Control Performance Research Articles

TRAJECTORY TRACKING CONTROL OF A TWO WHEELED SELF-BALANCING ROBOT BY USING SLIDING MODE CONTROL

Two-Wheeled Self-Balancing Robots are widely used in various fields today. These systems have a highly unstable nature due to their underactuated structures. On the other hand, parameter uncertainties and external disturbances significantly affect their control performance. The best way to deal with parameter uncertainties that can easily lead controllers to instability is to use robust control methods. Dealing with these uncertainties is particularly crucial in control of underactuated and unstable systems such as Two-Wheeled Self-Balancing Robots. In this study, trajectory tracking control of a two wheeled self-balancing robot by using Sliding Mode Control (SMC) was realized. The chattering problem inherent in the SMC method was eliminated by employing tangent hyperbolic (tanh) switching function instead of signum function. The performance of the SMC controller has been examined under five different cases including external disturbance and various parameter uncertainties and compared with PID and LQR methods. The results showed that the SMC method is much more insensitive to parameter changes than the PID and LQR methods. It has also been observed that all three controllers maintain their stability against disturbance inputs, but the SMC method offers a better control performance.

The conformance control and enhanced oil recovery performance of CO2 foam reinforced by regenerated cellulose

CO2 foam flooding is one of the most promising technologies for conformance control and enhanced oil recovery (EOR) in heterogenous tight oil reservoirs. However, it is challenging to maintain foam stability at in-situ high temperature and high salinity reservoir conditions, thus seriously limiting the flooding efficiency. Herein, a new approach of CO2 foam reinforced by regenerated cellulose (RC) was proposed and evaluated by multiple light scattering method and microscopic detection; afterwards, the conformance control and EOR performances were evaluated via flooding experiments at representative reservoir conditions of 85 °C and 59737.6 mg/L, using artificial micro-models and heterogenous core test. Results have demonstrated that: Compared with CO2 foam without RC, the foam with 1 % RC could elongate the foam half-life by ∼30 times (i.e., from 18 to 543 mins) and the drainage half-time by ∼4 times (i.e., from 2.5 to 9.5 mins), respectively; For the conformance control, the CO2 foam flooding with RC improved the sweep efficiency from the respective 42.4 % and 15.8 % to 95.4 % compared with water flooding and CO2 flooding in the micromodel experiments; the CO2 foam with RC increased the injection pressure from 0.20 to 3.1 MPa in the core flooding experiments, suggesting the diversion of fluid from high permeable to low permeable zone. In terms of the EOR, the CO2 foam flooding with RC improved the oil recovery from the respective 29.7 % and 12.9 % to 92.3 % in contrast with water flooding and CO2 flooding in micro-flooding experiments and from 35.5 % to 56.4 % by core-flooding experiments; the underlying mechanisms are attributed to oil emulsification, rock wettability alteration and foam deformation. These insights provide new approaches to maintain foam stability, improve conformance control and EOR performances in heterogenous tight oil reservoirs under harsh conditions.

Toward Impedance Control in Human-Machine Interfaces for Upper-Limb Prostheses.

Adaptation of upper-limb impedance (stiffness, damping, inertia) is crucial for humans to physically interact with the external environment during grasping and manipulation tasks. Here, we present a novel framework for Adaptive Impedance Control of Upper-limb Prosthesis (AIC-UP) based on surface electromyography (sEMG) signals. AIC-UP uses muscle-tendon models driven by sEMG signals from agonist-antagonist muscle groups to estimate the human motor intent as joint kinematics, stiffness and damping. These estimates are used to implement a variable impedance controller on a simulated robot. Designed for use by amputees, joint torque or stiffness measurements are not used for model calibration. AIC-UP was evaluated with eight able-bodied subjects and a transradial amputee performing target-reaching tasks in simulation through wrist flexion-extension. The control performance was tested in free space and in the presence of unexpected perturbations. We show that AIC-UP outperformed a neural network that regresses the desired kinematics from sEMG signals, in terms of robustness to muscle coactivations needed to complete the task. These results were in agreement with the qualitative feedback from the participants. Additionally, we observed that AIC-UP enables the user to adapt the stiffness and damping to the tasks at hand.

Robust observer-based-α-variable model-free supertwisting fractional order sliding mode control for nonlinear PEMFC system with uncertainties and disturbance

The service life and efficiency of proton exchange membrane fuel cells (PEMFCs) are significantly related to the control performance of the air supply system. Therefore, this research develops a novel robust observer-based- [Formula: see text]-variable model-free supertwisting fractional-order sliding mode control ([Formula: see text]-MF-STFOSMC) for complex nonlinear PEMFC air supply system with unmeasurable state variables, model uncertainties, and external disturbance. First, the fifth-order nonlinear PEMFC air supply system model is presented, and the unmeasured state variables are estimated using a nonlinear disturbance observer (NDOB1). Second, the ultra-local model (ULM) algorithm is employed to reconstruct the complex nonlinear PEMFC air supply system, avoiding the need for precise modeling and reducing the controller design difficulty. To estimate and compensate for the uncertain dynamics in the ULM algorithm, another NDOB2 is proposed to realize zero estimation error and finite-time observation. Besides, to achieve optimal control performance with less input chattering, finite-time convergence, and fast response speed, the [Formula: see text]-MF-STFOSMC is designed using super-twisting fractional-order sliding mode control (STFOSMC) algorithm. Furthermore, the stability of [Formula: see text]-MF-STFOSMC via a closed-loop system is verified using the Lyapunov theorem. Finally, the nonlinear PEMFC air supply system model with the proposed controller is realized in MATLAB/Simulink environment, and the simulation results are given to show the superiority and effectiveness of the proposed technique.

Linear Model Predictive Control and Back-Propagation Controller for Single-Point Magnetic Levitation with Different Gap Levitation and Back-Propagation Offline Iteration

Magnetic suspension balance systems (MSBSs) need to allow vehicle models to levitate stably in different attitudes, so it is difficult to ensure the stable performance of the system under different levitation gaps using a controller designed with single balance point linearization. In this paper, a levitation controller based on linear model predictive control and a back-propagation neural network (LMPC-BP) is proposed and simulated for single-point magnetic levitation. The deviation of the BP network is observed and compensated by an expansion state observer (ESO). The iterative BP neural network model is further updated using current data and feedback data from the ESO, and then the performance of the LMPC-BP controller is evaluated before and after the update. The simulation results show that the LMPC-BP controller can achieve stable levitation at different gaps of the single-point magnetic levitation system. With further updating and iteration of the BP network, the controller anti-jamming performance is improved.

Numerical investigation on secondary injection and thrust vector control in a planar double divergent nozzle

In this work, the performance of secondary injection thrust vector control of an altitude adaptive planar double divergent supersonic nozzle is numerically investigated. The compressible turbulent flow is solved using the kT−kL−ω transition-sensitive eddy-viscosity model. An analytical model to predict the separation distance and penetration height due to secondary fluid injection is developed based on the blunt-body theory, and it shows good agreement with the numerical estimation. The computational results indicate that injection pressure, injector location and injection angle significantly impact the thrust vector control performance. The core flow is disrupted by the momentum injection from the secondary fluid, causing the flow field structure to become asymmetrical. The wall pressure imbalance between the upper and lower nozzle increases as the secondary jet pressure elevates. Further, the thrust vectoring amplification factor is improved when the injector is placed downstream near the outlet since the shock reflection is circumvented. Indeed, that resulted in the generation of a large lateral thrust force. Most importantly, for a high injector slot angle, the primary downstream vortex disappears, and a strong secondary upstream vortex is generated, which shifts the primary upstream vortex opposite to the core flow, subsequently the shock separation is advanced. In addition, a secondary injector fitted near the exit with high inclination angles and injection pressure generated a lateral thrust of 20%.

Blockchain and Cryptography Framework of E-Apps with Big Data

With the tremendous and rapid evolution taking place in the field of technology and considering the rise in data volume that is dealt with daily, managing this data, whether in terms of security or terms of storage especially if the data is huge, is considered a necessary issue. Therefore, in this research, we established a framework that provides both security and storage/repository management. Where the security issue in the suggested framework is supported by the use of lightweight hash functions and public-key encryption algorithms represented by SPONGENT and elliptic curve cryptography (ECC). Additionally, the fast random number generator is also used to support the security algorithms included in the framework, while managing the repository storage is controlled by the use of a hybrid Blockchain to manage storage for this type of big data. The process of storing this data in databases or any of the traditional centralized methods exposes the data to loss or penetration. After analyzing the proposed framework, it successfully addressed the prevention of malicious within the field of proposed research attacks. Moreover, the performance analysis of the framework proposed was quite effective with the lightweight SPONGENT and ECC results, while the creation of blocks in the storage phase was not more than 0.18 ns. Thus, we obtained an effective framework in terms of security, performance, and terms of data repository management and control.

Integer PI, fractional PI and fractional PI data trained ANFIS speed controllers for indirect field oriented control of induction motor

Induction motor drives with variable speed applications that employ vector control are quite popular nowadays because they provide strong dynamic performance and flexible speed control. By decoupling the torque-producing current components of stator current from the rotor flux, Indirect Field Oriented Control is recognized for generating excellent performance in induction motor drives. This investigation is being done to show the effectiveness of the novel FPI input-output data-trained ANFIS controller and compare the three controllers' performance in terms of load variation capabilities, motor parameter variation, and speed tracking. Consequently, a comparison of the three controllers is important to select which controller performs high in induction motor drive. Indirect Field Oriented Control of induction motor with Fractional Proportional Integral (FPI), Integer Proportional Integral (IPI), and Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers are all discussed in this work along with their designs and comparative analysis. The square of error was used as a fitness function to genetically optimize the FPI and IPI controller parameters. The suggested Adaptive Neuro-Fuzzy Inference System (ANFIS) controller uses a hybrid learning approach. It is trained by the FPI controller's input-output data. Using the results of MATLAB simulations under various operating situations, the performance of the ANFIS controller was compared with FPI and IPI controllers. Because of FPI controller includes an extra parameter for adjustment, namely integration order, it performed better than IPI controller for speed control of the induction motor. According to the simulation findings, the percentage peak overshoots while employing ANFIS, FPI, and IPI controllers were 0.495 %, 12.062 %, and 14.699 % respectively. As a result, ANFIS exhibits a drastic reduction in overshoot. Additionally, with the ANFIS controlled induction motor drive, the speed achieves the required set value at 0.14 s. For no load, constant, and changing loads, the induction motor drive's performance has been examined.

Performance of MPPT Charge Controller Under Moderate - to High - Temperature Field Condition

Performance of MPPT Charge Controller Under Moderate - to High - Temperature Field Condition

Cold Sintering with Functionalized Polymers for Ductile Ceramic Matrix Composites with Controllable Mechanical Performance

Cold Sintering with Functionalized Polymers for Ductile Ceramic Matrix Composites with Controllable Mechanical Performance

The Distributed Adaptive Bipartite Consensus Tracking Control of Networked Euler–Lagrange Systems with an Application to Quadrotor Drone Groups

Actuator faults and external disturbances, which are inevitable due to material fatigue, operational wear and tear, and unforeseen environmental impacts, cause significant threats to the control reliability and performance of networked systems. Therefore, this paper primarily focuses on the distributed adaptive bipartite consensus tracking control problem of networked Euler–Lagrange systems (ELSs) subject to actuator faults and external disturbances. A robust distributed control scheme is developed by combining the adaptive distributed observer and neural-network-based tracking controller. On the one hand, a new positive definite diagonal matrix associated with an asymmetric Laplacian matrix is constructed in the distributed observer, which can be used to estimate the leader’s information. On the other hand, neural networks are adopted to approximate the lumped uncertainties composed of unknown matrices and external disturbances in the follower model. The adaptive update laws are designed for the unknown parameters in neural networks and the actuator fault factors to ensure the boundedness of estimation errors. Finally, the proposed control scheme’s effectiveness is validated through numerical simulations using two types of typical ELS models: two-link robot manipulators and quadrotor drones. The simulation results demonstrate the robustness and reliability of the proposed control approach in the presence of actuator faults and external disturbances.

Fuzzy logic based sliding surface adjustment of second-order sliding mode controllers

<p>This research work designs a variant of second-order sliding mode control scheme, making use of varying sliding surface inferred using a fuzzy inference system. The varying sliding surface is an effective strategy to improve controller performance. A surface with a relative degree of two is first built by accounting for the uncertainties and perturbances of the system. Thereafter, in order to enhance the dynamics of the system being controlled, a varying sliding surface based on a straightforward double input-single output fuzzy logic inference architecture is proposed. The controller ensures system's reaching conditions, and also the stability and robustness. The designed control scheme is studied in comparison with a sliding mode controller of second order having a constant surface of sliding using SIMULINK based simulation for a nonlinear system. The comparison shows that the proposed strategy exhibits an improved dynamic performance than the conventional sliding mode control of second order having a constant surface of sliding.</p>

Gray Matter Volumes Mediate the Relationship Between Disease Duration and Balance Control Performance in Chronic Ankle Instability.

The relationship between structural changes in the cerebral gray matter and diminished balance control performance in patients with chronic ankle instability (CAI) has remained unclear. This paper aimed to assess the difference in gray matter volume (GMV) between participants with CAI and healthy controls (HC) and to characterize the role of GMV in the relationship between disease duration and balance performance in CAI. 42 participants with CAI and 33 HC completed the structural brain MRI scans, one-legged standing test, and Y-balance test. Regional GMV was measured by applying voxel-based morphometry methods. The result showed that, compared with HC, participants with CAI exhibited lower GMV in multiple brain regions (familywise error [FWE] corrected p < 0.021). Within CAI only, but not in HC, lower GMV in the thalamus (β = -0.53, p = 0.003) and hippocampus (β = -0.57, p = 0.001) was associated with faster sway velocity of the center of pressure (CoP) in eyes closed condition (i.e., worse balance control performance). The GMV in the thalamus (percentage mediated [PM] = 32.02%; indirect effect β = 0.119, 95% CI = 0.003 to 0.282) and hippocampus (PM = 33.71%; indirect effect β = 0.122, 95% CI = 0.005 to 0.278) significantly mediated the association between the disease duration and balance performance. These findings suggest that the structural characteristics of the supraspinal elements is critical to the maintenance of balance control performance in individuals suffering from CAI, which deserve careful consideration in the management and rehabilitation programs in this population.

A novel class of non-Gaussian system performance assessment and controller parameter tuning methods

Traditional variance-based control performance assessment (CPA) and controller parameter tuning (CPT) methods tend to ignore non-Gaussian external disturbances. To address this limitation, this study proposes a novel class of CPA and CPT methods for non-Gaussian single-input single-output systems, denoted as data Gaussianization (inverse) transformation methods. The idea of quantile transformation is used to transform the non-Gaussian data with the goal of maximizing mutual information into virtual Gaussian data. In addition, optimal system data for the virtual loop are mapped back to the actual non-Gaussian system using quantile inverse transformation. Furthermore, a CARMA model-based recursive extended least square algorithm and a CARMA model-based least absolute deviation iterative algorithm are used to identify virtual Gaussian and non-Gaussian system process models, respectively, while implementing the CPT. Finally, a unified framework is proposed for the CPA and CPT of a non-Gaussian control system. The simulation results demonstrate that the proposed strategy can provide a consistent benchmark judgment criterion (threshold) for different non-Gaussian noises, and the tuned controller parameters have good performance.

Self-organizing feature selection fuzzy neural network-based terminal sliding mode control for uncertain nonlinear systems

A composite terminal sliding mode controller (CTSMC) for a kind of uncertain nonlinear system (UNS) is developed in this study. The primary aim of the design is to enhance the control performance of the CTSMC by learning its unknown parameters using a newly fuzzy neural network (FNN). Firstly, the stability and convergence of CTSMC for UNS with known parameters are demonstrated. Secondly, since some parameters of actual UNS are unmeasurable, a self-organizing feature selection fuzzy neural network (SOFSFNN) is intended to approach these unknown parts. Finally, the CTSMC using SOFSFNN is applied to UNS. The outcomes demonstrate that it has minimal tracking error, good robustness, and the ability to dynamically modify the network structure.

Comparison of the Performance of Type-1 and Interval Type-2 Fuzzy PI Controllers for Liquid Level Control in a Coupled Tank System

In this study, liquid level control in a coupled tank system is realized with PI controllers whose parameters are adapted using fuzzy logic approximation methods. Type-1 fuzzy PI control, interval type-2 fuzzy PI control and classical PI+Feedforward control methods are applied to the tank system in real time for different reference liquid levels. The performances of the controllers are compared in terms of system response parameters such as rise time, settling time and overshoot percentage. In addition, the performance evaluation of the controllers over a certain period of time is also compared by calculating integral-based concepts that express error performance metrics such as integral square error (ISE), integral time square error (ITSE), integral absolute error (IAE) and integral time absolute error (ITAE). The obtained results show that the type-2 fuzzy PI controller shows the best performance, followed by type-1 fuzzy PI control and classical PI+FF controller, respectively.

An Experimental Study on the Thermal Performance of a Heat Sink Filled with Porous Aluminum Skeleton/Paraffin Composite Phase Change Material.

Organic phase change material is an ideal solution to solve the heat dissipation problem of electronic devices. However, its low thermal conductivity limits its application. To solve this problem, a new porous aluminum skeleton/paraffin composite phase change material (AS-PCM) was prepared. The effects of porosity and porous aluminum skeletons on temperature control performance were explored. The experimental results show that the addition of AS significantly improves the thermal conductivity of organic PCM, and the thermal conductivity of AS-PCM is 32.3-59.6 times higher than that of pure paraffin. In addition, the temperature difference in AS-PCM with 75% porosity is 1-2 °C lower than that of AS-PCM with 85%, and 5-8 °C lower than that of AS-PCM with 95% porosity. The skeleton structure has an impact on the temperature control performance. The Mcc porous aluminum skeleton/paraffin composite phase change material (MAS-PCM) yields the best thermal performance compared with the Fcc porous aluminum skeleton/paraffin composite phase change material (FAS-PCM). The temperature control time of the MAS-PCM heat sink is increased by 5.3-50.8% relative to the FAS-PCM heat sink. The research results provide a novel approach for improving the thermal conductivity of PCMs.

Application of Artificial Intelligence to Improve Production Process Efficiency in Manufacturing Industry

The rapid advancement of Artificial Intelligence (AI) has profoundly impacted the manufacturing industry, offering transformative potential to enhance production process efficiency. This paper presents a systematic literature review of AI applications in the manufacturing sector, focusing on key AI technologies such as machine learning, robotics, predictive analytics, and natural language processing. The review highlights how these technologies have improved quality control, resource management, and overall operational performance. However, the adoption of AI also presents challenges, including significant investment costs, the need for a skilled workforce, and concerns over data security and privacy. Despite these challenges, the integration of AI in manufacturing presents numerous opportunities for future research and innovation, particularly in the areas of sustainable manufacturing and the convergence of AI with other emerging technologies. This study concludes that while AI offers substantial benefits for production efficiency, its successful implementation requires careful strategic planning and investment in both technology and human resources.

Host-Guest recognition strengthened supramolecular preformed particle gel for conformance control in low-permeability fractured reservoirs

The conventional preformed particle gels (PPG) have problems of irreversibly shear-breakage after shearing actions during the application for conformance control in low-permeability fractured reservoirs. Herein, a novel host–guest recognition strengthened supramolecular PPG (S-PPG) was synthesized with synergistically non-covalent and covalent crosslinking structures, using host (allyl β-CD) and guest (cetyldimethylallyl ammonium chloride) monomers. Firstly, the chemical structures and micro-morphologies of S-PPG were systematically characterized by FTIR, Cryo-SEM, EDS and AFM methods. Then, the dispersion and thermal stability, swelling properties and rheological properties, including viscoelasticity, creep-recovery and shear self-healing abilities, of S-PPG were investigated, respectively. Finally, the low-permeability cores with fractures were used to identify the injectivity and plugging properties of S-PPG and conventional PPG. The results showed that the S-PPG exhibited strong crosslinking network, high dispersion and thermal stability. In addition, the S-PPG also displayed better swelling, viscoelasticity, creep-recovery and shear self-healing ability after equilibrated swelling process compared to those of conventional PPG. Finally, the S-PPG exhibited considerable compatibility with low-permeability cores with fracture width of 0.05 mm, and produced higher plugging rate (93.53 %) than conventional PPG (86.35 %). Therefore, the newly formulated supramolecular preformed particle gel (S-PPG) containing host–guest recognition can provide a new approach for solving the problems of irreversibly shear-breakage and improving the conformance control performance of PPG in low-permeability fractured reservoirs.

Fatigue and depression at 8 weeks and 1 year after bacterial brain abscess and their relationship with cognitive status

ABSTRACT A bacterial brain abscess (BA) is a focal brain infection with largely unknown long-term implications. This prospective study assessed the frequency of fatigue and symptoms of depression at 8 weeks and 1 year after BA and examined the relationship between fatigue, depressive symptoms, and cognitive status. Twenty BA-patients (age 17–73; 45% female) were assessed for fatigue, depression, memory, and executive functions. Fatigue rates were 40-65% at 8 weeks and 25-33% at 1 year on various fatigue questionnaires. Patient Health Questionnaire indicated symptoms of depression in 10% at the 8-week follow-up only. Relevant comorbidities and vocational outcomes were not associated with fatigue or symptoms of depression. Mean fatigue scores improved significantly between the two-time points. Greater fatigue was related to subjective problems with working memory, inhibition, self-monitoring, and emotional control and worse objective verbal memory performance. Symptoms of depression were associated with one out of two fatigue measures. We conclude that fatigue is common in the first year after BA, and higher levels of fatigue are related to more cognitive problems. Symptoms of clinical depression were rare. These findings underscore fatigue as an important consequence of BA and emphasize the necessity for targeted rehabilitation interventions.