Robust Control Research Articles

Controllability of delayed generalized asynchronous Boolean control networks under disturbances

For delayed generalized asynchronous Boolean networks with random disturbances, it is still uncertain about their robust controllability. This paper uses the semi-tensor product to transform these Boolean networks into linear representations to derive the dependence matrix of the network transition on the control mode. After that, three different control modes are utilized to define controllability. Each control mode provides both reachable trajectories set under a specific initial trajectory and an initial trajectory set under a specific target trajectory. It is also calculated for the likelihood of a transition between two states. Lastly, two concrete examples validate the theoretical findings under various control modes.

State and disturbance observer based robust disturbance rejection control for friction electro-hydraulic load simulator

State and disturbance observer based robust disturbance rejection control for friction electro-hydraulic load simulator

Robust inverse control strategy for dissolved oxygen concentration based on disturbance boundary estimation

Robust inverse control strategy for dissolved oxygen concentration based on disturbance boundary estimation

Robust Funnel Model Predictive Control for Output Tracking with Prescribed Performance

Robust Funnel Model Predictive Control for Output Tracking with Prescribed Performance

Optimal fuzzy P + Dµ controller for cancer chemotherapy

This paper aims to develop a robust, effective, and optimal controller for an automatic intravenous drug delivery system for the chemotherapy treatment of cancer patients. For this purpose, a novel hybrid control method, namely the Fuzzy P + Dµ controller, is designed by combining fractional calculus and fuzzy logic controller (FLC). Particle swarm optimization (PSO) algorithm is employed to tune the controller parameters optimally. To evaluate the performance of the proposed controller, a cancer patient model that considers cumulative drug toxicity, one of the most common side effects of chemotherapy, is utilized. Limits on toxicity and drug concentration for patient safety are incorporated into the controller design and optimization process. The graphical and numerical simulation results show that almost no cancerous cells are left at the end of the treatment period. The effectiveness of the controller is proven by the performance index (PI) obtained. Our optimal hybrid Fuzzy P + Dµ outclasses other control techniques in previous related studies with the best PI of 44.426 and the minimum final number of cancerous cells (FNCCs) of 5.084E-08. Moreover, in robustness tests perturbing intra-patient parameters and using different treatment protocols, the cumulative drug toxicity level is kept within safe limits, and almost no cancerous cells remain.

Augmented Two-Stage Hierarchical Controller for Distributed Power Generation System Powered by Renewable Energy: Development and Performance Analysis

The sustainable development of an area is highly reliant on a reliable electrical energy supply. Microgrids are important in integrating distributed energy resources (DERs) using power electronic converters. However, microgrid control becomes challenging with the increasing number of distributed generators and loads. With the conventional droop control method, power contributions from DER converters cannot be accurately shared due to a mismatch of line impedances. In this paper, an augmented hierarchical control mechanism is proposed to solve the issues mentioned above. This hierarchical control mechanism consists of primary and secondary controllers. The primary stage utilized the droop controller to improve optimal power flow, mainly for the resistive network. The secondary stage is based on an improved methodology to compensate for the voltage and frequency variations during small and large signal disturbances. Moreover, the modelling and analysis for PMSG-based wind energy conversion systems are also presented. The response of the primary controller for active and reactive power sharing is investigated. The analysis emphasizes the demonstration of optimal power-sharing under normal and abnormal conditions for the considered load. Finally, the suggested robust controller’s performance is evaluated in a MATLAB environment, and simulation results show the proposed scheme’s superiority under different operating conditions. The frequency is stable at 50 Hz after a 50 KW load is added.

Active Willis metamaterials with programmable density and stiffness

Investigation and implementation of Active Willis Metamaterials (AWM) have been done exclusively, in all the available literature, by approaches that do not rely on any solid control theory basis. When coupled with piezoelectric control elements, the available approaches have not included, from the first principles, the exact form of the constitutive relationship of the piezoelectric materials. Furthermore, in all these approaches, stability analysis, robustness, ability to accommodate uncertainty or parameter changes, or consideration of disturbance rejection has not been addressed at all. More importantly, the available formulations have always mixed the flow and effort variables of the AWM, resulting in a form that is totally incompatible for the use in generating, investigating, or even designing any appropriate sensing or control applications of the material. In this paper, the piezoelectric-based AWM is modeled, from the first principles, to develop a constitutive coupling form that enables its use in actuation, sensing, and as an integrated controller that can be analyzed, designed, and optimized using the classical, optimal, and robust control system theories. Lagrange dynamics formulation is used to generate the equations governing the Willis coupling, the piezoelectric coupling, as well as the active robust controller. With this developed controlled-based structure of the AWM, the inherent and powerful capabilities of the AWM that lie in its ability to robustly control the material properties themselves such as the compliance (or stiffness) and specific volume (or density) are demonstrated in great detail via several numerical examples. Controlling these properties enables the AWM to be used in numerous important and imaginative applications such as cloaking, beam shifting, beam focusing, as well as many other applications that are limited only by our imagination.

SYNLOCO‐VE: Synthesizing central pattern generator with reinforcement learning and velocity estimator for quadruped locomotion

AbstractIt is a challenging task to learn a robust and natural locomotion controller for quadruped robots at different terrains and velocities. In particular, the locomotion learning task will be even more difficult for the case with no exteroceptive sensors. In this article, the learning‐based locomotion control is, therefore, investigated for quadruped robots only using proprioceptive sensors. A new framework called SYNLOCO‐VE is proposed by synthesizing a feedforward gait planner, a trunk velocity estimator, and reinforcement learning (RL). The feedforward gait planner is developed based on the well‐known central pattern generator, but it can change the foot length for improved velocity tracking performance. The trunk velocity estimator is designed based on deep learning, which estimates the trunk velocity using historical data from proprioceptive sensors. The introduction of the trunk velocity estimator can mitigate the influence of the partial observation issue due to the lack of exteroceptive sensors. RL is employed to learn a feedback controller to regulate the robot gaits using feedback from proprioceptive sensors and the trunk velocity estimation. In the proposed framework, the feedforward gait planner can also guide the training process of RL, thus resulting in more stable and faster policy learning. Ablation studies are provided to demonstrate the efficiency of different modules in the proposed design. Extensive experiments are performed using a quadruped robot Go1, which only has proprioceptive sensors. The proposed framework is able to learn robust and stable locomotion at different terrains and tasks. Experimental comparisons are also conducted to illustrate the advantages of the proposed design over the state‐of‐the‐art methods.

Meta-analysis of intrauterine hCG perfusion efficacy in recurrent implantation failure as defined by ESHRE guidelines

PurposeThis study evaluates the efficacy of intrauterine hCG perfusion for RIF, as defined by ESHRE 2023 guidelines, highlighting hCG as a cost-effective alternative to other immunotherapies, especially suitable for less developed regions. It aims to clarify treatment guidance amidst previous inconsistencies.MethodsThis meta-analysis, registered with PROSPERO (CRD42024443241) and adhering to PRISMA guidelines, assessed the efficacy and safety of intrauterine hCG perfusion in enhancing implantation and pregnancy outcomes in RIF. Comprehensive literature searches were conducted through December 2023 in major databases including PubMed, Web of Science, Embase, the Cochrane Library, and key Chinese databases, without language restrictions. Inclusion and exclusion criteria were strictly aligned with the 2023 ESHRE recommendations, with exclusions for studies lacking robust control, clear outcomes, or adequate data integrity. The risk of bias was evaluated using the Newcastle-Ottawa Scale, ROBINS-I, and RoB2 tools. Data analysis was performed in R using the ‘meta’ package, employing both fixed and random effect models to account for study variability. Subgroup analyses by dosage, volume, hCG concentration, timing of administration, and type of embryo transfer were conducted to deepen insights, enhancing the reliability and depth of the meta-analysis in elucidating the role of hCG perfusion in RIF treatments.ResultsData from 13 studies, comprising six retrospective and six prospective studies from single centers, along with one multi-center RCT, totaling 2,157 participants, were synthesized to evaluate the effectiveness of intrauterine hCG perfusion in enhancing implantation and pregnancy outcomes in patients with RIF. Significant improvements were observed in clinical pregnancy and embryo implantation rates across various dosages, timing of administration, and embryo developmental stages, without impacting miscarriage rates. Notably, the most significant efficacy within subgroups occurred with a 500 IU dosage and perfusion parameters of ≤ 500µL volume and ≥ 2 IU/µL concentration. Additionally, a limited number of studies showed no significant increases in ectopic pregnancy or multiple pregnancy rates, and a modest improvement in live birth rates, although the small number of these studies precludes definitive conclusions.ConclusionsThe analysis suggests that intrauterine hCG perfusion probably enhances embryo implantation, clinical pregnancy, and live birth rates slightly in RIF patients. Benefits are indicated with a dosage of 500 IU and a maximum volume of 500µL at concentrations of at least 2 IU/µL. However, substantial heterogeneity from varying study types and the limited number of studies necessitate cautious interpretation. These findings underscore the need for more rigorously designed RCTs to definitively assess the efficacy and safety.

Lattice-Based Post-Quantum Public Key Encryption Scheme Using ElGamal’s Principles

Modern technologies like quantum and cloud computing have reshaped computing, offering immense power and scalability. While beneficial, they also challenge the security of traditional cryptographic systems. Quantum computing threatens RSA and ECC with algorithms like Shor’s algorithm, which can accelerate computations exponentially. This risks exposing these systems to attacks, necessitating quantum-resistant cryptography. Cloud computing poses data security concerns, requiring robust cryptographic mechanisms and access controls. Lattice-based cryptography, leveraging problems like the Short Integer Solution (SIS), emerges as a solution. This paper presents a novel quantum-resistant public key encryption scheme based on ElGamal and SIS, ensuring security against quantum and classical threats in modern cryptographic environments.

Adaptive Disturbance Suppression Method for Servo Systems Based on State Equalizer

Disturbances in the aviation environment can compromise the stability of the aviation optoelectronic stabilization platform. Traditional methods, such as the proportional integral adaptive robust (PI + ARC) control algorithm, face a challenge: once high-frequency disturbances are introduced, their effectiveness is constrained by the control system’s bandwidth, preventing further stability enhancement. A state equalizer speed closed-loop control algorithm is proposed, which combines proportional integral adaptive robustness with state equalizer (PI + ARC + State equalizer) control algorithm. This new control structure can suppress high-frequency disturbances caused by mechanical resonance, improve the bandwidth of the control system, and further achieve fast convergence and stability of the PI + ARC algorithm. Experimental results indicate that, in comparison to the control algorithm of PI + ARC, the inclusion of a state equalizer speed closed-loop compensation in the model significantly increases the closed-loop bandwidth by 47.6%, significantly enhances the control system’s resistance to disturbances, and exhibits robustness in the face of variations in the model parameters and feedback sensors of the control object. In summary, integrating a state equalizer speed closed-loop with PI + ARC significantly enhances the suppression of high-frequency disturbances and the performance of control systems.

Adaptive Disturbance Suppression Method for Servo Systems Based on State Equalizer.

Disturbances in the aviation environment can compromise the stability of the aviation optoelectronic stabilization platform. Traditional methods, such as the proportional integral adaptive robust (PI + ARC) control algorithm, face a challenge: once high-frequency disturbances are introduced, their effectiveness is constrained by the control system's bandwidth, preventing further stability enhancement. A state equalizer speed closed-loop control algorithm is proposed, which combines proportional integral adaptive robustness with state equalizer (PI + ARC + State equalizer) control algorithm. This new control structure can suppress high-frequency disturbances caused by mechanical resonance, improve the bandwidth of the control system, and further achieve fast convergence and stability of the PI + ARC algorithm. Experimental results indicate that, in comparison to the control algorithm of PI + ARC, the inclusion of a state equalizer speed closed-loop compensation in the model significantly increases the closed-loop bandwidth by 47.6%, significantly enhances the control system's resistance to disturbances, and exhibits robustness in the face of variations in the model parameters and feedback sensors of the control object. In summary, integrating a state equalizer speed closed-loop with PI + ARC significantly enhances the suppression of high-frequency disturbances and the performance of control systems.

Adaptive context-aware access control for IoT environments leveraging fog computing

AbstractThe increasing use of the Internet of Things (IoT) has driven the demand for enhanced and robust access control methods to protect resources from unauthorized access. A cloud-based access control approach brings significant challenges in terms of communication overhead, high latency, and complete reliance. In this paper, we propose a Fog-Based Adaptive Context-Aware Access Control (FB-ACAAC) framework for IoT devices, dynamically adjusting access policies based on contextual information to prevent unauthorised resource access. The main purpose of FB-ACAAC is to provide adaptability to changing access behaviors and context by bringing decision-making and information about policies closer to the end nodes of the network. FB-ACAAC improves the availability of resources and reduces the amount of time for information to be processed. FB-ACAAC extends the widely used eXtensible Access Control Markup Language (XACML) to manage access control decisions. Traditional XACML-based methods do not take into account changing environments, different contexts, and changing access behaviors and are vulnerable to certain types of attacks. To address these issues, FB-ACAAC proposes an adaptive context-aware XACML scheme for heterogeneous distributed IoT environments using fog computing and is designed to be context-aware, adaptable, and secure in the face of unauthorised access. The effectiveness of this new scheme is verified through experiments, and it has a low processing time overhead while providing extra features and improved security.

Adaptive context-aware access control for IoT environments leveraging fog computing

The increasing use of the Internet of Things (IoT) has driven the demand for enhanced and robust access control methods to protect resources from unauthorized access. A cloud-based access control approach brings significant challenges in terms of communication overhead, high latency, and complete reliance. In this paper, we propose a Fog-Based Adaptive Context-Aware Access Control (FB-ACAAC) framework for IoT devices, dynamically adjusting access policies based on contextual information to prevent unauthorised resource access. The main purpose of FB-ACAAC is to provide adaptability to changing access behaviors and context by bringing decision-making and information about policies closer to the end nodes of the network. FB-ACAAC improves the availability of resources and reduces the amount of time for information to be processed. FB-ACAAC extends the widely used eXtensible Access Control Markup Language (XACML) to manage access control decisions. Traditional XACML-based methods do not take into account changing environments, different contexts, and changing access behaviors and are vulnerable to certain types of attacks. To address these issues, FB-ACAAC proposes an adaptive context-aware XACML scheme for heterogeneous distributed IoT environments using fog computing and is designed to be context-aware, adaptable, and secure in the face of unauthorised access. The effectiveness of this new scheme is verified through experiments, and it has a low processing time overhead while providing extra features and improved security.

Robust seamless GEO scanning control strategy for airborne electro-optical/infrared reconnaissance systems

Robust seamless GEO scanning control strategy for airborne electro-optical/infrared reconnaissance systems

Force Control of a Haptic Flexible-Link Antenna Based on a Lumped-Mass Model

Haptic organs are common in nature and help animals to navigate environments where vision is not possible. Insects often use slender, lightweight, and flexible links as sensing antennae. These antennae have a muscle-endowed base that changes their orientation and an organ that senses the applied force and moment, enabling active sensing. Sensing antennae detect obstacles through contact during motion and even recognize objects. They can also push obstacles. In all these tasks, force control of the antenna is crucial. The objective of our research is to develop a haptic robotic system based on a sensing antenna, consisting of a very lightweight and slender flexible rod. In this context, the work presented here focuses on the force control of this device. To achieve this, (a) we develop a dynamic model of the antenna that moves under gravity and maintains point contact with an object, based on lumped-mass discretization of the rod; (b) we prove the robust stability property of the closed-loop system using the Routh stability criterion; and (c) based on this property, we design a robust force control system that performs efficiently regardless of the contact point with the object. We built a mechanical device replicating this sensing organ. It is a flexible link connected at one end to a 3D force–torque sensor, which is attached to a mechanical structure with two DC motors, providing azimuthal and elevation movements to the antenna. Our experiments in contact situations demonstrate the effectiveness of our control method.

Designing Decentralized Multi-Variable Robust Controllers: A Multi-Objective Approach Considering Nearly Optimal Solutions

This article presents a new methodology for designing a robust, decentralized control structure that considers stochastic parametric uncertainty and uses a multi-objective approach. This design tunes the loop pairing and controller to be implemented. The proposed approach obtains the optimal and nearly optimal controllers relevant to the nominal scenario. Once obtained, the robustness of these solutions is analyzed. This methodology is compared with a traditional approach for selecting the most robust control pairings. The traditional approach obtains lightly robust controllers, i.e., the most robust controllers with an acceptable performance for the nominal scenario, and it obtains trade-offs between robustness and nominal performance. However, the traditional approach has a high computational cost because it is necessary to consider uncertainty in the optimization stage. The proposed approach mathematically guarantees the acquisition of at least one neighbor controller for each existing lightly robust controller. Therefore, this approach obtains solutions similar to lightly robust solutions with a significantly lower computational cost. Furthermore, the proposed approach provides the designer with more diversity and interesting solutions that are not lightly robust. The different approaches are compared using an example of a multi-variable process with two alternative control structures. The results show the usefulness of the proposed methodology.

Designing Decentralized Multi-Variable Robust Controllers: A Multi-Objective Approach Considering Nearly Optimal Solutions

This article presents a new methodology for designing a robust, decentralized control structure that considers stochastic parametric uncertainty and uses a multi-objective approach. This design tunes the loop pairing and controller to be implemented. The proposed approach obtains the optimal and nearly optimal controllers relevant to the nominal scenario. Once obtained, the robustness of these solutions is analyzed. This methodology is compared with a traditional approach for selecting the most robust control pairings. The traditional approach obtains lightly robust controllers, i.e., the most robust controllers with an acceptable performance for the nominal scenario, and it obtains trade-offs between robustness and nominal performance. However, the traditional approach has a high computational cost because it is necessary to consider uncertainty in the optimization stage. The proposed approach mathematically guarantees the acquisition of at least one neighbor controller for each existing lightly robust controller. Therefore, this approach obtains solutions similar to lightly robust solutions with a significantly lower computational cost. Furthermore, the proposed approach provides the designer with more diversity and interesting solutions that are not lightly robust. The different approaches are compared using an example of a multi-variable process with two alternative control structures. The results show the usefulness of the proposed methodology.

Optimizing Grid-Connected Inverter Performance Through Voltage Stability Control

Everyday, non-renewable energy sources are being depleted while energy use rises. In response to these arguments, we ought to generate more renewable energy. But a big problem with renewable energy is that its production is weather-dependent. In overcast and unfavorable weather, the energy generated on the grid side is insufficient to match the demand on the load side. The primary load-side issues may be non-linear loads that lower the energy network’s power quality or excessive or unmanaged energy demand. When compared to the production of renewable energy, energy sources that are connected to the grid through an electronic converter or inverter have quite different operating characteristics. The stability of the grid and solutions to the grid-to- transmission-line stability issue are the main topics of this study. The benefits of using inverters in a power system from the grid to the transmission line with MPPT and PI controllers are examined in this article. A robust control system with an inverter-equipped PI controller and MPPT addresses the main problem.

A robust and optimal voltage control strategy for low-voltage grids utilizing group coordination of photovoltaic and energy storage systems via consensus algorithm

This study presents a novel voltage control strategy for low voltage (LV) distribution grids, addressing the lack of coordination between photovoltaic (PV) reactive control and energy storage system (ESS) active control. The proposed strategy concentrates on group coordination of PV and ESS to improve LV grid performance. Initially, it suggests employing PV reactive power for voltage regulation before utilizing ESS active power, relying on the voltage cost sensitivity factor (VCSF) of different regulating devices, to reach efficient PV resource utilization. Devices are categorized by their VCSF, prioritizing groups with larger VCSF for voltage regulation, enhancing overall coordination. In the PV reactive voltage regulation stage, the reactive power utilization rate is the consensus variable, while in the ESS active control stage, the change in state of charge (SOC) is the consensus variable, collectively working towards desired voltage regulation and SOC change. Coordinated control is utilized to converge key node voltages to set values and obtain effective LV grid voltage regulation. An IEEE 14-node LV grid simulation incorporating PV and ESS validates the proposed strategy, demonstrating its effectiveness and correctness in improving voltage control performance. The strategy achieves 70.56% of the capacity and 81.45% of the setup cost compared to similar research, with associated costs limited to 15.80% and 35.0% of accumulated costs.