Robust Control Research Articles

Adaptive Set-Membership Filter Based Discrete Sliding Mode Control for Pneumatic Artificial Muscle Systems With Hardware Experiments

Pneumatic artificial muscle (PAM), featuring good flexibility and safety, has been widely used in rehabilitation and bionic robots. However, the complex hysteretic nonlinearities and uncertainties of the PAM cause great difficulties and challenges to the accurate modeling and controller design, especially when confronted with unknown external disturbances in applications. This paper proposes a robust control strategy with disturbance compensation for the hysteresis compensation and trajectory tracking of PAMs. Considering the high hysteretic nonlinearity of the PAM, a modified Prandtl-Ishlinskii model is used as a feedforward hysteresis compensator. For the linearized system, adaptive set-membership filtering (ASMF) is used to estimate the nonlinear terms and external disturbances of the overall system. A sliding mode controller (SMC) with disturbance compensation is designed and cascaded to the feedforward hysteresis compensator in series. The stability of the closed-loop system is theoretically proved. The proposed method guarantees that the tracking error of the PAM system is bounded. Finally, the effectiveness and robustness of the proposed controller are verified via a series of experiments on an in-house built testbench for PAMs. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —With the increasing demand on human-robot interaction, the safety and compliance of robots have become one key requirement. PAM is a compliant actuator, exhibiting good flexibility, safety, and clean energy. PAM is widely used in rehabilitation robots, whereas its strong hysteresis nonlinearity and sensitivity to external disturbances affect its motion accuracy. This paper proposes an ASMF-based discrete SMC, which uses an inverse hysteresis model to compensate for the strong hysteresis of the PAM and uses ASMF to estimate the lumped disturbance of the system. Compared with the other filters, ASMF is unique in that its estimation error is bounded, which is very useful in the stability proof of the overall system. The effectiveness of the proposed controller is experimentally verified. Experimental results show that PAM’s hysteresis can be efficiently compensated, and the influence of external disturbances can be attenuated by the proposed controller, resulting in improved motion accuracy and robustness. In future work, efforts will be directed towards the modeling and control of PAMs in multi-DOF robots.

Tube-based model predictive control for linear systems with bounded disturbances and input delay

This paper proposes a tube-based model predictive control strategy for linear systems with bounded disturbances and input delay to ensure input-to-state stability. Firstly, the actual disturbed system is decomposed into a nominal system without disturbances and an error system. For the nominal system, solving an optimization problem, where the delayed control input is set as an optimization variable, yields a nominal control law that enables the nominal state signal to approach to zero. Then, for the error system, the Razumikhin approach is used to identify a robust control invariant set. Using the set invariance theorem, an ancillary control law is developed to confine the error state signal in the invariant set. Combining the two results, we obtain a control law that enables the state signal to remain within a robustly invariant tube. Finally, the effectiveness of the developed control strategy is validated by simulations.

Stabilization of chaotic fractional-order Lu ̈ system using sliding mode control

In this paper a fractional-order sliding mode controller (FOSMC) is proposed to control the chaotic fractional-order l’s system. Sliding mode control is a robust controller and its objective is to allo the system states to move to the sliding surface and remain on it. The stability analysis for the proposed control is performed based on the Lyapunov stability theorem. Simulation results show the ability of the proposed controller to improve the system performance.

Robust Observer-Based Controller Design for Constrained Takagi-Sugeno Systems

This work concerns a descriptor Takagi-Sugeno (T-S) fuzzy system. The system is subject to disturbances, sensor faults, the presence of unmeasurable premise variables, and input saturation which the latter are considered uncertainties of the system. First, the considered system is transformed into the polytopic representation, and then, a robust Proportional-Integral (PI) observer-based controller is designed. This observer-based controller, can estimate both the system states, and the faults, and stabilize closed-loop state trajectories. An integrated robust controller strategy is adopted by a novel structure of non-Parallel Distributed Compensation (non-PDC) control law. furthermore, the proposed approach expresses the stability conditions of controller design are expressed in terms of Linear Matrix Inequalities (LMIs) constrained optimization problem. Using LMI TOOLBOX in MATLAB, we can easily design the type PI observer for efficient robust state/fault estimation and solve the observer-based controller design problem of discrete-time T-S fuzzy systems. Finally, an application to a rolling disc process is given to illustrate the effectiveness of the proposed method.

Data-driven robust iterative learning control of linear systems

We propose a data-driven robust iterative learning control (ILC) technique to multi-input-multi-output (MIMO) linear systems. Control of MIMO linear systems, particularly with strong cross-axis coupling, is challenging as modeling of a MIMO system can be complicated, time-consuming, and often requires a trade-off between robustness and performance. As such, limitations exist in current ILC techniques. The aim of this paper is to develop an efficient and easy-to-use data-driven ILC technique to output tracking of MIMO linear systems under random disturbance. Through the proposed technique, the complicated modeling process and the robustness-accuracy trade-off are avoided, and the up-to-now system dynamics is captured by constructing and updating the iteration gain using the input and output data in the last iteration. It is shown that monotonic convergence of the ILC algorithm is guaranteed, and an optimal gain can be obtained to maximize the convergence rate and minimize the residual tracking error. The proposed technique is illustrated through experiments on a three-input three-output piezoelectric actuator system, with comparison to the adaptive multi-axis inversion-based iterative control (A-MAIIC) technique. The experimental results show rapid convergence and improved formance of the proposed technique when the cross-axis coupling is strong.

Constraint-following adaptive robust control for multi-vehicle system formation with collision avoidance

For the formation on unstructured road with complex road conditions, the rationality of constraint design and uncertainty compensation seriously affect the connected and autonomous vehicle (CAV) system performance. The system constraints are studied for safe and efficient formation driving, and an adaptive robust formation control scheme is proposed. First, for the desired configuration and global collision avoidance, a series of equality and unilateral inequality constraints are designed for inter-vehicle spacing. Second, a constraint handling scheme is proposed based on the diffeomorphism method, adjustable transformation function, and bounded constraint. Third, (possibly fast) time-varying but bounded multi-source uncertainty is considered, and then a self-adjusting leakage-type adaptive law is proposed to estimate the integrated uncertainty bound online. Fourth, the performance measure β is defined to evaluate the constraint-following error, and an adaptive robust control is proposed. The effectiveness of the proposed control scheme is verified by simulation. The results show that the proposed control renders the error to be uniformly bounded and uniformly ultimately bounded. The CAV system approximately follows the constraints, regardless of the uncertainty.

Active suspension for all-terrain vehicle with intelligent control using artificial neural networks

The automotive industry focuses on developing advanced protection and stability control systems, particularly for suspension and steering, to enhance vehicle comfort, luxury, and safety. This research presents an intelligent controller for all-terrain vehicle (ATV) suspension systems based on Artificial Neural Network (ANN) technology. The controller leverages ANN capabilities to optimize system performance. MATLAB simulations were conducted to evaluate its effectiveness under various disturbances. A comparative analysis compared the ANN regulator, classic ANFIS regulator, and passive performance in different disturbance scenarios. The simulation results demonstrate exceptional performance of the ANN-based controller in displacement reduction, speed, acceleration, and robustness. The controller effectively mitigates disturbances, enhancing overall suspension system performance. These findings highlight the advantages of employing ANN technology in ATV suspensions. This research contributes to intelligent control systems advancement in the automotive industry, specifically in ATV suspensions. The demonstrated improvements have the potential to enhance passenger comfort, vehicle stability, and safety across terrains. By implementing ANN-based controllers, automotive manufacturers can optimize suspension systems, leading to improved vehicle performance. Several indicators, including RMSE, MRE, and R2, were utilized to test and validate the models. The R2 values for the three quality parameters ranged from 0.989 to 0.999, indicating a high level of consistency in the predictions made by the ANN, a "5-12-1" structure is employed. The results of this study add to the expanding body of knowledge endorsing the efficacy of ANNs in simulating and optimizing quarter-vehicle dynamics.

Evaluating the impact of blockchain technology in healthcare data management: A review of security, privacy, and patient outcomes

The integration of blockchain technology into healthcare data management has emerged as a transformative paradigm, promising to address critical challenges in security, privacy, and patient outcomes. The paper begins with an exploration of the fundamental principles of blockchain technology and its current applications in the healthcare sector. Special emphasis is placed on the potential benefits and challenges associated with adopting blockchain, setting the stage for an in-depth analysis. In terms of security, the paper examines how blockchain ensures data integrity and immutability, leveraging decentralized structures and smart contracts for robust access control. The discussion extends to the privacy implications of blockchain, highlighting its role in granting patients’ ownership and control over their health data while maintaining confidentiality and anonymity. The analysis also delves into regulatory compliance and legal considerations, ensuring a holistic examination of the privacy landscape. The impact on patient outcomes is a central focus, exploring how blockchain enhances interoperability, streamlines healthcare processes, and potentially transforms the patient experience. Real-world case studies provide practical insights into the positive effects observed in healthcare settings. However, acknowledging the transformative potential comes with an exploration of challenges and limitations. Technical hurdles, ethical considerations, and the need for seamless integration with existing healthcare systems are discussed, providing a balanced perspective on the roadblocks faced in widespread blockchain adoption. Looking towards the future, the paper outlines emerging trends in blockchain and healthcare, offering recommendations for organizations considering implementation. It concludes by summarizing key findings and underscoring the importance of ongoing research, ultimately positioning blockchain as a catalyst for a paradigm shift in healthcare data management.

Securing the digital supply chain: Cybersecurity best practices for logistics and shipping companies

The review investigates the pressing need for robust cybersecurity measures within the logistics and shipping sector, where the digital supply chain is vulnerable to a myriad of cyber threats. The paper delves into the specific challenges faced by logistics companies, including the interconnectedness of global supply chains, reliance on digital technologies for operations, and the high value of goods in transit. It explores the multifaceted nature of cyber risks, encompassing threats such as ransomware, phishing attacks, data breaches, and supply chain disruptions, which can have far-reaching consequences for business continuity and reputation. Through a detailed analysis, the study elucidates cybersecurity best practices tailored to the logistics and shipping industry, encompassing both technical solutions and organizational policies. These include implementing robust authentication and access controls, encrypting sensitive data in transit and at rest, establishing secure communication channels, and conducting regular vulnerability assessments and penetration testing. Furthermore, the paper emphasizes the importance of fostering a culture of cybersecurity awareness among employees through comprehensive training programs and incident response drills. It also discusses the role of regulatory compliance frameworks such as GDPR, CCPA, and industry-specific standards like ISO 27001 in guiding cybersecurity efforts and ensuring adherence to best practices. By providing actionable recommendations and insights garnered from real-world case studies, the study equips logistics and shipping companies with the knowledge and tools needed to bolster their cybersecurity defenses, safeguard critical assets, and maintain trust in the digital supply chain ecosystem.

Cascade NMPC-PID control strategy of active heave compensation system for ship-mounted offshore crane

In this paper, the problem associated with active heave motion compensation during heavy-lift operations performed by ship-mounted offshore cranes beneath rough marine environment is investigated. To effectively decouple the correlated motion of the suspended payload from the support vessel via the secondary regulated active heave compensation (SRAHC) system, a novel hierarchical control strategy is proposed by integrating a cascade control structure with an adaptive robust control scheme that incorporates a vertical motion forecast algorithm based on autoregressive integrated moving average (ARIMA) model, a conventional proportional-integral-derivative (PID) framework, and a nonlinear model predictive control (NMPC) method. The introduction of the cascade structure is motivated by its prompt error response against control lag and efficient system order reduction for computational burden alleviation, which forms a crucial foundation for the NMPC-based real-time regulation of PID gains, ensuring optimal evolution of the SRAHC system while improving its practical feasibility. Furthermore, the enhancements in heave compensation and trajectory tracking performance, noise resistance, constraint satisfaction, and engineering application potential of the proposal are demonstrated through a thorough comparative analysis conducted in a practical co-simulation research system.

Role of Forensic Investigations in Reduction of Financial Fraud Among Commercial Banks in South Sudan

Financial fraud poses a significant challenge to the banking sector globally, including South Sudan. This study aimed to investigate the effectiveness of forensic investigations in mitigating financial fraud within commercial banks. Employing a mixed-methods approach and an explanatory research design, the study targeted 119 respondents and nine commercial banks. Data was analysed using descriptive statistics and regression analyses. The findings revealed a strong association between increased levels of forensic investigations and a significant reduction in financial fraud among commercial banks in South Sudan. The study emphasized the crucial role of adopting sophisticated technologies like data analytics and forensic accounting techniques, as well as implementing robust internal control mechanisms, in enhancing the effectiveness of forensic investigations. Through bolstering forensic investigation practices, not only can financial fraud be curbed, but the overall integrity and trustworthiness of South Sudan's banking sector can be enhanced. To address financial fraud effectively, the study recommended that commercial banks invest in advanced forensic investigation tools and technologies, enabling them to better detect and prevent fraudulent activities. Furthermore, enhancing the training and capacity-building of forensic professionals within these institutions was identified as a means to improve their effectiveness in dealing with fraud. Strengthening internal control systems and fostering a culture of transparency and accountability were also highlighted as vital steps. This research holds significance in informing policy and practice within South Sudan's banking sector. Through highlighting the effectiveness of forensic investigations in combating financial fraud, the study emphasizes the importance of prioritizing forensic capabilities within commercial banks. Moreover, it contributes to the existing literature on forensic investigations and financial fraud, offering valuable insights for researchers and policymakers alike

Robust Model Predictive Control for Systems Affected by Constant and Norm 2 Bounded Disturbance

Robust Model Predictive Control for Systems Affected by Constant and Norm 2 Bounded Disturbance

Robust dynamic surface control of series elastic actuators based on reduced-order extended state observer

The compliance of series elastic actuators (SEAs) is significant for ensuring safe human–machine interaction. However, in practical applications, the SEA is inevitably encountered with unknown disturbances, such as parameter uncertainties, unmodeled dynamics, and environmental interferences. In this paper, a robust dynamic surface control scheme is presented to address the high-precision trajectory tracking problem of the SEA. Firstly, a reduced-order extended state observer (RESO) with only link-side position measurements is designed to estimate the unknown lumped disturbance and unmeasurable system states. Based on the estimated values from the RESO, a robust dynamic surface controller is proposed for the trajectory tracking of the SEA. Additionally, the finite-time filter is introduced to overcome the “explosion of complexity” in the backstepping controller, and the filtering error in the dynamic surface controller is reduced compared with the conventional first- and second-order filters. The stability of the closed-loop system is guaranteed by the Lyapunov theory. Finally, the effectiveness and superiority of the proposed control algorithm are verified through simulations and experiments.

Fuzzy backstepping control for enhanced stability of a quadrotor unmanned aerial vehicle

This paper presents a robust fuzzy backstepping control approach to enhance the stability and maneuverability of a Quadrotor Unmanned Aerial Vehicle (UAV). The proposed design combines the robustness of backstepping control with the adaptability of fuzzy logic to address uncertainties and disturbances commonly encountered in UAV dynamics. By incorporating fuzzy logic, the controller can adapt to changing environmental conditions, ensuring reliable performance in various flight scenarios. The backstepping technique enables systematic handling of nonlinear dynamics and achieves precise tracking of desired trajectories. Extensive simulations demonstrate the effectiveness of the proposed control strategy in stabilizing the UAV and enabling agile maneuvers, even in the presence of disturbances and model uncertainties. These results highlight the potential of the proposed approach to enhance the robustness and agility of Quadrotor UAVs in real-world applications.

Efficacy and tolerability of a depigmenting gel serum comprising tranexamic acid, niacinamide, 4-butylresorcinol, phytic acid, and a mixture of hydroxy acids that targets the biological processes regulating skin melanogenesis.

The diverse causes of hyperpigmentation and complex nature of melanogenesis make it a challenge to manage. Current approaches either fail to deliver effective pigmentation control or have undesirable safety profiles that preclude their long-term use. To evaluate the capacity of a cosmetic gel serum comprising tranexamic acid, niacinamide, 4-butylresorcinol, phytic acid, and a mixture of hydroxy acids that was designed to target the biological processes regulating skin melanogenesis to attenuate melanin production invitro and reduce hyperpigmentation clinically. Capacity to reduce melanin production invitro was determined in melanocyte-containing reconstructed human epidermis (RHEm). Clinical efficacy and skin tolerability following twice daily application were assessed in 35 subjects with slight to moderate facial hyperpigmentation by instrumental (VISIA®-CR, Mexameter®) and clinical (mMASI, clinical score, IGA for hyperpigmentation) evaluation on D14, D28, D56, and D84. Maintenance of pigmentation control was followed up 1 month after cessation of treatment on D112. In RHEm invitro, melanin production was reduced by 50.0% from baseline (D0) on D14 (p < 0.001) and by 67.0% on D21 (p < 0.001). Clinical reductions from baseline in brown spots count (-9.0%; p < 0.05), brown spots area (-16.7%; p < 0.001), and the melanin index (-11.4%; p < 0.001) were observed within 14 days of use. Statistically significant improvements in all clinical parameters were achieved by D28. By the end of treatment on D84, the number and surface area of brown spots were reduced by 28.4% and 40.3% compared to D0, respectively (p < 0.001, both), the melanin index was reduced by 31.1% (p < 0.001), mMASI was reduced by 63.0% (p < 0.001), and skin luminosity was increased by 79.0% (p < 0.001). IGA was reduced from 2.3 on D0 to 1.3 on D84 (p < 0.001). Improvements to all these parameters were maintained until D112, 1 month after termination of treatment. The product also demonstrated very good skin tolerability. A gel serum comprising tranexamic acid, niacinamide, 4-butylresorcinol, and hydroxy acids, designed to target the biological processes regulating skin melanogenesis, demonstrates rapid, robust, and sustained pigmentation control in this cohort.

Online learning of stable robust adaptive controllers design based on data-dependent feedback linearization with application to rotary inverted pendulum

AbstractThis study introduces an online (supervised) learning method to design nonlinear auto-regressive moving average (NARMA) controllers for feedback-linearized nonlinear single-input single-output (SISO) systems. The algorithm ensures Schur stability of the overall closed-loop system and provides adaptiveness and robustness for the NARMA controllers. The first stage of the method derives, in a data-dependent way, a feedback-linearized model of the nonlinear plant by using its input and output sample pairs. The method’s second stage, which constitutes the novel part of the presented study, builds up an online learning scheme for the linear auto-regressive moving average (ARMA) controller based on an already learned feedback-linearized model of the nonlinear plant. During online supervised learning, ARMA parameters of the feedback-linearized SISO plant model and the closed-loop ARMA model are computed by minimizing the plant identification and the closed-loop system tracking errors. Both errors are defined as $${{\ell}}_{1,{\varvec{\varepsilon}}}$$ ℓ 1 , ε , namely ε-insensitive loss functions that provide NARMA controller the robustness against noise and outliers. The proposed online learning control algorithm is applied to a rotary inverted pendulum model and to a real rotary inverted pendulum setup. The tracking performance of the developed controller is compared with those of the linear quadratic regulator and coupled sliding mode controller in terms of mean square error.

Enhancing Internal Control Mechanisms in Local Government Organizations: A Crucial Step towards Mitigating Corruption and Ensuring Economic Development

Corruption poses a significant challenge to economic development and governance worldwide, with its detrimental effects permeating various levels of society. In the context of Greece, where corruption has been a longstanding issue, the role of internal audit mechanisms within local government organizations (LGOs) emerges as paramount. This paper presents a comprehensive analysis of the internal control landscape within LGO revenue departments, focusing on factors influencing its effectiveness and proposing strategies for improvement. Drawing upon survey data and regression analyses, this study highlights the crucial role of robust internal control mechanisms in combating corruption and fostering economic development. The findings underscore the importance of competent personnel, legislative compliance, interdepartmental collaboration, and technology utilization in enhancing internal control practices. Despite existing legislation, gaps in internal control implementation persist, including understaffing, inadequate procedures, and limited access to information. This study emphasizes the transformative potential of effective internal audit measures in mitigating corruption at the local level, thereby contributing to broader economic growth and societal well-being. Recommendations for strengthening the internal control structures within LGOs include the formal establishment of internal audit functions, adherence to professional standards, and the promotion of information system utilization. By addressing the corruption and inefficiencies within LGOs, this research underscores the pivotal role of institutional effectiveness in promoting transparency, accountability, and sustainable economic progress.

A thrust inversion method for small satellite electric propulsion based on a momentum wheel

Electric propulsion, which is an advanced form of thrust production, is widely used in small satellites. Measuring the thrust of electric propulsion in an orbit is difficult because it is very small (μN∼mN). However, the thrust of electric propulsion can be determined by evaluating the change in the speed of a momentum wheel. The contributions of this paper mainly include two aspects: 1) a robust PID controller and 2) a thrust inversion method. The torque of the electric propulsion is regarded as the disturbance torque in this paper. To achieve finite-time attitude stabilization of a satellite during electric propulsion, a robust PID controller is designed. Furthermore, the thrust of the electric propulsion can be determined in the stable attitude of the satellite by only measuring the speed derivative of the momentum wheel. Compared with the traditional thrust inversion method, the designed thrust inversion method is simple and effective and does not use a filtering algorithm. In the HIL experiment, the error rate between the inverted thrust (31.287 μN) and the designed thrust (30 μN) is 4.29%. Most importantly, the thrust inversion method is verified in orbit based on the small satellite APSCO SSS-1, which is consistent with the HIL experimental results and ground test results.

Robust fuzzy-logic flight control for unmanned aerial vehicles (UAVs)

Researches on Unmanned Aerial Vehicles (UAVs) have been recently attracting considerable interest in the field of control theory applications. They are used in a wide range of areas thanks to having the potential of high manoeuvrability, hovering and flying, taking off and landing capabilities. However, to maintain robust control action towards changing conditions of the system is not an easy matter since quadrotor UAVs are highly unstable systems with high precision. Therefore, the main purpose of this study is to control a quadrotor UAV by using a proposed multi-input single-output (MISO) fuzzy-logic controller that ensures robustness if model parameters and trajectory change. For that reason, a 2-dimensional 3 degree-of-freedom quadrotor was used in this study to better evaluate the performance of proposed controller on UAVs. Afterwards, numerical analysis was performed and the findings were analysed. Consequently, the single most striking observation to emerge from the study is that the satisfactory results have been obtained demonstrating that the proposed fuzzy logic controller has remarkable advantage on the robustness of quadrotor UAVs.

Autonomous Rapid Exploration in Close-Proximity of Asteroids

The increasing number of space missions may overwhelm ground support infrastructure, prompting the need for autonomous deep-space guidance, navigation, and control (GNC) systems. These systems offer sustainable and cost-effective solutions, particularly for asteroid missions that deal with uncertain environments. This study proposes a paradigm shift from the proposals currently found in the literature for autonomous asteroid exploration, which inherit the conservative architecture from the ground-in-the-loop approach that relies heavily on reducing uncertainties before close-proximity operations. Instead, it advocates for robust guidance and control to handle uncertainties directly, without extensive navigation campaigns. From a series of conservative assumptions, the authors demonstrate the feasibility of this autonomous GNC for robotic spacecraft by using existing technology. It is shown that a bolder operational approach enables autonomous spacecraft to significantly reduce exploration time by weeks or months. This paradigm shift holds great potential for reducing costs and saving time in autonomous missions of the future.