Robust Control Scheme Research Articles

Hybrid multi-objective optimization of µ-synthesis robust controller for frequency regulation in isolated microgrids

Frequency regulation in isolated microgrids is challenging due to system uncertainties and varying load demands. This study presents an optimal µ-synthesis robust control strategy that regulates microgrid frequency while enhancing system performance and stability—a proposed fixed-structure approach for selecting performance and robustness weights, informed by subsystem frequency analysis. The controller is optimized using multi-objective particle swarm optimization (MOPSO) and multi-objective genetic algorithm (MOGA) under inequality constraints, employing a Pareto front to identify optimal solutions. Comparative analyses demonstrate that the MOPSO-optimized controller achieves superior robustness and performance, tolerating up to 236% uncertainty compared to 171% for conventional µ-synthesis controllers. Additionally, it significantly reduces frequency deviation and enhances transient response. Nyquist stability analysis confirms robustness across renewable energy uncertainties. The results highlight the proposed controller’s effectiveness in isolated microgrid frequency regulation, with future work focused on discrete-time implementation for practical digital signal processing (DSP) applications.

Novel robust control with disturbance rejection for permanent magnet synchronous motors and experimental validation.

A novel robust control strategy is proposed in this work to address the dynamic control problem of permanent magnet synchronous motors (PMSM) position tracking and lessen the effect of system parameter and load fluctuations on the dynamic performance of PMSM. The tracking performance is improved by a robust control element built with the Lyapunov method to reduce the impact of uncertain factors such as parameter uncertainty, nonlinear friction, and external interference; the nominal control element is stabilized by the dynamics model. The uniformly bounded and uniformly final bounded systems are proven, and the associated conclusions are provided using the Lyapunov minimax approach. In this work, modeling and experimental investigation are conducted using the cSPACE fast controller, based on the permanent magnet synchronous motor test platform. The results of the testing and simulation show that the developed controller can effectively regulate the permanent magnet synchronous motor and achieve more accurate position tracking even in the face of ambiguity.

Parallelized robust distributed model predictive control in the presence of coupled state constraints

In this paper, we present a robust distributed model predictive control (DMPC) scheme for dynamically decoupled nonlinear systems which are subject to state constraints, coupled state constraints and input constraints. In the proposed control scheme, all subsystems solve their local optimization problem in parallel and neighbor-to-neighbor communication suffices. The approach relies on consistency constraints which define a neighborhood around each subsystem’s reference trajectory where the state of the subsystem is guaranteed to stay in. Contrary to related approaches, the reference trajectories are improved consecutively. In order to ensure the controller’s robustness against bounded uncertainties, we employ tubes. The presented approach can be considered as a time-efficient alternative to the well-established sequential DMPC. In the end, we briefly comment on an iterative extension. The effectiveness of the proposed DMPC scheme is demonstrated with simulations, and its performance is compared to other DMPC schemes.

Antibiotic Susceptibility Pattern of Bacterial Isolates from Infected Diabetic Foot Ulcer in Patients of Type 2 Diabetes Mellitus Presenting at Hayatabad Medical Complex Peshawar

Objectives: This study aimed to determine the antibiotic resistance profiles of bacterial isolates obtained from diabetic foot ulcers (DFUs) in type 2 diabetes patients at Hayatabad Medical Complex, Peshawar, by identifying the most prevalent bacterial species and their corresponding resistance patterns. Materials and Methods: This cross-sectional study was conducted over six months from March to September 2024, involving 120 clinically diagnosed patients with infected diabetic foot ulcers. Bacterial isolates were obtained from wound swabs and identified using standard cultural and biochemical tests. Antibiotic susceptibility was determined through the disc diffusion method. Results: The results indicated that 93.3% of wound swabs showed positive bacterial growth, predominantly gram-negative bacteria, with Escherichia coli (28%) and Klebsiella pneumoniae (22%) being the most prevalent isolates. Polymicrobial infections were found in 18% of samples. Resistance rates were notably high for ampicillin (72%) and ciprofloxacin (55%), while carbapenems and piperacillin-tazobactam demonstrated higher sensitivity. Among gram-positive isolates, methicillin-resistant Staphylococcus aureus (MRSA) was detected in 60% of cases. Conclusion: The research underscores the significant prevalence of multidrug-resistant (MDR) bacteria in diabetic foot ulcers, emphasizing the critical need for innovative approaches to antibiotic treatment and robust infection control strategies.

Investigating Transient Responses of IEEE 6 Bus Power System to Various Fault Types using PowerWorld Simulator

The transient analysis of power systems is essential for understanding the dynamic responses and stability under fault conditions. This paper focuses on the transient analysis of the IEEE 6 bus power system using the PowerWorld Simulator, with the primary objective of investigating the system’s behavior under various fault conditions, including three-phase balanced faults, line-to-ground faults, line-to-line faults, and double line-to-ground faults. The IEEE 6 bus system, a standardized benchmark for testing power system algorithms, provides a simplified yet effective model for examining transient phenomena. Utilizing the Power World Simulator, this study models and simulates the fault conditions to assess their impacts on key parameters such as bus voltages, generator rotor angles, and generator voltages. By conducting a series of simulations, we aim to provide a detailed characterization of the transient response of the IEEE 6 bus system under each fault scenario. The results of our analysis reveal distinct patterns of system behavior for each type of fault. Three-phase balanced faults, being the most severe, significantly disrupt the stability of the system, causing considerable deviations in voltage and phase angles. Line-to-ground faults, although less severe, still pose substantial challenges, especially in terms of voltage stability at the faulted bus. Line-to-line faults primarily affect the phase voltages, leading to asymmetrical disturbances that propagate through the network. Double line-to-ground faults, which combine characteristics of line-to-line and line-to-ground faults, exhibit complex transient dynamics that test the system’s resilience and control mechanisms. Our findings underscore the necessity for robust protective measures and control strategies to mitigate the adverse effects of these faults. The study highlights the importance of fault location, fault type, and system configuration in determining the overall stability and reliability of the power system.

Biomimetic Linkage Mechanism Robust Control for Variable Stator Vanes in Aero-Engine.

This work addresses the position tracking control design of the stator vane driven by electro-hydrostatic actuators facing uncertain aerodynamic disturbances. Rapidly changing aerodynamic conditions impose complex disturbance torques on the guide vanes. Consequently, a challenging task is to enhance control precision in complex uncertain environments. Inspired by the principles of mammalian muscle movement, a novel robust control strategy based on the backstepping method has been proposed. Using backstepping, virtual rotational speed and virtual pressure difference force are designed, which decompose the high-order position closed-loop control problem into three lower-order parts, eliminating the need for matching conditions. Subsequently, robust controllers were designed, and stability proofs and performance analyses of the controllers were provided. This control strategy was tested through numerical hydraulic simulation. The results show that compared to other control methods, this approach significantly improves tracking accuracy and robustness. Therefore, it is believed that this method has the potential to become a new generation solution for such problems.

Vehicle active suspension control considering parameter uncertainty and velocity variation

Due to the uncertainty in vehicle model parameters, the control performance of active suspensions often deteriorates, especially under extreme conditions such as high velocities and full loads. To address this challenge, this paper proposes a novel robust finite frequency H ∞ output feedback control strategy based on linear parameter varying (LPV) systems. First, we establish a new LPV model for the half-car active suspension control system, taking into account the uncertainties associated with vehicle velocity and sprung mass. Next, a robust finite frequency H ∞ output feedback controller is designed using a gain scheduling approach, formulated through a set of linear matrix inequalities (LMIs). Finally, simulation results demonstrate that the proposed velocity-dependent robust control strategy significantly reduces the root mean square (RMS) values of vertical acceleration by approximately 21% to 31% under various conditions of sprung mass and vehicle speed compared to traditional control methods.

Extended state observer-based adaptive dynamic surface prescribed performance control for electro-hydraulic actuators with uncertainties.

For Electro-Hydraulic Actuators (EHA) with parametric uncertainties and mismatched and matched disturbances, most existing robust adaptive control strategies can achieve only uniformly ultimately bounded tracking errors. An Extended-State-Observer (ESO)based asymptotic control scheme is proposed by incorporating the prescribed performance control into the backstepping framework to ensure satisfied tracking performance and anti-disturbance ability of EHA systems. A novel ESO is designed to acquire an asymptotic estimation without prior bounds of the mismatched disturbance and its derivatives. Dynamic surface control with an adaptive adjustment mechanism is utilized to avoid the "complexity explosion" and guarantee the asymptotic stability of the closed-loop system. The efficacy of the proposed strategy is validated through simulation and experiments.

ADHDP-based robust self-learning 3D trajectory tracking control for underactuated UUVs

In this work, we propose a robust self-learning control scheme based on action-dependent heuristic dynamic programming (ADHDP) to tackle the 3D trajectory tracking control problem of underactuated uncrewed underwater vehicles (UUVs) with uncertain dynamics and time-varying ocean disturbances. Initially, the radial basis function neural network is introduced to convert the compound uncertain element, comprising uncertain dynamics and time-varying ocean disturbances, into a linear parametric form with just one unknown parameter. Then, to improve the tracking performance of the UUVs trajectory tracking closed-loop control system, an actor-critic neural network structure based on ADHDP technology is introduced to adaptively adjust the weights of the action-critic network, optimizing the performance index function. Finally, an ADHDP-based robust self-learning control scheme is constructed, which makes the UUVs closed-loop system have good robustness and control performance. The theoretical analysis demonstrates that all signals in the UUVs trajectory tracking closed-loop control system are bounded. The simulation results for the UUVs validate the effectiveness of the proposed control scheme.

Robust low-complexity vibration suppression control of rolling mill with time-varying state constraints

In this paper, a novel robust low-complexity vibration suppression control scheme is proposed for the vertical-torsional-horizontal coupling vibration of the rolling mill. To facilitate the control design, the overall coupling vibration system is decoupled into vertical-horizontal coupling vibration subsystem and torsional vibration subsystem. For the vertical-horizontal coupling vibration subsystem, a novel robust low-complexity controller is designed via constructed error transformations to provide strong robustness against uncertainties and disturbances. For the torsional vibration subsystem, a new robust state feedback controller is constructed via a backstepping technique to ensure the vibration angle of the motor rotor within given constraints via a proper asymmetric barrier function. The overall coupling vibration system is proved to be stable in the sense of uniform ultimate boundedness, and the vibration amplitude of the rolling mill can be reached arbitrarily small by selecting appropriate controller parameters. The numerical simulation is performed to verify the effectiveness and the superiority of the proposed control strategy.

Distributed Robust Volt-VAr Control Strategy for Accurate Reactive Power Sharing in Islanded Microgrid

Distributed Robust Volt-VAr Control Strategy for Accurate Reactive Power Sharing in Islanded Microgrid

Green ILC: A Novel Energy-Efficient Iterative Learning Control Approach.

In this paper, we introduce Green Iterative Learning Control (Green ILC), an innovative hybrid control method that addresses the critical need for energy-efficient control in dynamic, repetitive-task environments. By integrating the iterative refinement capabilities of traditional Iterative Learning Control (ILC) with the optimization strengths of gradient descent, Green ILC achieves a balanced trade-off between tracking accuracy and energy consumption. This novel approach introduces a cost function that minimizes both tracking errors and control effort, enabling the system to adaptively optimize performance over iterations. Theoretical analysis and simulation results demonstrate that Green ILC not only achieves faster convergence but also provides significant energy savings compared with traditional ILC methods. Notably, Green ILC reduces energy consumption by prioritizing efficiency, making it particularly suitable for energy-intensive applications such as robotics, manufacturing, and process control. While a slight decrease in tracking accuracy is observed, this trade-off is acceptable for scenarios where energy efficiency is paramount. This work establishes Green ILC as a promising solution for modern industrial systems requiring robust and sustainable control strategies.

A Scalable Convex Min–Max Optimization Algorithm for Robust Control of Polytopic Positive Systems

ABSTRACTIn this paper, we investigate the robust control of a class of bilinear positive systems. The main objective is to minimize the norm of the controlled system while dealing with polytopic uncertainties. In contrast to traditional Lyapunov‐based robust control problems with polytopic uncertainties, we establish that minimizing the objective function over the vertices of the polytopic set, rather than considering its infinite elements, is adequate for achieving the globally optimal solution. Following this, we present a customized algorithm that relies on partial cutting plane to address the min–max problem. The introduced robust control strategy exhibits global convergence to the optimal solution and demonstrates scalability with the number of states and the number of vertices in the uncertain family. Simulation results validate the effectiveness of the proposed strategy in handling uncertainties.

Robust Nonlinear Model Predictive Control for the Trajectory Tracking of Skid-Steer Mobile Manipulators with Wheel–Ground Interactions

This paper presents a robust control strategy for trajectory-tracking control of Skid-Steer Mobile Manipulators (SSMMs) using a Robust Nonlinear Model Predictive Control (R-NMPC) approach that minimises trajectory-tracking errors while overcoming model uncertainties and terra-mechanical disturbances. The proposed strategy is aimed at counteracting the effects of disturbances caused by the slip phenomena through the wheel–terrain contact and bidirectional interactions propagated by mechanical coupling between the SSMM base and arm. These interactions are modelled using a coupled nonlinear dynamic framework that integrates bounded uncertainties for the mobile base and arm joints. The model is developed based on principles of full-body energy balance and link torques. Then, a centralized control architecture integrates a nominal NMPC (disturbance-free) and ancillary controller based on Active Disturbance-Rejection Control (ADRC) to strengthen control robustness, operating the full system dynamics as a single robotic body. While the NMPC strategy is responsible for the trajectory-tracking control task, the ADRC leverages an Extended State Observer (ESO) to quantify the impact of external disturbances. Then, the ADRC is devoted to compensating for external disturbances and uncertainties stemming from the model mismatch between the nominal representation and the actual system response. Simulation and field experiments conducted on an assembled Pioneer 3P-AT base and Katana 6M180 robotic arm under terrain constraints demonstrate the effectiveness of the proposed method. Compared to non-robust controllers, the R-NMPC approach significantly reduced trajectory-tracking errors by 79.5% for mobile bases and 42.3% for robot arms. These results highlight the potential to enhance robust performance and resource efficiency in complex navigation conditions.

A Robust Control Strategy for Effective Field-Oriented Control of PMSMs

Field-Oriented Control (FOC) is widely recognized as a standard framework for Permanent Magnet Synchronous Motor (PMSM) drives. Linear control techniques are commonly employed in designing controllers for this strategy. However, traditional control methods often exhibit performance limitations and reduced robustness, particularly under harsh operating conditions, which makes the FOC structure less appealing and less effective. To address and overcome these challenges, this study proposes a Second-order Non-singular Terminal Sliding (SNTS) mode approach to achieve fast, accurate, and robust tracking for the FOC control structure applied to PMSM drives. The SNTS method combines the benefits of non-singular terminal sliding mode and second-order control laws. This approach ensures rapid and precise tracking while minimizing steady-state errors by using a nonlinear terminal sliding mode surface instead of a linear one. Furthermore, the system state transitions smoothly along the sliding mode surface with continuous functions, which reduces chattering around the sliding surface. The second-order control law incorporated into this method helps mitigate chattering and achieve fast convergence. The Lyapunov stability theory is employed to verify the stability of the SNTS technique designed for the PMSM system. Simulation and experimental validation on a hardware platform confirm the effectiveness and superiority of the proposed SNTS method, demonstrating its capability to enhance the performance of speed controllers for PMSM drives.

The adaptive sliding mode control based on U–K theory for foot trajectory following of hexapod robot

This paper addresses the control of a hexapod robot’s foot trajectory tracking using an adaptive sliding mode control (SMC) approach based on Udwadia–Kalaba theory. Unlike the traditional control approach, the Udwadia–Kalaba theory allows for the transformation of the hexapod robot foot trajectory tracking control problem into a system servo binding solution problem. This method eliminates the requirement to linearize the nonlinear system. The system may contain uncertainties, such as less-than-ideal initial circumstances and vibration disturbances during operation, which have an impact on the control precision due to mistakes in modeling, measurements, and changes in operational states. To deal with the uncertainty, the adaptive SMC controller was developed. The stability analysis is carried out using the second Lyapunov function method. By modeling the hexapod robot’s legs and running simulations to compare the simulated tracking route to the planned trajectory, the precision and stability of the control approach suggested in this study are finally demonstrated, and by comparing with the simulation results of adaptive robust control strategy, the advantages of RBF neural network adaptive SMC strategy are obtained.

Adaptive robust control of tank gun pitching stabilization system considering uncertainty

Abstract Aiming at the complex nonlinearity, uncertainty and coupling of the tank gun pitching stabilization system, an adaptive robust control strategy for the tank gun pitching stabilization system is proposed by combining adaptive control and deterministic robust control. Considering that the tank gun pitching stabilization system is a kind of uncertainty as well as nonlinear coupled dynamics system, a mechanical dynamics model and an electrical control model under the high mobility environment are established. Based on the backstepping concept fusion adaptive robust idea, the parameter adaptive law is constructed to eliminate the effect of unknown system parameter, and the adaptive robust function is designed to inhibit the interference for unmodeled disturbance of the system control precision. The designed controller synthesizes the advantages of adaptive control methods as well as robust control methods to improve the practical value in engineering. Based on Lyapunov theory, the analysis indicates that the tank gun pitching stabilization system can be controlled asymptotically only by unknown parameter effects, and the ideal stabilization accuracy of the system can be ensured when both parameter uncertainty and uncertain nonlinearity exist in the system. A large number of comparative co-simulations based on Recurdyn-Simulink demonstrate the superiority of the presented technique in this study.

A robust distributed model predictive control strategy for Leader–Follower formation control of multiple perturbed wheeled mobile robotics

This article studies the formation and trajectory tracking control of multiple mobile robots with kinematic sub-systems. We proposed an effective design of robust distributed model predictive control (MPC) strategy for Leader–Follower formation control scheme in a group of multiple perturbed Wheeled Mobile Robotics (WMRs) with the consideration of tracking performance in not only the position but also the orientation, as well as the distance between the center and head in each WMR. Furthermore, the relation between formation control objective and non-holonomic property in each agent is also discussed. For the purpose of achieving the desired formation, according to trajectory of leader WMR, the barycentric of the formation requirement is known as corresponding virtual followers, and a distributed tube-MPC scheme is applied to each follower WMR for tracking a reference trajectory with not only the position but also its orientation. In addition, the stability and the performance tracking of multiple perturbed WMRs are investigated by employing Lyapunov stability theory with the indirect comparison method to be implemented by pointing out precisely the proposed terminal controller and the equivalent terminal region. Comprehensive simulation results in several scenarios demonstrate the validity of the proposed control scheme.

Comprehensive Control Analysis of Dual DC-DC Output Converter for Integration of Offshore Wind Turbine Systems

This paper presents an in-depth exploration of control state-space modeling tailored for high-power isolated dual output DC-DC (ISO-D2) converter, with a particular emphasis on elucidating system dynamics through the derivation of state-space matrices and transfer functions. Employing state-of-the-art analysis techniques, this study offers a systematic framework for comprehending the converter's behavior across varied operational scenarios. Through the derivation of state-space matrices encompassing state, input, and output parameters, the converter's dynamic response is encapsulated in a precise mathematical representation. Additionally, transfer functions are established to facilitate frequency domain analysis and stability evaluation. These derived models furnish invaluable insights into the performance characteristics of the converter, thus enabling the formulation of robust control strategies. Particularly, the derived state-space matrices and transfer functions serve as instrumental tools for the design of advanced control algorithms, crucial for optimizing the performance of high-power isolated dual output DC-DC (ISO-D2) converters, in real-world applications such as solar plants and offshore DC wind farms.

Green and chemical synthesis of iron oxide nanoparticles: Comparative study for antimicrobial activity and toxicity concerns

Infections caused by multidrug-resistant bacteria in healthcare settings highlight the critical need for robust infection control strategies and effective antimicrobial stewardship. This was further emphasized by the COVID-19 pandemic. This necessitates developing more effective antimicrobial agents. Iron oxide nanoparticles (IONPs) were reported to be biocompatible and possess a broad-spectrum antimicrobial activity with less susceptibilty of developing microbial resistance. Chemical synthesis is employed for industrial manufacture of IONPs. However, concerns about potential toxicity associated with chemical synthesis have led to a preference for green synthesis approaches, despite ongoing debates regarding their cytotoxicity. According to the authors' knowledge, the impact of synthesis methodology on the antimicrobial activity and cytotoxicity of IONPs has not been previously studied. Therefore, the current study is considered the first attempt to assess and compare the toxicity and antimicrobial effect of IONPs synthesized using different methodologies (green versus chemical synthesis) while correlating these effects to the physicochemical properties of IONPs. Additionally, this study addresses for the first time the disturbance of the microbial antioxidant defense system in multidrug-resistant bacteria after their exposure to IONPs. Both types of IONPs effectively eradicated SARS-CoV-2, and multidrug-resistant bacteria: S. aureus, and E. Coli, in addition, both disrupted the antioxidant bacterial defense mechanism, indicating that reactive oxygen species play a significant role in their antimicrobial activities. Chemically synthesized IONPs showed superior antimicrobial activity to those produced by green methods and in-vivo study demonstrated their greater compatibility with skin and eyes. Therefore, chemically synthesized IONPs might be used as a disinfectant in healthcare settings.