Virtual Control Research Articles

Enhancing Robot End‐Effector Trajectory Tracking Using Virtual Force‐Tracking Impedance Control

This article presents an extended Cartesian space robot control framework that features a virtual force tracking impedance control to enhance the end‐effector trajectory tracking performance. Initially, the concept of a virtual surface is introduced, which is assumed to be at some constant distance from the desired end‐effector trajectory. This virtual surface generates a virtual contact force when interacting with the torque‐controlled robot end‐effector. The interaction is then manipulated using an impedance control model to track a constant desired force. If the robot end‐effector deviates from the desired trajectory, the constant force‐tracking impedance control generates a compliance trajectory that regulates the end‐effector movements, constraining it to the desired trajectory. For robust force tracking, impedance parameters are optimally tuned using a closed‐loop dynamic model incorporating both robot and impedance dynamics. Additionally, super twisting sliding mode control (STSMC) is integrated to overcome uncertainties and the impact of robot dynamics on force‐tracking performance. Experimental validation confirms the theoretical claims of the proposed approach. It demonstrates that force‐tracking impedance control improves the end‐effector trajectory tracking by quickly reacting to the dynamic trajectories compared to position control only and effectively maintains it on the desired trajectories.

Dual-channel event-triggered prescribed performance adaptive fuzzy time-varying formation tracking control for nonlinear multi-agent systems

This paper is concerned with the problem of dual-channel event-triggered prescribed performance adaptive fuzzy time-varying formation tracking control for multi-agent systems subject to actuator saturation, in which state variables are unmeasurable and nonlinear functions are totally unknown. A fuzzy state observer is constructed to estimate unmeasurable states. Meanwhile, fuzzy logic systems are used to approximate unknown nonlinear functions. To effectively save the usage of communication resources, this paper designs both sensor output signal and control signal triggering mechanisms, respectively. Unfortunately, the output triggering can lead to a problem that virtual control laws are non-differentiable. To solve this problem, we first utilize observer output signals to construct virtual control laws to ensure the first-order differentiation of virtual control laws. Then, a dynamic filtering technology is introduced to avoid the repeated differentiation of virtual control laws. Furthermore, an improved first-order auxiliary system is designed to compensate for the impact of actuator saturation. It is shown that the designed controller can guarantee tracking errors steer to a preset accuracy within a prescribed settling time, and all signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results verify the effectiveness of the developed control scheme.

Study on the Precise Evaluation of Environmental Impacts of Air Pollution in Cold Regions Using the Cost Control Method

Objective: With the acceleration of industrialization, air pollution has become a global environmental issue, particularly in cold regions where the unique climatic and geographical conditions give rise to distinctive types of air pollution and impacts. Considering the economic evaluation of environmental damage is crucial for effective pollution control policies, this study aims to provide a more precise environmental damage assessment method through the Improved Virtual Control Cost Method (IVCCM) to optimize air pollution governance strategies in cold regions. Method: This study utilizes a case study of a major company producing methanol and coal-based natural gas, where the emissions from the boiler exhaust exceeded the prescribed standards for particulate matter, sulfur dioxide, and nitrogen oxides during a specific period. By employing a segmented counting approach that accounts for downtime, precise calculations were conducted for the actual periods of excess emissions. Adjustments were made to the calculation coefficients within the Virtual Control Cost Method to more accurately reflect the ecological damage caused by air pollution. Results: The IVCCM calculations revealed that the total environmental loss caused by the company’s excessive air pollution emissions amounted to USD 1.6844 million, significantly lower than the original calculation method (USD 2.1885 million). Specifically, the environmental losses due to particulate matter, sulfur dioxide, and nitrogen oxides were USD 0.0032 million, USD 0.3600 million, and USD 1.3212 million, respectively. Conclusions: The IVCCM enables a more precise assessment and prediction of ecological environmental damage caused by air pollution in cold regions. Compared to traditional methods, it effectively reduces assessment costs, mitigates disputes arising from unclear parameter values and calculation methods, and facilitates the development of more rational environmental protection policies and measures.

Finite-time SMC-based admittance controller design of macro-micro robotic system for complex surface polishing operations

In the field of robotic polishing, achieving uniform material removal typically involves addressing the issue of constant contact force control. However, multi-source external disturbances in the polishing scenarios of complex workpiece surfaces can severely affect the robot’s force control accuracy. To enhance the responsiveness and disturbance rejection capabilities of robots in the compliant polishing process, this paper proposes an adaptive admittance controller with practical finite-time stability. A virtual control input is introduced into the basic admittance control framework in light of the state space theory, aiming to provide flexibility for common adaptive law designs. On this basis, a robust sliding mode control (SMC) algorithm is proposed to suppress external disturbances. The force tracking error is theoretically proven to achieve finite-time convergence when applying the proposed control strategy. Experimental results across various polishing scenarios demonstrate that, compared with the existing admittance control strategies, the proposed method can reduce fluctuations of the polishing force and improve the surface quality, thus verifying its effectiveness.

Cooperative Load Transportation with Quadrotors using Adaptive RISE Control

The problem of transporting cable-suspended payloads using two quadrotors is analyzed in this work. The system kinematics are treated using a virtual structure formation controller, which generates the acceleration references commanded to the aerial vehicles. The disturbances caused by the payload are treated as unmodeled disturbances and a novel robust integral of the sign of the error (RISE) controller is proposed, guaranteeing the asymptotic convergence of the tracking errors. A model reference adaptive control is also incorporated into the RISE controller, combining the advantages of adaptive and robust control. The proposal is validated by numerous experiments using two quadrotors to transport a bar-shaped payload in adverse conditions. The results allow the conclusion that the proposed system is capable of performing transportation and orientation tasks with the payload, subject to translational accelerations up to 3.5 m/s2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document}.

Analysis of non-uniform grid-forming control techniques for the HVDC connection of renewable energy

Grid-forming control techniques are required to achieve a high penetration of power electronics-based renewable energy sources for decarbonized electric systems. However, different grid-forming control strategies can operate in the same grid, and grid stability shall be warranted. This paper analyses the use of different grid-forming control strategies for diode rectifier-based wind power plants. The analysed grid-forming controllers are the well-known droop control, an advanced droop control, and the virtual synchronous machine control. The controllers’ analysis validates the three grid-forming controllers’ interoperability, identifying each control parameter’s contribution to the stability of each system state variable. Furthermore, the analysis allows a better tuning of the control parameters. Additionally, a fault-ride-through strategy that improves the system restoration after faults is proposed and validated. The proposed fault-ride-through strategy achieves a soft restoration of the active power.

Adaptive Predefined Time Control for Strict-Feedback Systems with Actuator Quantization

An adaptive predefined-time quantized control issue is considered for strict-feedback systems with actuator quantization. To handle the unknown nonlinearities of a system, the neural networks are first applied to model them. To analyze the predefined-time stability under approximation error, a stability lemma is first introduced. Then, a refreshing predefined-time quantized control strategy is presented. Compared with the existing control studies for actuator quantization, the stability time is not influenced by the initial state and can be set in advance. Furthermore, unlike the available predefined-time control studies, a new parameter adaptive law and virtual controllers are designed. This design not only ensures the predefined-time stability, but overcomes the singularities of system in coventional backstepping control design because of repeating differentiation for virtual controllers.

AUTOSAR-Compatible Level-4 Virtual ECU for the Verification of the Target Binary for Cloud-Native Development

The rapid evolution of automotive software necessitates efficient and accurate development and verification processes. This study proposes a virtual electronic control unit (vECU) that allows for precise software testing without the need for hardware, thereby reducing developmental costs and enabling cloud-native development. The software was configured and built on a Hyundai Autoever AUTomotive Open System Architecture (AUTOSAR) classic platform, Mobilgene, and Renode was used for high-fidelity emulations. Custom peripherals in C# were implemented for the FlexTimer, system clock generator, and analog-to-digital converter to ensure the proper functionality of the vECU. Renode’s GNU debugger server function facilitates detailed software debugging in a cloud environment, further accelerating the developmental cycle. Additionally, automated testing was implemented using a vECU tester to enable the verification of the vECU. Performance evaluations demonstrated that the vECU’s execution order and timing of tasks and runnable entities closely matched those of the actual ECU. The vECU tester also enabled fast and accurate verification. These findings confirm the potential of the AUTOSAR-compatible Level-4 vECU to replace hardware in development processes. Future efforts will focus on extending capabilities to emulate a broader range of hardware components and complex system integration scenarios, supporting more diverse research and development efforts.

A Digital Twin System for Adaptive Aligning of Large Cylindrical Components

Most large aerospace cylindrical components still adopt a manual aligning method with low automation, large manual intervention, and heavy dependence on operator workers, resulting in the low quality and efficiency of large component aligning, which seriously prolongs the manufacturing time of aerospace products. To cope with this issue, based on closed-loop adaptive control and digital twin (DT) technologies, an adaptive aligning system for large cylindrical components, i.e., the DT aligning system, is proposed in this study. For the DT aligning system, through the DT multi-dimensional modeling, i.e., geometric modeling, physical modeling, functional modeling, and data modeling, it can be divided into a physical space, a virtue space, and twin data. Note that the association, mapping, and interaction between physical space and virtual space of the aligning system can be realized via the twin data, thereby realizing real-time virtual display, monitoring, and control of the large component aligning. In addition, based on the measured pose data, aligning stress, and predicted aligning error, an adaptive force/position control method for large component aligning is proposed, and it can achieve real-time decision-making and precise execution of the aligning process. Finally, through application validation, the DT process system can realize the real-time status perception and process execution decision during the large component aligning. Finally, through experimental validation, it is found that the proposed system, i.e., the DT aligning system, can improve the quality and efficiency of the large aerospace cylindrical component aligning, as well as the automation and intelligent level of the aligning system.

Computational techniques to monitoring fractional order type-1 diabetes mellitus model for feedback design of artificial pancreas

Background and objectives:In this paper, we developed a significant class of control issues regulated by nonlinear fractal order systems with input and output signals, our goal is to design a direct transcription method with impulsive instant order. Recent advances in the artificial pancreas system provide an emerging treatment option for type 1 diabetes. The performance of the blood glucose regulation directly relies on the accuracy of the glucose-insulin modeling. This work leads to the monitoring and assessment of comprehensive type-1 diabetes mellitus for controller design of artificial panaceas for the precision of the glucose-insulin glucagon in finite time with Caputo fractional approach for three primary subsystems. Methods:For the proposed model, we admire the qualitative analysis with equilibrium points lying in the feasible region. Model satisfied the biological feasibility with the Lipschitz criteria and linear growth is examined, considering positive solutions, boundedness and uniqueness at equilibrium points with Leray–Schauder results under time scale ideas. Within each subsystem, the virtual control input laws are derived by the application of input to state theorems and Ulam Hyers Rassias. Results:Chaotic Relation of Glucose insulin glucagon compartmental in the feasible region and stable in finite time interval monitoring is derived through simulations that are stable and bounded in the feasible regions. Additionally, as blood glucose is the only measurable state variable, the unscented power-law kernel estimator appropriately takes into account the significant problem of estimating inaccessible state variables that are bound to significant values for the glucose-insulin system. The comparative results on the simulated patients suggest that the suggested controller strategy performs remarkably better than the compared methods. Conclusion:In the model under investigation, parametric uncertainties are identified since the glucose, insulin, and glucagon system’s parameters are accurately measured numerically at different fractional order values. In terms of algorithm resilience and Caputo tracking in the presence of glucagon and insulin intake disturbance to maintain the glucose level. A comprehensive analysis of numerous difficult test issues is conducted in order to offer a thorough justification of the planned strategy to control the type 1 diabetes mellitus with designed the artificial pancreas.

Small signal analysis of DC voltage control based on a virtual resistance of DC/DC converter integrated in a multiterminal DC grid

AbstractThe future multi‐terminal direct‐current (MTDC) grid will require the interconnection of point‐to‐point high‐voltage (HV) DC links with different specifications such as DC voltage level, system grounding configuration and HVDC technology. To adapt these differences, it is obligatory for DC/DC converters to interconnect HVDC links. Additionally, they are capable of providing supplementary functionalities as they are highly controllable devices. In this article, a primary virtual resistance DC voltage controller associated with DC/DC converter is proposed for managing DC grid voltages of the interconnected HVDC grids, increasing the reliability of the system. The commonly known topology, Front‐to‐Front Modular Multilevel Converter (F2F‐MMC) is adopted for DC/DC converter. Time‐domain simulations are performed using EMTP software for validating the controller behaviour under power disturbances and large events of loss of one converter in a MMC‐based MTDC system. The converters are modelled using reduced order modelling (ROM) methodology. Apart from this, dynamic studies have been carried out using a linear state space model for small‐signal stability analysis of a HVDC system integrating DC/DC converter with a virtual resistance DC voltage controller. The results are examined through parametric sensitivity analysis.

Estimation-based event-triggered fixed-time fuzzy tracking control for high-order nonlinear systems

This paper investigates adaptive-estimation-based dynamic event-triggered fuzzy tracking control scheme for high-order nonlinear systems in fixed-time interval. The growing assumptions of coexistence unknown nonlinear uncertainties are removed with the aid of fuzzy logic systems. Contrary to the existing results, an adaptive estimation tactic is formulated to estimate the severe coexistence uncertainties online such that no boundaries are needed for the adaptive parameters, despite approximation errors, unknown virtual control gains and time-varying disturbances. On this estimation mechanism, the virtual control gains have been relaxed to unknown, which is more applicable. In view of controller and actuator, the communication burden is reduced with the aid of dynamic event-triggered rule, and Zeno behavior can be prevented successfully during dynamic sampling and updating of the control signal. Moreover, the singularity problem has been conquered by using the inequality transformation technique and all signals are bounded in fixed-time interval. Finally, two examples are used to verify the feasibility and rationality.

Command-filtered incremental backstepping attitude control of spacecraft with predefined-time stability

This paper investigates the predefined-time attitude tracking control problem within the theoretical framework of incremental dynamic inversion. The utilization of the Taylor series facilitates the transformation of the attitude control system into a discrete-time plant, with the control input expressed in an incremental form. Additionally, a novel predefined-time stable dynamics system is presented and incorporated into the disturbance observer designing process, serving the purpose of estimating lumped disturbance. It is also employed in a command filter design to approximate the derivatives of the virtual control law within a predefined time. Consequently, an incremental backstepping attitude tracking control scheme is further developed, integrating the proposed predefined-time disturbance observer to ensure the system's robustness and the predefined-time filter to address challenges related to “term explosion” and singularity problem. Rigorous Lyapunov analysis affirms that the attitude control system, when using the incremental backstepping controller, remains predefined-time stable. The effectiveness of the proposed control scheme is subsequently validated through numerical simulations.

Finite-time adaptive fuzzy event-triggered control for nonlinear time-delay system with input quantization via command filter

This paper investigates the problem of finite-time adaptive fuzzy event-triggered control for a class of uncertain nonlinear systems with input quantization. The nonlinear systems under consideration contain unmodeled dynamics and time-varying delays. Fuzzy logic systems are used to handle unknown nonlinear terms. Additionally, finite-time filters are introduced to solve the problem of “explosion of complexity” as well as to ensure that the derivative of the virtual controller can be approximated in finite time. The Lyapunov–Krasovskii function is used to handle time-varying delays. The hysteresis quantizer is used to ensure adequate precision when there is a low transmission rate requirement. The control strategy combines event-triggered mechanisms and quantization technology to ensure that all signals of the closed-loop system remain bounded in finite time. Finally, two examples are included to demonstrate the effectiveness of the proposed controller.

Virtual Reality to Reduce Pain, Fatigue, and Emotional Distress in People With Cancer: A Scoping Review.

Cancer and its treatments cause symptoms such as pain, fatigue, and emotional distress, which affect the quality of life of patients at different stages of cancer. Virtual reality (VR) has emerged as a promising alternative for addressing these symptoms by immersing patients in a virtual environment that isolates them from reality. To describe the existing evidence on the use of VR to improve pain, fatigue, and emotional distress in people with cancer at different stages of the cancer trajectory. A scoping review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Search was conducted in PubMed and CINAHL until October 2023. Twenty-three primary studies related to the study topic were identified, and the following information was collected: type of VR (immersive vs nonimmersive) used in oncology services, purpose (distraction, training, or relaxation), devices used (head-mounted display, headset, or virtual controller), and the environment simulated (natural environment, skill games, or educational environment). The effects of VR on pain, fatigue, and emotional distress are described, highlighting its ability to mitigate these symptoms in the short term. Virtual reality has been shown to be an effective technique for reducing pain, fatigue, and emotional distress in people with cancer in the short term. Nurses can use VR as a complementary tool to promote virtual environments that improve the care for and therefore the quality of life of people with cancer.

Adaptive neural network dynamic surface optimal saturation control for single‐phase grid‐connected photovoltaic systems

AbstractAn adaptive neural network (NN) based optimal saturation control scheme is investigated for single‐phase grid‐connected photovoltaic (PV) systems by incorporating dynamic surface control (DSC) and adaptive dynamic programming (ADP) based on the backstepping control design framework. For each backstepping step, a critic‐actor architecture is constructed via reinforcement learning (RL), and the PV system is optimized according to the cost function in the architecture. Due to the nonlinearity, it is difficult to solve the Hamilton–Jacobi–Bellman (HJB) equation. The neural networks (NNs) are employed to approximate the solution of the HJB equation such that the optimal virtual control and the actual controller are obtained. By considering control input symmetric saturation nonlinearity link, constraints on pulse width modulation (PWM) are ensured. On this basis, the combination of backstepping control design and dynamic surface technique is used to overcome the shortcomings of “differential explosion” and simplify calculations. Based on the Lyapunov method, the stability analysis proves that all signals of the closed‐loop PV systems are semiglobally uniformly ultimately bounded (SGUUB). Simulation experiments and comparative results are given to verify the efficacy of the studied control strategy.

Practical prescribed time tracking control for a class of nonlinear systems with event triggering and output constraints

AbstractThis paper investigates the practical prescribed time tracking for a class of uncertain nonlinear systems based on neural networks and event‐triggered control. Introducing a time‐varying constraint function transforms the original practical prescribed time‐tracking control issue into a tracking error constraint problem. An event‐triggered adaptive control has been proposed, which can effectively reduce the communication burden between the controller and the actuator. Using neural networks to approximate unknown nonlinear functions avoids the differentiation of virtual controllers, thereby reducing the computational burden. In addition, users can independently choose preset time and tracking accuracy without changing the control structure, which remains independent of the initial conditions and any design parameters. Finally, the effectiveness of this method is verified through simulation examples.

Virtual Model Predictive Control for Inverters to Achieve Flexible Output Impedance Shaping in Harmonic Interactions With Weak Grids

Virtual Model Predictive Control for Inverters to Achieve Flexible Output Impedance Shaping in Harmonic Interactions With Weak Grids

Virtual impedance optimization control strategy for wind turbine grid-forming converters in weak grid

Abstract Grid forming(GFM) technology is key to the effective consumption and grid-friendly integration of large-scale wind power. However, as the new type of power system sees a high penetration of new energy sources and a decrease in circuit capacity, leading to systems exhibiting weak grid characteristics, this paper studies the adaptive characteristics of grid-forming wind turbine converters under weak grid conditions. It investigates the dynamic relationship between power coupling characteristics and the system impedance ratio based on the virtual impedance method and proposes an impedance optimization method to improve the power decoupling accuracy and dynamic response capability of grid-forming converters. The research results show that the grid-forming controller can operate stably under weak grid conditions, an increase in grid impedance ratio leads to stronger power coupling intensity, and the voltage support capability of grid-forming converters weakens. The simulation verifies that the virtual impedance optimization strategy proposed in this paper can effectively improve the power coupling characteristics of the grid-forming converter and enhance the converter’s dynamic response performance under weak grid conditions.

Virtual impedance control of double-fed induction generator for damping enhancement in hvdc collection system

Virtual impedance control of double-fed induction generator for damping enhancement in hvdc collection system