fuzzy-I controller with a settling time of 5.232 s and when converging the steady-state error to zero.

Web-based Internet of Things on environmental and lighting control and monitoring system using node-RED, MQTT and Modbus communications within embedded Linux platform

This paper presents our innovative approach in the field of Internet of Things (IoT). Our focus was on enhancing the research environment at Cheng Shiu University's laboratory through the creation of a dynamic network monitoring setup and lighting control system. Our aim was to simplify equipment management and promote energy efficiency. The core of our development is the Raspberry Pi 4B embedded Linux platform, which serves as a host for both a Message Queuing Telemetry Transport (MQTT) broker and a Node-RED server. Python programs, developed in PyCharm IDE, utilize multiple threads for efficient data exchange via Modbus TCP and MQTT protocols, facilitating seamless interaction between PLCs and MQTT broker servers. The Node-RED IoT development tool was instrumental in crafting a cross-platform Human-Machine Interface (HMI) web server with MQTT Publish/Subscribe capabilities, ensuring smooth communication with the MQTT broker server. By configuring the Apache HTTP server to direct to the Node-RED web directory, we can effectively monitor laboratory conditions and manage the lighting system. The experimental results validate the cost-effectiveness of our approach in transforming traditional control systems into a web-based supervisory control system, thereby enhancing the overall functionality of existing equipment. This project underscores our commitment to advancing IoT solutions in practical settings.

Solar powered high gain DC-DC converter with FPGA controller for portable devices

A solar-powered converter design is necessary for portable devices with increased gain and reduced ripple in overall operation. This research presents a photovoltaic-powered Field Programmable Gate Array (FPGA) based on increased gain multiple output converter design and analysis. The dynamic panel input makes it difficult to operate the load. To control and make the input static to satisfy the demand, perturb and observe with a load balancing algorithm technique are used. The converter gained output is high, and batteries are used to balance the loads when solar irradiance is low. Simulation results were carried out with the presented converter circuit that can be operated with a 12 V supply as input from a renewable power source. The output gain ratio of 14 to a minimum of 40 % duty cycle operating at a switching frequency of 20 kHz has been achieved. The proposed methodology is transferred to hardware by connecting with the reconfigurable FPGA Spartan-6 development board to manage the dynamic load control system's complex logic and high processing ability. The overall performance is compared with various pre-existing systems, tabulations are made with the analysis, and experimental results are also illustrated at the end.

Dynamic Reservation-Based Traffic Control System to Integrate VTOL Vehicle Operations in Connected Vehicle Surface Traffic

Dynamic Reservation-Based Traffic Control System to Integrate VTOL Vehicle Operations in Connected Vehicle Surface Traffic

Real-time estimation of vehicle inertia parameters based on Kalman-bucy filter

Vehicle inertial parameters such as mass and moments of inertia are required for most vehicle dynamic control systems. Due to the wide range variation of these parameters during vehicle operation, accurate estimation of their values in real-time plays an important role in improving the efficiency of vehicle control systems. In this article, the vehicle sprung mass and moments of inertia are estimated in real-time based on a Kalman-Bucy filter algorithm designed for a spatial vibration model of a two-axle truck. This proposed method requires measuring only the vertical, roll, and pitch velocity of the sprung mass and, therefore can reduce the sensor cost significantly. The simulation results for a random roughness road profile according to ISO 8608 class C with step variations in sprung mass and moments of inertia showed that the designed estimator rejected the process and measurement noises and tracked the real vehicle parameters effectively with acceptable errors

Research on Dynamic Gesture Recognition and Control System based on Machine Vision

Hand gesture recognition and control is a new type of human-computer interaction that can provide a more convenient and efficient operation mode by utilizing non-contact gesture recognition technology. This paper presents a lightweight dynamic gesture recognition method for intelligent office presentation control. First, we introduce the concept of hand gesture recognition and go over key gesture recognition technologies like classification. The structure, process, and evaluation index of the gesture recognition algorithm are described in detail using a convolutional neural network model. During the experiment's algorithm verification phase, we test and analyze the algorithm using the Python language, compilation environment, and data set. In the control experiment, we evaluated the system's ability to control the office application's start, play, next, previous, and exit functions. We achieve 96.3% accuracy on the test set. Experimental results show that the system can recognize a wide range of hand gestures and accurately control the presentation.

Optimization of dynamic control systems using water cycle algorithm

In this paper, path optimization in dynamic control systems was performed using water cycle algorithm. The core principles and assumptions that guide the suggested technique are drawn from nature and are based on observations of the water cycle process and how rivers and streams naturally flow into the sea. The Water Cycle Algorithm has been recognized as a reliable method for dealing with large-scale optimization challenges, largely because of its proficient exploitation and exploration capabilities. This Algorithm does not require the continuity of functions when specifying the issue, unlike many gradient-based approaches. This is achieved through the use of a simple mathematical model that represents the water cycle process. This model is used to simulate the movement of water droplets in a cloud, where each water droplet represents a potential solution to the optimization problem. Although water cycle optimization has been utilized successfully to tackle optimization issues, this is the first time dynamic control optimization has been used. The control problem is switched out for an optimum programming problem in order to find optimal pathways utilizing the Shifted Chebyshev polynomial. The water cycle method was then used to resolve the new issue. The numerical results demonstrate the excellent precision with which this search method can discover solutions.

Research on the characteristics of EV interior sound quality and its dynamic active control system design and development un-der accelerated driving conditions.

In order to improve the interior sound quality of Electric Vehicles (EV), solve the problem of low sense of power and comfort of the interior sound as well as the large electromagnetic excitation order noise of motor and the sharp interior sound, this article designs a dynamic active sound control system for EV under accelerated driving conditions. Firstly, by comparing and analyzing the sound spectrum characteristics of fuel vehicle (FV) and EV during acceleration, a short-time Fourier transform (STFT) is adopted to extract and synthesize the engine sound. Secondly, the influence of the engine order composition and the energy distribution in the frequency domain on the sound quality of the vehicle is analyzed, and an active control system for sound quality is proposed. And the software and hardware development of the active control sound system is completed. Finally, through real-vehicle testing and verification, the sense of comfort and power of the EV interior sound has been greatly improved during acceleration, and the total value of interior sound can meet the requirement. The sound pressure level and loudness of interior sound have been increased, and the sharpness of the sound inside the vehicle has been improved, with a maximum reduction of 1.0acum.

Contextualizing Interpersonal Data Sharing in Smart Homes

A key feature of smart home devices is monitoring the environment and recording data. These devices provide security via motion-detection video alerts, cost-savings via thermostat usage history, and peace of mind via functions like auto-locking doors or water leak detectors. At the same time, the sharing of this information in interpersonal relationships---though necessary---is currently accomplished on an all-or-nothing basis. This can easily lead to oversharing in a multi-user environment. Although prior work has studied people's perceptions of information sharing with vendors or ISPs, the sharing of household data among users who interact personally is less well understood. Interpersonal situations make data sharing much more context-based and, thus, more complicated. In this paper, we use themes from the theory of contextual integrity in an online survey (n=1,992) to study how people perceive data sharing with others in smart homes and inform future designs and research. Our results show that data recipients in a smart home can be reduced to three major groups, and data types matter more than device types. We also found that the types of access control desired by users can vary from scenario to scenario. Depending on whom they are sharing data with and about what data, participants expressed varying levels of comfort when presented with different types of access control (e.g., explicit approval versus time-limited access). Taken together, this provides strong evidence that a more dynamic access control system is needed, and we can design it in a more usable way.

New evacuation sign "NO ENTRY"

This study addresses concerns about the effectiveness of conventional evacuation signs in directing occupants during emergencies, particularly in situations involving obstacles like smoke, flames, or high temperatures. The research explores innovative solutions to enhance safety during evacuations, focusing on two main approaches: the Active Dynamic Signage System (ADSS) and the dynamic evacuation control system. The study introduces the concept of 23 different pictograms utilizing LED technology, with the "no-entry" sign receiving the highest rating during evaluations.
 The research emphasizes the importance of dynamic evacuation control systems, suggesting the integration of signs forbidding entry into hazardous spaces. The study concludes that efficient evacuation is crucial for safety culture, necessitating a discussion on the introduction of a new pictogram – the NO ENTRY sign – into the evacuation sign catalog. The authors advocate for the adoption of a dynamic evacuation lighting system and offer an open license for the proposed pictograms to encourage broad discourse.

Estimation of vehicle tire effective rolling radius and vertical wheel/road contact force

In a vehicle, knowledge of physical wheel/road contact parameters is valuable for the design of dynamic control and active safety systems. In this paper, a new method is designed to estimate the effective rolling radius and the vertical wheel/road contact force using an extended Kalman filter and the Pacejka magic formula. Moreover, it allows the estimation of the longitudinal force, the rolling resistance force, and the longitudinal slip. The method is designed in four blocks. First, a comparison with other research is carried out to validate both the controller and quarter-vehicle model blocks. Then, the blocks of the extended Kalman filter and the Pacejka magic formula are applied to estimate all parameters. The relative mean estimation errors are calculated, and the results show that the developed method is capable of estimating all parameters simultaneously and in real-time with satisfactory accuracy.