Control Performance Research Articles

A Fractional-Order Model Predictive Control Strategy with Takagi–Sugeno Fuzzy Optimization for Vehicle Active Suspension System

Aiming at the problem of system controller performance failure caused by improperly setting the value of each weighting coefficient of the model predictive control (MPC), a fractional-order MPC strategy with Takagi–Sugeno fuzzy optimization (T–SFO MPC) is proposed for a vehicle active suspension system. Firstly, the fractional-order model predictive control framework for active suspension systems is designed based on a 1/4 vehicle model. Then, we analyze the influence of different weighting coefficients on the suspension performance and introduce the Takagi–Sugeno fuzzy optimization theory to adaptively adjust the weighting coefficients of the fractional-order MPC controller. Finally, the system responses of the T–SFO MPC, traditional MPC, linear quadratic regulator (LQR), and passive suspension control are numerically analyzed under various road conditions. Simulation results show that suspension response with the T–SFO MPC is significantly improved compared with passive suspension control, traditional MPC control, and LQR control, and the weight coefficients of the T–SFO MPC can be adaptively adjusted according to the dynamic changes of suspension response. Compared with passive suspension, the root mean square (RMS) value of the vertical acceleration of the T–SFO MPC under various roads decreased by a maximum of 37.97%, and the RMS value of suspension dynamic deflection and tire dynamic load decreased by a maximum of 32.94% and 37.8%, respectively. These results validate that the proposed control method can achieve coordinated optimization of vehicle comfort and handling stability.

Multi-modal response control with multiple suspension-type tuned vibration absorbers

Due to complicated excitations, engineering structures are often subjected to multi-modal responses. Considering the feasibility in practical installation on slender structures, multiple Suspension-type Tuned Vibration Absorbers (S-TVAs) are investigated for multi-modal response control. Firstly, parametric optimization of a single S-TVA for single-modal response control is investigated analytically. The issues regarding to the optimal tuning, static and dynamic performances, and installation location are addressed. Subsequently, an optimal design method for multi-modal response control with multiple S-TVAs is presented. Two aspects on the optimization strategy are discussed. Consequently, the optimization should be performed with an inverse modal order sequence. And, the modal information should be updated considering the S-TVA optimized in the previous step. Finally, the effectiveness of the presented optimal design method is validated through practical wind-induced response control on a slender chimney. The most unfavorable response can be suppressed up to 59.7 %, which is 47.8 % better than traditional single-modal control approach. Moreover, it is interesting to find that the practical overall control performance may not be achieved with more controlled modes intuitively. It is recommended to select from several practical cases determined by the presented optimal design method. Practical installation and feasibility are highly required to be considered in practice.

Dorsolateral prefrontal cortex to ipsilateral primary motor cortex intercortical interactions during inhibitory control enhance response inhibition in open-skill athletes

Numerous studies have reported that long-term sports training can affect inhibitory control and induce brain functional alterations. However, the influence of environmental dynamics in sports training on inter-cortical connectivity has not been well studied. In the current study, we used twin-coil transcranial magnetic stimulation to investigate the functional connectivity between dorsolateral prefrontal cortex (DLPFC) and ipsilateral primary motor cortex (M1) during proactive and reactive inhibition in participants with sports skills in dynamic environment (open-skill experts), stable environment (closed-skill experts), and no sports skills (controls). Using a modified stop signal task, proactive inhibition was measured by the response delay effect (RDE), and reactive inhibition was measured by the stop-signal reaction time (SSRT). Intra-hemispheric DLPFC-M1 interactions and single pulse motor-evoked potentials (MEPs) were measured during the task. A stronger inhibitory effect of the DLPFC over M1 was observed during early reactive control stages compared to baseline levels. In addition, this inhibitory effect was pronounced when comparing open-skill experts to non-athlete controls, a relationship that was significantly correlated with superior reactive control performance. Furthermore, DLPFC to M1 influencing direction shifted from late proactive control to reactive control. Behavioral results also demonstrated enhanced proactive control abilities in open-skill experts relative to controls. Such enhancement may be due to the combination of environmental complexity and physical fitness in long-term skill training.

Learning-based adaptive neural control for safer navigation of unmanned surface vehicle with variable mass

This paper presents a novel approach to the precise control of variable mass unmanned surface vehicles (USVs) during payload deployment, where both mass and draught undergo unpredictable changes. We propose a draught observation method and an adaptive control strategy that leverages the strong coupling between the USV's motion states, mass, and draught. Our method employs a radial basis function neural network (RBF-NN) for real-time draught observation, using an offline training strategy based on gradient descent, combined with an adaptive online training strategy to improve observation accuracy. An adaptive control strategy based on the Backstepping method is then developed, incorporating real-time draught data from the RBF-NN to address unknown variations in mass and draught. The stability of both the RBF-NN observer and the adaptive control algorithm is rigorously verified using the Lyapunov method. Simulation results demonstrate that the proposed draught observation method achieves up to 30% faster convergence compared to traditional methods, with a significant improvement in observation accuracy. Furthermore, the adaptive control strategy effectively manages real-time adjustments in dynamic scenarios, maintaining robust control performance even under significant mass changes, where conventional approaches fail.

Advanced Methods for Verifying Memory Controllers in Modern Computing Systems

The primary component of the computer system that requires enhancement is the memory's performance. A memory controller helps in the management of control between the memory as well as the processor. This study gives a general outline of how controller performance in systems based on power converters may be improved using AI approaches. Among the many uses for power converters in today's electrical and energy systems are the effective control and conversion of energy in electric cars, renewable energy sources, and industrial automation, among others. Complementary control strategies used in classical approaches, although work well, may experience difficulties when it comes to system complication, change, and unpredictability. To overcome these restrictions, artificial intelligence methods appear rather prospective. Among the AI types one can distinguish neural networks, fuzzy logic, machine learning that may help to control the converters with enhanced efficiency by using adaptive and intelligent control. These approaches enhance control precision, succession, and reaction time to enhance system effectiveness and dependability. Moreover, this integration of AI enhances the most power converters by making it easier to predict maintenance, detect faults, and optimise for energy hence enhancing the systems. The paper presents new trends in the advanced smart control of the power converters, total control techniques, advantages as well as the challenges posed by AI control strategies. It also describes future trends and research areas for achieving enhanced performance in power converter-based systems through AI and Control techniques

Active Disturbance Rejection Control of Engine Speed in Series Hydraulic Hybrid Power System

Abstract: In this paper, a novel series hydraulic hybrid powertrain is proposed for a three-axis all-terrain vehicle. The engine drives two variable displacement pumps responsible for driving and steering, respectively. A variable displacement motor is connected to the ring gear of the planetary coupling mechanism to drive the vehicle and a fixed-displacement motor is connected to the sun gear to steer the vehicle. The active disturbance rejection control with feedforward control is employed to control the engine speed. The engine speed is controlled in a close-looped manner by adjusting the engine throttle. The controller parameters are decided by analyzing the influence of each parameter on the controller performance by means of the control variable method. The simulation results indicate that the proposed control strategy enables the vehicle to obtain better engine speed following and anti-disturbance performance. An all-terrain prototype is established and field tests are carried out to verify the effectiveness of the design and control strategy of the series hydraulic hybrid powertrain for the all-terrain vehicle.

Augmentation in Performance of Real-Time Balancing and Position Tracking Control for 2-DOF Ball Balancer System Using Intelligent Controllers

Augmentation in Performance of Real-Time Balancing and Position Tracking Control for 2-DOF Ball Balancer System Using Intelligent Controllers

Neutronics design of shutdown and control systems for a Zero Power Experiments of chloride-based molten salt fast reactor.

Nuclear power's role as a reliable, baseload, low-carbon source and its importance in achieving clean energy goals are being increasingly recognized with growing urgency around decarbonization of the global energy systems. However, to deliver a long-term sustainable solution, it is essential to develop innovative nuclear technologies for improving the fuel utilization and reducing the nuclear waste disposal challenge. Zero Power Reactors (ZPR) are an essential initial step for developing new nuclear technologies because they allow for testing and refinement in a safe environment before large-scale deployment. This paper discusses the design of a ZPR experiments for the development of iMAGINE, a novel chloride-based molten salt reactor technology. The paper presents a detailed analysis of the neutronic design for the shutdown and control systems of an experimental ZPR based on the iMAGINE molten salt reactor technology. The study concludes that a split-core design with a lower corner reflector as an extension of the lower annular reflector offers the most robust ZPR configuration, offering optimum operational margins and maneuverability. This design ensures safety, regulatory compliance, and sufficient control and shutdown performance for the successful development of the iMAGINE technology.

Radiative heat transfer to enhance the performance of liquid metal-based phase change heat sink under natural convection condition

Liquid metal phase change materials have the advantages of high thermal conductivity and high volumetric latent heat, which are expected to address the growing challenges of thermal management of advanced electronics. In previous studies, the effect of radiative heat transfer from fins of a phase change heat sink on thermal management performance has rarely been considered. In this study, radiative coating materials with high emissivity were prepared and coated on the fins of the liquid metal phase change heat sink. The effect of radiative heat transfer on the performance of liquid metal phase change heat sink was investigated. The experimental results of continuous heating under natural convection conditions show that the introduction of the radiative coating with an emissivity of 0.9298 can extend the time for the surface temperature of the heat source to reach 100 °C by 9.4%, while shortening the recovery time of the phase change heat sink by 14.9%. The results of high-power cyclic heating indicate that the high emissivity coating can reduce the peak temperature by 16.6 °C in the tenth working cycle. A simplified numerical model was subsequently developed and validated to determine the specific effects of phase change and radiative heat transfer on the overall thermal control performance. The radiation-enhanced liquid metal phase change heat sink proposed in this study is simple and maintenance-free. It is expected to address the thermal management issues of electronic devices that cannot use active cooling or operate in thin-air environments.

A perspective on alignment between steady‐state design and control for a distillation system in the context of flexibility analysis

AbstractThis perspective paper discusses the alignment between steady‐state design and control (dynamic) simulation in the context of flexibility analysis for distillation systems. Operational variations, such as fluctuations in feed composition or flowrate, can stem from external factors like raw material quality or temperature variations. These disturbances need to be mitigated to ensure optimal industrial performance. Traditionally, these process uncertainties are usually considered in a sequential manner, where the steady‐state flowsheet is established first, and control performance is analyzed afterwards. However, recent studies have started integrating flexibility index into the design phase to account for process uncertainties. While this integration has improved design strategies, aligning steady‐state and dynamic control simulations for comprehensive flexibility analysis remains underexplored. In this paper, we wish to share new perspectives and insights we have observed by analyzing the existing literature. We highlight some points not immediately apparent in our previous studies, hoping these insights will serve as guidelines for future research in this direction. We believe that validating results between steady‐state and control simulations is a long‐term endeavor that cannot be conclusively addressed soon. However, we hope this paper will be valuable to future researchers, contributing to advancing this field and reaching those who will appreciate and build upon these insights. © 2024 Society of Chemical Industry (SCI). Published by John Wiley & Sons Ltd

Validation of a Model Predictive Control Strategy on a High Fidelity Building Emulator

In recent years, advanced controllers, including Model Predictive Control (MPC), have emerged as promising solutions to improve the efficiency of building energy systems. This paper explores the capabilities of MPC in handling multiple control objectives and constraints. A first MPC controller focuses on the task of ensuring thermal comfort in a residential house served by a heat pump while minimizing the operating costs when subject to different pricing schedules. A second MPC controller working on the same system tests the ability of MPC to deal with demand response events by enforcing a time-varying maximum power usage limitation signal from the electric grid. Furthermore, multiple combinations of the control parameters are tested in order to assess their influence on the controller performance. The controllers are tested on the BOPTEST framework, which offers standardized test cases in high-fidelity emulation models, and pre-defined baseline control strategies to allow fair comparisons also across different studies. Results show that MPC is able to handle multi-objective optimal control problems, reducing thermal comfort violations by between 66.9% and 82% and operational costs between 15.8% up to 20.1%, depending on the specific scenario analyzed. Moreover, MPC proves its capability to exploit the building thermal mass to shift heating power consumption, allowing the latter to adapt its time profile to time-varying constraints. The proposed methodology is based on technologically feasible steps that are intended to be easily transferred to large scale, in-field applications.

Model predictive control for autonomous vehicle path tracking through optimized kinematics

Tracking performance and stability of path tracking is crucial for unmanned vehicles' navigational tasks. Researches on vehicle path tracking controllers primarily rely on dynamic models. In contrast, there are less designs and researches that focus on path tracking controller based on kinematic model. This scarcity may stem from the perceived inadequacy in the accuracy of vehicle kinematic models. This research introduces a novel method for modifying the traditional vehicle kinematic model by incorporating a front wheel steering angle modification function to enhance tracking performance of path tracking controllers based on kinematic models. In terms of control strategy selection, the study opted for nonlinear model predictive control with terminal cost. A controller which is founded on the modified model was used on a simulated vehicle on CarSim-Simulink platform to assess the tracking performance. The simulation was designed with a double-shift line reference trajectory and the initial speeds of the simulated vehicle are 5 m/s and 10 m/s, respectively. The results of the simulations indicate that employing a controller based on the modified model could reduce the peak tracking error by 76.45 % and 43.06 %, and the root mean square of the tracking error was reduced by 73.29 % and 36.06 %, respectively.

A study of multi-model identification and fusion control algorithms for rotary limit devices

In order to improve the control performance of rotating limit device on the motion range of rotating parts of mechanical equipment, the multi model identification and fusion control algorithm of rotating limit device is studied. The angle sensor and speed sensor are used to collect the rotation angle and speed of rotating machinery equipment. Taking the collected data as the input of multiple support vector machines, the D-S evidence theory is selected to fuse the status of rotating machinery equipment identified by multiple models and determine the final status of rotating machinery equipment. Set the deviation between the target angle and the actual angle of the rotating mechanical equipment as the input of the PID controller. The PID controller determines the limit control quantity of the rotating limit device to the rotating machinery equipment, and realizes the rotating limit control of the rotating machinery equipment. The rotary table is selected as the mechanical equipment controlled by the rotary limit device. The experimental results show that the rotary limit device can control the rotation of the rotary table in the ideal angle range, achieve smooth tracking of the rotation angular velocity, and has excellent control performance.

Distributed robust tracking control for multiple unmanned aerial vehicles: theory and experimental verification

The distributed trajectory tracking problem for multiple UAVs under unknown external disturbance is investigated. A new nonlinear robust trajectory tracking strategy for multiple UAVs is proposed. The dynamic model for the UAVs' formation is illustrated in the Tangent frame. For the trajectory tracking problem, a Robust formation tracking control strategy based on robust integral of the signum of error (RISE) is designed to compensate for the effects of unknown external disturbances, which improves the robustness of the UAVs' formation system. Based on the Lyapunov stability analysis, it is proved that the global asymptotic convergence of the coordination errors and the semi-global asymptotic convergence of the UAVs' position tracking errors are achieved. The proposed formation control strategy is validated via real-time experiments that are performed on the self-build UAVs' formation flight control testbed. Flights without wind disturbance and flights with disturbance are all performed. The performance comparison experiment with the conventional sliding mode control algorithm is performed. The experimental results show that the designed control strategy can achieve good coordinated trajectory tracking of multiple UAVs, and has better control performance compared with normal sliding mode control law.

Adaptive hierarchical interval type-2 fuzzy control for trajectory tracking of an underactuated AUV with uncertain dynamics

In this study, an adaptive hierarchical interval type-2 fuzzy control (AHIT2FC) scheme is presented for three-dimensional (3D) trajectory tracking control of an underactuated autonomous underwater vehicle (AUV), which has only three independent control inputs and is subject to partially parametric uncertainty and unknown external disturbances. To decrease reliance on hydrodynamic parameters, an interval type-2 fuzzy logic system (IT2FLS) is utilized, which exhibits superior capabilities in handling high levels of uncertainties prevalent in the underwater environment as compared to a type-1 fuzzy logic system. However, the traditional fuzzy system structure encounters the “curse of dimensionality” issue, which results in high computational complexity and imposes a significant burden on the limited computing resources underwater. The proposed AHIT2FC framework, based on a hierarchical structure, can substantially enhance computational speed while ensuring excellent control performance. Subsequently, Lyapunov’s theory is adopted to guarantee that all the tracking errors in the closed-loop system are semiglobally uniformly bounded (SGUB). Finally, comparative simulation results demonstrate the effectiveness and superiority of the proposed AHIT2FC scheme.

Adaptive neural network backstepping control for the magnetorheological semi-active air suspension system with uncertain mass and time-varying input delay

Aiming at rejecting external disturbance induced by the random road profile, this paper proposes an adaptive robust control strategy to address the control problem of semi-active air suspension system installed with magnetorheological dampers (MRD) for enhancing ride comfort and handling performance. To effectively depict the inherent nonlinear dynamics of the adjustable air spring, a radial basis function neural network (RBFNN) model is trained by the experimental data offline, and an adaptive learning algorithm is introduced to maintain the modeling accuracy. Moreover, to handle the prevalent sensitive parameter variation (such as sprung mass), a nonlinear estimator is designed to estimate the uncertain sprung mass. Furthermore, a delay compensator is integrated into the control law to mitigate the impact of the time-varying input delay caused by the actuator of MRD to restrain the chattering phenomena. Additionally, the stability of the closed-loop system is rigorously proven by employing a Lyapunov–Krasovskii functional, guaranteeing the boundedness of both tracking error and estimation error. Simulation results are displayed and analyzed, illustrating the feasibility and efficiency of the proposed control strategy in depressing the sprung mass acceleration and tire deflection, showing its significance in both control performance enhancement and chattering elimination.

Trajectory tracking multi-constraint model predictive control of unmanned vehicles based on sideslip stiffness estimation with XGBoost algorithm

When the vehicle is driving at high speed on a slippery road, the sideslip stiffness of the tire which is closely related to the lateral force of the tire, will vary with the change of road conditions, tire inflation pressure, vertical load, and other factors. If the tire sideslip stiffness is set to a fixed value in the control system based on the model design, the uncertainty of the sideslip stiffness will cause a large error with the actual application. Therefore, in order to improve the trajectory tracking accuracy of the vehicle on the slippery road surface under the premise of ensuring the stability of the vehicle, an intelligent vehicle trajectory tracking control strategy considering the influence of sideslip stiffness is designed in this paper. In order to solve the multiple constraints of the vehicle driving on the low-adhesion road surface, a trajectory tracking controller based on the multi-constraint model predictive control algorithm is designed, and then the tire sideslip stiffness estimation strategy is designed based on the XGBoost algorithm, and the estimated sideslip deviation stiffness is added to the trajectory tracking control model. Carsim and MATLAB/Simulink are used to perform co-simulation to verify the accuracy of the proposed tire sideslip stiffness estimation and the tracking performance of the trajectory tracking controller. In order to further verify the reliability of the research content, the relevant working condition tests are carried out on the hardware-in-the-loop platform based on Carsim and LabVIEW, and the effectiveness of the trajectory tracking controller considering the influence of sideslip stiffness is verified.

Anti‐Interference Sliding Mode Control of PMSM Based on Improved Salp Swarm Algorithm

AbstractAiming at the problem that the control performance of permanent magnet synchronous motor is easily affected by load disturbance during operation, a fractional‐order sliding mode control method combining fractal‐order sliding mode controller (FOSMC) and fractional‐order sliding mode load torque observer (FOSMO) is proposed. The parameters of this method are tuned using the improved salp swarm algorithm (ISSA). Initially, the fractional‐order sliding mode controller is designed by applying fractional‐order calculus theory to enhance the system's response speed. Subsequently, a fractional‐order sliding mode load torque observer is developed to improve the system's anti‐interference capability and robustness due to the slow response and low identification accuracy of traditional load torque observers. Lastly, the control parameters are self‐tuned using the improved salp swarm algorithm due to the difficulty of adjusting them manually. Simulation and experimental results demonstrate that the proposed control method exhibits good convergence, no overshoot, minimal buffeting, and strong robustness against uncertain factors like load disturbances. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Optimized Trajectory Tracking for ROVs Using DNN + ENMPC Strategy

This study introduces an innovative double closed-loop 3D trajectory tracking approach, integrating deep neural networks (DNN) with event-triggered nonlinear model predictive control (ENMPC), specifically designed for remotely operated vehicles (ROVs) under external disturbance conditions. In contrast to single-loop model predictive control, the proposed double closed-loop control system operates in two distinct phases: (1) The outer loop controller uses a DNN controller to replace the LMPC controller, overcoming the uncertainties in the kinematic model while reducing the computational burden. (2) The inner loop velocity controller is designed using a nonlinear model predictive control (NMPC) algorithm with its closed-loop stability proven. A DNN + ENMPC 3D trajectory tracking method is proposed, integrating a velocity threshold-triggered mechanism into the inner-loop NMPC controller to reduce computational iterations while sacrificing only a small amount of tracking control performance. Finally, simulation results indicate that compared with the ENMPC algorithm, NMPC + ENMPC can better track the desired trajectory, reduce thruster oscillations, and further minimize the computational load.

Structural Analysis of Hyaluronic Acid Fillers Using Nuclear Magnetic Resonance: Implications for Quality Control and Clinical Performance.

Potential disruptions in the biocompatibility of hyaluronic acid (HA) fillers can arise with mono-linked 1,4-butanediol diglycidyl ether (BDDE) or unreacted (pendant) 1,4-butanediol di-(propan-2,3-diolyl) ether. Assessing the filler's degree of modification involves evaluating improperly cross-linked BDDE. This study analyzed commercially available HA fillers using nuclear magnetic resonance (NMR), focusing on key parameters, such as the degree of modification (MoD), the cross-linker ratio (CrR), and the degree of cross-linking. We assessed thirteen commercially available HA fillers using NMR. The samples were placed in an NMR instrument, and each sample was analyzed for 26 h, including MoD and CrR assessments. MoD 1H ranged from 17.065% to 2.239%, MoD 13C ranged from 12.567% to 1.947%, and CrR 13C ranged from 0.394 to 0.014. Significant distinctions were observed in the CrR 13C values when the MoD values of the products were similar. This study underscores the importance of considering the MoD and the CrR together to ensure optimal cross-linking and minimize the risks associated with residual BDDE impurities. Utilizing NMR for HA gel characterization provides valuable insights regarding product quality control, safety assessments, and clinical performance evaluations for esthetic interventions, contributing to filler product improvements. Further studies correlating NMR findings with real-world outcomes are essential for ensuring safety and efficacy.