Robust Control Research Articles

Fully Data-Driven Robust Output Formation Tracking Control for Heterogeneous Multiagent System With Multiple Leaders and Actuator Faults.

This article investigates a fully data-driven method to solve the robust output formation tracking control problem for the multiagent system (MAS) under actuator faults. The outputs of the followers are controlled to track those of multiple leaders with respect to a convex point while achieving an expected time-varying formation. To obviate the requirement of various system prior knowledge in typical MAS control, a hierarchical frame is developed with three learning and control stages using the online measured data. First, a distributed adaptive observer is designed to coordinate the state convex of multiple leaders while estimating unknown dynamics. The adaptive mechanism relaxes the demand for global topology. Second, by collecting and reusing the online system data, an off-policy reinforcement learning (RL) method is proposed in a continuous form to acquire nominal feedback gains from partial observations of the followers. Essential system models are learned along with the RL process, while solutions to the output regulation equations are implicitly obtained. Third, a comprehensive robust controller is further presented based on the previous learning results. To address the actuator faults with efficiency loss and bias, the adaptive neural networks and robust compensations are utilized in a model-free manner. The output formation tracking is achieved under a derived feasibility condition while stabilities of the learning and control methods are analyzed. Finally, simulation results demonstrate the validity of this fully data-driven control frame.

Concurrent Learning Robust Adaptive Fault Tolerant Boundary Regulation of Hyperbolic Distributed Parameter Systems.

This article develops a robust adaptive boundary output regulation approach for a class of complex anticollocated hyperbolic partial differential equations subjected to multiplicative unknown faults in both the boundary sensor and actuator. The regulator design is based on the internal model principle, which amounts to stabilize a coupled cascade system, which consists of a finite-dimensional internal model driven by a hyperbolic distributed parameter system (DPS). To this end, a systematic sliding mode equipped with a backstepping approach is developed such that the robust state feedback control can be realized. Moreover, since the available information is a faulty boundary measurement at the right side point, state estimation is required. However, due to the presence of boundary unknown faults, we need to solve an issue of joint fault-state estimation. Restrictive persistent excitation conditions are usually required to guarantee the exact estimation of faults but are unrealistic in practice. To this end, a novel concurrent learning (CL) adaptive observer is proposed so that exponential convergence is obtained. It is the first time that the spirit of CL is introduced to the field of DPSs. Consequently, the observer-based adaptive boundary fault tolerant control scheme is developed, and rigorous theoretical analysis is given such that the exponential output regulation can be achieved. Finally, the effectiveness of the proposed methodology is demonstrated via comparative simulations.

Intelligent control of the power generation system using DSPACE

AbstractWind power systems (WPs) are complex non‐linear systems with varying parameters affected by environmental changes, including wind speed fluctuations. Extracting maximum power from WPs poses a significant challenge due to these factors. Direct power control (DPC) is a highly effective technique known for its simplicity and ease of implementation. However, it suffers from power ripples caused by the use of hysteresis comparators and switching tables that operate at variable frequencies. To address this issue, this paper presents the robust neural controller (NC) based on DPC, which replaces the switching tables. The Double‐Fed Induction Generator (DFIG) is the chosen generator for the studied WP system due to its advantageous features. The NC‐DPC effectively regulates the exchange of active and reactive powers between the DFIG and the system, maximizing power extraction from the WP system while reducing Total Harmonic Distortion and enhancing overall system quality. The effectiveness of the NC‐DPC is evaluated through MATLAB simulations and further supported by experimental data obtained using the Real‐Time Interface of the dSPACE‐DS1104 Controller card.

Robust Disturbance Rejection Rotor Current Control of Doubly-Fed Induction Generators

Robust Disturbance Rejection Rotor Current Control of Doubly-Fed Induction Generators

EHFE refinement and extension for variable physical Earth radiation in spacecraft uncertainty thermal analysis

Spacecraft uncertainty thermal analysis is an important part of ensuring robust and reliable spacecraft thermal control system design. The calculation of the Earth external heat flux is an important part of it, however, the current model for calculating the Earth external heat flux is the constant physical Earth radiation model. In this paper, a model of the Earth’s variable physical radiation is developed based on the long-term averaging method. We refine and extend the application of the external heat flux expansion (EHFE) formula to accommodate our proposed model. This study further includes an uncertainty analysis of Earth radiative external heat flux, utilizing the enhanced EHFE formula in the context of a representative spacecraft. We provide statistical data, such as mean values, standard deviation, and probability density functions, derived from our analysis. The augmented EHFE formula, when properly generalized, can be applied to compute the celestial body’s variable physical radiative external heat flux affecting the spacecraft. This methodological advancement offers theoretical underpinning for the thermal design of spacecraft intended for deep space exploration.

Robust Smith Predictor-Based Internal Model Nonlinear Control With Lead Compensation Extended State Observer for PMLSM Servo Systems Considering Time Delay

Robust Smith Predictor-Based Internal Model Nonlinear Control With Lead Compensation Extended State Observer for PMLSM Servo Systems Considering Time Delay

Robust Voltage Control With Resonant Extended State Observer for Hybrid Fuel Cell System

Robust Voltage Control With Resonant Extended State Observer for Hybrid Fuel Cell System

Improved performance of a shunt hybrid active power filter by a robust exponential functional link network-based nonlinear adaptive filter control to enhance power quality

This research article details the design and implementation of a nonlinear adaptive filtering (NAF) technique using an exponential functional link network (EFLN) for a shunt hybrid active power filter (SHAPF) control to solve the current-associated power quality issues on the utility side at the distribution level of electrical power systems. Separation of the fundamental component from the harmonics, achieving unity power factor operation, reducing the reactive power drawn from the source, balancing the currents during transients, and reduction of total harmonic distortion (THD) of the source current are the issues considered to resolve. The proposed technique solves these issues by generating the sinusoidal reference current and separating the fundamental current from the harmonics. When compared to conventional and existing adaptive filtering techniques such as least mean square (LMS), least mean fourth (LMF), and variable step size LMS (VSS-LMS), the proposed EFLN-NAF method excels in terms of speedy convergence, adaptability in noise-specific environments and reduced steady-state coefficient error. MATLAB/Simulink software is utilized to perform the simulations to examine the suggested strategy for the chosen SHAPF topology both in static and dynamic scenarios. For a 15 kW and 3kVAr requirement of the nonlinear load, simulation results proved that the designed PPF for 2kVAr is able to share the reactive power with the APF, thereby reducing its rating and cost. The proposed method of filtering has been proven to be fast converging with 0.049 s, and the THD in steady state is brought to 1.32 % in steady-state and to 3.77 % during transient conditions, which are under standard limits. A hardware prototype of the experimental setup is constructed at the laboratory scale with OPAL-RT (OP4510) as the controller. With an active and reactive power demand of 1.1 kW and 210VAr, the designed PPF supplies 110 VAr, whereas the rest is supplied by the APF. The practical THD in source current is observed to be 2.081 %, which meets the standards. The results from both simulations and experiments are validated, and the efficacy of the proposed technique in mitigating the aforementioned power quality issues is proved.

Distributionally Robust Model Predictive Control With Output Feedback

Distributionally Robust Model Predictive Control With Output Feedback

Human–Robot Cooperation Control Strategy Design Based on Trajectory Deformation Algorithm and Dynamic Movement Primitives for Lower Limb Rehabilitation Robots

Compliant physical interactions, interactive learning, and robust position control are crucial to improving the effectiveness and safety of rehabilitation robots. This paper proposes a human–robot cooperation control strategy (HRCCS) for lower limb rehabilitation robots. The high-level trajectory planner of the HRCCS consists of a trajectory generator, a trajectory learner, a desired trajectory predictor, and a soft saturation function. The trajectory planner can predict and generate a smooth desired trajectory through physical human–robot interaction (pHRI) in a restricted joint space and can learn the desired trajectory using the locally weighted regression method. Moreover, a triple-step controller was designed to be the low-level position controller of the HRCCS to ensure that each joint tracks the desired trajectory. A nonlinear disturbance observer is used to observe and compensate for total disturbances. The radial basis function neural networks (RBFNN) approximation law and robust term are adopted to compensate for observation errors. The simulation results indicate that the HRCCS is robust and can achieve compliant pHRI and interactive trajectory learning. Therefore, the HRCCS has the potential to be used in rehabilitation robots and other fields involving pHRI.

Robust Arbitrary-Time Path-Tracking Control Using Reduced Order Kinematic Model for Unmanned Ground Vehicles

Robust Arbitrary-Time Path-Tracking Control Using Reduced Order Kinematic Model for Unmanned Ground Vehicles

Simplified robust two-step model predictive control for permanent magnet synchronous motor

Traditional finite control set model predictive control (FCS-MPC) selects an optimal voltage vector in one control cycle. However, considering multiple control cycles, it cannot be proved that the voltage vector is optimal, and the uncertainty of PMSM parameters seriously affects the prediction accuracy. In order to balance the relationship between switching frequency, steady-state performance, and robustness, a multi-step robust FCS-MPC is proposed. Firstly, an incremental prediction model is established to eliminate the flux linkage of permanent magnets. The inductance parameters in the incremental model are identified online based on the observer and inductance extraction algorithm. Then, based on the principle of no-beat, the candidate voltage vector in two control periods is simplified to get the candidate voltage vector, and the cost function is used to determine the optimal voltage vector again. Finally, the proposed FCS-MPC method is compared with the traditional FCS-MPC method. The experimental results show the effectiveness of FCS-MPC strategy.

Profitability in rural restaurants and owners' motivation to agritourism: A case from Nepal

AbstractRural Restaurants (RR) are located in rural areas primarily to serve the owner's business interests. However, they also play a crucial role in providing economic opportunities for rural communities and are regarded as a center for rural economy. Rural areas often face challenges such as depopulation due to limited access to public services, inadequate infrastructure, geographical remoteness, economic hardships, educational barriers, and other factors. Thus, this study aims to investigate the profitability of RRs and owners' motivations for agritourism development, as profitability and business expansion are typically positively correlated. Data from 105 samples collected in rural Nepal in 2022 and 2023 were analyzed using Tobit and Ordered Logit regression models, respectively. The study conceptualizes and confirms various contributors to RR profitability, including factors related to the supply side, demand appeal, and those influenced by pandemic‐related factors. Additionally, we explored how owners' motivations for agritourism are influenced by the restaurant's characteristics, quality‐related factors, and other attributes. Policymakers need to support RR owners in developing agritourism initiatives and contribute to quality control through effective enforcement of consumer laws by a responsible and robust quality control team.

Development of A Palm-Vein Recognition System for Identification and Verification Systems using Enhanced Convolutional Neural Network

Biometric authentication systems have gained significant attention in access control applications due to their ability to provide enhanced security and convenience. Among various biometric modalities, palm-vein recognition has emerged as a promising approach, offering high accuracy, reliability, and resistance to forgery. However, existing palm-vein recognition systems often face challenges in implementation costs, computational efficiency, and performance limitations. This research aimed to develop an enhanced palm-vein recognition system for access control applications by optimizing a Convolutional Neural Network (CNN) architecture. A palm-vein dataset comprising 1000 images from 200 LAUTECH students was acquired, with 5 images per individual. The dataset was split into 700 training images and 300 testing images. The acquired images were pre-processed for quality enhancement and region of interest extraction. A Gravitational Search Algorithm (GSA) optimized CNN (GSA-CNN) was then employed to extract deep features from the pre-processed images, which were classified using a SoftMax layer. Experimental results revealed that the CNN technique achieved a specificity, sensitivity, False Positive Rate (FPR), accuracy of 74.60%, 79.89%, 25.40%, 77.67% at 117.52 seconds, respectively. In contrast, the proposed GSA-CNN technique demonstrated superior performance, achieving a specificity, sensitivity, FPR, accuracy of 92.06%, 92.53%, 7.94%, 92.33% at 97.14 seconds, respectively. The GSA-CNN system outperformed the conventional CNN approach in terms of accuracy, specificity, sensitivity, FPR, and processing time, demonstrating its potential for reliable and efficient palm-vein recognition in access control applications. The findings have significant implications for developing robust and secure access control systems, contributing to enhanced privacy and security across various domains.

Exploring the Potential of Stem Cell Therapy and Non-Opioid Anesthesia and Pain Reduction: Reducing the Negative Impact on the Individual and Society

The opioid epidemic poses a significant public health challenge, stemming from the widespread misuse and abuse of opioids for pain management. Stem cell therapy (SCT) and non-opioid anesthesia emerge as promising alternatives to traditional opioid-based treatments, offering effective pain relief while mitigating the risks of addiction and overdose. SCT involves undifferentiated cells, which can differentiate into various cell types to promote tissue repair and alleviate pain. Non-opioid anesthesia employs techniques such as regional anesthesia and multimodal analgesia to manage pain during surgeries without relying on opioids.Both SCT and non-opioid anesthesia demonstrate advantages in reducing opioid dependence and improving patient outcomes. SCT releases neurotrophic factors for neuropathic pain relief and can prevent opioid tolerance, while non-opioid anesthesia provides robust pain control with fewer side effects.Although further research is needed to optimize their use, the integration of SCT and non-opioid anesthesia affords a comprehensive approach to addressing the opioid crisis. By reducing opioid prescriptions and promoting safer pain management practices, these interventions not only benefit individual patients but also alleviate the socioeconomic burden of opioid-related complications. Ethical considerations, such as informed consent and patient autonomy, are essential in ensuring these alternative treatments’ responsible and equitable implementation. SCT and non-opioid anesthesia represent significant advancements in combating the opioid epidemic and improving public health outcomes.Abbreviations: CPSP: Chronic Post-Surgical Pain; ESCs: Embryonic Stem Cells; HSCs: Hematopoietic Stem Cells; MSCs: Mesenchymal Stem Cells; NSAIDs: Nonsteroidal Anti-Inflammatory Drugs; OFA: Opioid-Free Anesthesia; PONV: Postoperative Nausea and Vomiting; QoL: Quality of Life; SCT: Stem Cell Therapy; TKA: Total Knee Arthroplasty

Assessment of internal and external disturbances on the fuzzy-based thermal control of a sub-scaled building testbed

This experimental study follows on our previous work on the development of robust fuzzy-based thermal control strategies of a multi-room sub-scaled building testbed. In the present analysis, the focus is placed on testing the robustness of the fuzzy controller under internal and external disturbances, as it deals with maintaining specific setpoint values of room temperatures. The testbed has eight rooms, distributed on two floors, with a cooling unit that supplies cool air to each room, and eight 40 W light bulbs serving as heat sources. T-type thermocouples gather the temperature data, and eight dampers deliver the airflow. The controller uses information about the difference between setpoint and actual temperatures, their derivative, and their cumulative integral. The fuzzy sets and if-then rules are built based on experimental data, and a Mamdani inference method is used to provide the inputs to the actuators. Results from experimental tests show that the fuzzy control strategy can handle the different types of disturbances while maintaining the room setpoints.

Research on fault-tolerant control methods for Bi-rotor vehicles under actuator failure

A PID-self-impedance fault-tolerant controller based on the inner and outer loop structure is developed for a new form of bi-rotor vehicle, taking into account the fault-tolerant control problem when the actuator fails. First, the dynamics model and actuator fault model of the bi-rotor vehicle are established, the PID active fault-tolerant controller with control parameter switching is selected for the outer loop, and the self-immune controller is selected for the inner loop. Second, when a fault occurs, the outer loop PID controller’s settings are switched to the controller parameters of the relevant fault state based on the extent of the fault, and the parameters are matched with the robust controller of the inner loop to achieve fault-tolerant control. Finally, simulation experiments show that the designed fault-tolerant controller can still achieve stable tracking of position and attitude in the presence of actuator faults. This indicates that the flight control system is robust to fault-induced disturbances and effectively improves the vehicle’s fault-tolerant performance.

Development of a Universal Adaptive Control Algorithm for an Unknown MIMO System Using Recursive Least Squares and Parameter Self-Tuning

Development of a Universal Adaptive Control Algorithm for an Unknown MIMO System Using Recursive Least Squares and Parameter Self-Tuning

Hierarchical gain scheduling based tilt angle guided robust control during mode transition for tilt-rotor unmanned aircraft vehicle

Tilt-rotor unmanned aircraft vehicle has the potential to combine vertical take-off and landing capability with efficient, high-speed cruise flight. However, the mode transition process is risky owing to the internal continuous time change of aerodynamic and external uncertain wind disturbances. Gain scheduling between two modes is commonly used to achieve mode transition control. However, the optimal scheduling parameters to allocate the manipulated variables for the stable transition have not been determined to data. Focusing on this problem, a gain scheduling-based tilt angle guided robust control method for mode transition is proposed. The dynamic model is first built and analyzed based on a newly developed 360-kg tilt-rotor unmanned aircraft vehicle. Based on this model, the transition guided curve is mapped with respect to velocity and tilt angle, and it is introduced to the gain scheduling method to optimally achieve the allocation of the manipulated variables. Finally, the feasibility and validity are verified in a simulation experiment. Furthermore, the robustness is verified in simulated wind disturbance.

Green energy solution for grid integrated solar PV system for water storage towers

In India, water storage towers are present almost everywhere, especially across small towns to rural areas. The storage tanks either utilize diesel powered or utility grid fed induction motor (IM) water pumps. The large spaces available with water storage towers can be utilized to plant solar PV panel for large storage towers water pumping operation, hence, making a switch from diesel or grid run storage towers to solar operated storage towers. This paper presents a robust control of grid integrated solar PV-based water pump system with efficient and smooth transition between standalone and grid interfaced modes. A smart DC link voltage control is presented for distributed generation system (DGS), which provides full control on DC link voltage for both modes of operation at all dynamic load conditions. An enhanced phase-locked loop (EPLL) control is utilized for grid source converter (GSC) in grid control operation, making it flexible to different grid scenarios and power quality. It works for harmonics suppression, load balancing and grid side converter control. An enhanced phase-locked loop (EPLL) is implemented for synchronization control algorithm, which provides an accurate estimation of voltage angle even during frequency ramps and exhibiting superior performance particularly in terms of robustness against DC offsets. The primary requirement for smooth operation of grid-tied solar PV system is effective power management between different sources. The issues of power management and power quality, are discussed in this work, and solutions are presented. The implemented control algorithms are tested successfully for dynamic solar insolation, varying load conditions, unbalance in load, sudden grid outage condition. The system appropriateness is validated by experimental results.