Model Of Control System Research Articles

Design and Optimization of FOPID Controller for Precise Power Head Control in Reverse Drilling Rigs Using Gray Wolf Algorithm

Reverse well drilling rig is an important support for realizing deep mineral resource extraction, and the precise control law of its power head is crucial for improving drilling efficiency and reducing safety accidents. In challenging environments and varying rock strata distributions at different well depths, Proportional-Integral-Derivative (PID) controller is used to control the power head of the reverse drilling rig, despite the simple control, flexible and convenient adjustment, but due to the fact that there are only three parameters regulated by the PID controller, the control is not precise enough and the adjustment accuracy is not high enough, Overcoming the drawbacks of PID control technology at a fundamental level can be an extremely challenging task, which results in the failure of real-time feedback and precise control of the power head of the reverse drilling rig and precise control. The research is based on the theory of wellbore engineering machinery, fractionalorder control technology and intelligent optimization algorithm. Using a combination of physical modeling, mathematical modeling and simulation test analysis, the intelligent optimization algorithm (Gray Wolf Optimization (GWO)) is used to parameterize the fractional-order PID (FOPID) controller and apply it to the rotational speed control of power head. The energy conservation relationship is coupled to the kinetic mathematical model of the power head. Laplace transform analysis is utilized to derive the integer-order mathematical transfer function linking the power head speed and the electric displacement signal. The fractional-order mathematical transfer function is subsequently determined using the least square method. With the help of MATLAB-Simulink software, build a model of electro-hydraulic proportional control system for countershaft drilling rigs, adopt six intelligent optimization algorithms to adjust the parametric fractional-order controller, and examine the impact of step disturbances on various control combination methods of electro-hydraulically coupled power head and countershaft drilling rig motors through the three dynamic indexes of standard variance, overshooting amount and stabilization time. Bode and Nyquist plots are used to determine that the system has good stability and robustness with a controller. The simulation analysis confirms the reasonableness and accuracy of the gray wolf algorithm (GWO) for adjusting the FOPID parameters, and the joint simulation with the help of AMEsim and Simulink software verifies the stability and timeliness of the variable pump’s response, and at the same time confirms that the control method of the gray wolf algorithm (GWO) has a very good control effect. This research establishes a solid theoretical groundwork for controlling the power head with precision and the development of wellbore engineering. It also serves as a guide for creating motion control methods for large mechanical equipment.

Design and Real-Time Control of an Electric Furnace with Three-Dimensional Heating

Currently, heating in electric ovens is generally achieved using heaters placed on the top and bottom surfaces. In some advanced ovens, heaters are installed on the back surface, allowing 3D heating. However, in these ovens, temperature measurements are obtained from a single point, making the temperature control unreliable. Additionally, this type of oven cannot provide homogeneous heating since it lacks heaters on the left, right, or front surfaces. In this study, a unique electric oven equipped with heaters and temperature sensors was designed and produced on all six surfaces. To model its performance, the heating behavior of the oven was derived using the Ziegler–Nichols tangent method, and the gain factors for the proportional (P), proportional-integral (PI), and proportional-integral-derivative (PID) controllers were determined. Subsequently, real-time digital control of the oven was performed using on-off, P, PI, and PID controllers, which ensured a comprehensive evaluation of the oven’s control performance. The experimental results showed that homogeneous heating could only be achieved when all panels were energized. Additionally, the PI and PID controllers stabilized the system with a maximum steady-state error of 1.3 °C in all cases, demonstrating the accuracy of the derived system model and adequacy of the implemented control system.

Industrial pipeline vibration attenuation with magnetorheological tuned mass damper

In order to solve the high-frequency vibration problem of industrial pipeline in petrochemical enterprises, a tuned mass damper (TMD) based on magnetorheological (MR) technology was proposed. The magnetorheological-tuned mass damper (MR-TMD) can reduce the vibration response of the structure by tuning resonance with the pipeline system, it can also have the semi-active control characteristics of MR devices. By analyzing the vibration signals collected on the pipeline site, the relevant design requirements and parameters of the damper were determined. The optimal frequency ratio, passive damping, and mass ratio of the damper were determined using the optimal design theory. Meanwhile, the high-performance MR composite was prepared for the working conditions of MR-TMD and a modified Herschle-Bulkley model was derived to characterize the behavior of the material. Furthermore, the mechanical performance of the proposed MR-TMD was tested, the output damping force is about 890.5 N when the applied current is 2 A, which can meet the working requirements of pipeline vibration attenuation. Then, according to the established vibration control model of pipeline system, the vibration system was simulated and analyzed combined with LQR algorithm, and the vibration attenuation performance in different conditions were predicted and compared. The research shows that the TMD combined with MR technology can effectively reduce the vibration of industrial pipeline system.

Performance-based reliability modelling of displacement control system considering the internal degradation and external impacts

Performance-based reliability modelling of displacement control system considering the internal degradation and external impacts

Research on the Coordinated Control of Mining Multi-PMSM Systems Based on an Improved Active Disturbance Rejection Controller

This study focuses on the problems of poor control performance, synchronization performance and stability in multi-motor permanent magnet drive systems in mining belt conveyors when a Proportional Integral Derivative (PID) controller is used to control the multi-motor. In this paper, a system model for three-motor synchronous control of a mine belt conveyor is established. On this basis, an Enhanced first-order Active Disturbance Rejection Controller (Efal_ADRC) is designed based on an optimized nonlinear function. Additionally, a weighted arithmetic mean is used to enhance the compensator of the ring coupling control structure. Finally, the system model is evaluated and simulated using various algorithms. Results show that synchronous control of a multi-Permanent Magnet Synchronous Motor (multi-PMSM) drive system based on the Efal_ADRC ring coupling control structure has better anti-interference ability, control accuracy and synchronization, which is conducive to the stable and efficient safe operation of the belt conveyor.

The Simulation of Pose Control for Stewart Platform Based on Gain-Enhanced PID with Generalized Boolean Algebra

Due to the nonlinear and time-varying uncertainties commonly present in practical industrial systems, traditional PID controllers often fail to achieve optimal control performance under significant input variations. This paper proposes a gain-enhanced PID control method optimized by generalized Boolean algebra, applying Boolean algebraic logic control strategies to traditional PID control to improve parameter adaptability and real-time control performance. Using MATLAB/Simulink simulation tools, a dual-loop control simulation model is established for the Stewart platform, incorporating both conventional PID control and the gain-enhanced PID control optimized by generalized Boolean algebra. Both models are configured with identical PID parameters for simulation, and comparative experimental results are presented. The experimental results demonstrate that, in the Simulink simulation model of the Stewart platform's pose control system, the proposed gain-enhanced PID control system optimized by generalized Boolean algebra improves the dynamic performance and response speed compared to the traditional PID control system, enhancing the real-time performance and stability of the Stewart platform control.

A physiologically-based quantitative systems pharmacology model for mechanistic understanding of the response to alogliptin and its application in patients with renal impairment.

Alogliptin is a highly selective inhibitor of dipeptidyl peptidase-4 and primarily excreted as unchanged drug in the urine, and differences in clinical outcomes in renal impairment patients increase the risk of serious adverse reactions. In this study, we developed a comprehensive physiologically-based quantitative systematic pharmacology model of the alogliptin-glucose control system to predict plasma exposure and use glucose as a clinical endpoint to prospectively understand its therapeutic outcomes with varying renal function. Our model incorporates a PBPK model for alogliptin, DPP-4 activity described by receptor occupancy theory, and the crosstalk and feedback loops for GLP-1-GIP-glucagon, insulin, and glucose. Based on the optimization of renal function-dependent parameters, the model was extrapolated to different stages renal impairment patients. Ultimately our model adequately describes the pharmacokinetics of alogliptin, the progression of DPP-4 inhibition over time and the dynamics of the glucose control system components. The extrapolation results endorse the dose adjustment regimen of 12.5mg once daily for moderate patients and 6.25mg once daily for severe and ESRD patients, while providing additional reflections and insights. In clinical practice, our model could provide additional information on the in vivo fate of DPP4 inhibitors and key regulators of the glucose control system.

Performances improvement of DC Motor using a Fractional Order Adaptive PID Controller optimized by Genetic Algorithm

In the past 20 years, scientists and engineers have rediscovered fractional calculus and have begun using it in more and more domains, most notably control theory. This study introduces a fractional adaptive PID (FAPID) controller which incorporates an additional parameter to enhance the performance of a conventional adaptive PID (APID) controller. A comparative analysis is conducted between the APID and FAPID controllers optimized using the metaheuristic Genetic Algorithm (GA). The evaluation uses a linearized model of the DC motor control system. The results demonstrate that FAPID controllers significantly outperform conventional APID controllers, particularly regarding rise time, settling time, overshoot, and mean absolute error. Among the proposed designs, the integration of FAPID proves to be the most effective in achieving a balance between responsiveness and stability, exhibiting exceptional robustness and adaptability to variations in DC motor and environmental conditions. This method can be extended to various fractional and integer systems to enhance their efficiency and reduce noise disturbance.

Testability modeling and analysis of cabin pressure control system based on multi-signal flow graphs

Testability modeling and analysis of cabin pressure control system based on multi-signal flow graphs

Modeling and simulation of a real-time control system for a planar redundant parallel robot

Modeling and simulation of a real-time control system for a planar redundant parallel robot

Программная модель интеллектуальной системы управления сложными процессами переработки мелкорудного сырья

Intelligent process control systems allow analyzing incoming information about the control object and the external environment at a qualitatively new level and, on this basis, increasing the efficiency of the entire production. The article presents the results of a study aimed at developing a software model of an intelligent control system for complex processes of small-scale ore processing. The complexity of the processes lies in the interrelationship of many variables describing the technological units involved in processing. An analytical description of such interrelations allows describing processes with high accuracy, but leads to complex calculations that are difficult to apply in real conditions. The use of new computational algorithms from the group of intelligent methods made it possible to overcome this contradiction, ensuring, on the one hand, good accuracy of solutions, and on the other hand, making it possible to automate the adjustment of the control system to changing input effects and external conditions. The basis of the proposed software model is a deep neural network of long short-term memory, solving the regression problem when analyzing input data and calculating control effects. The novelty of the research results is the structure of the software model of the intelligent control system, including a neural network as a regulator and a hierarchical system of fuzzy inference for a generalized assessment of the control qualities. An original feature of the software structure is the use of a calculator of derivatives of different orders to feed them to the input of the neural network, which helps to expand its receptive field and increases the accuracy of its results. Testing of the proposed structure of the software model was carried out in the MatLab-Simulink environment. The results of the simulation experiment showed that, unlike the PID controller, the use of a deep neural network as a regulator allows for successful compensation of the influence of external factors on the quality of control.

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.

Development of a prioritized traffic light control system for emergency vehicles

This research presents a model for an adaptive traffic signal control system aimed at improving urban traffic regulation. It dynamically adjusts signal timing based on vehicle volume at intersections, prioritizing emergency vehicles by allowing them immediate passage. Utilizing Arduino coding, the system controls traffic light intensity according to the traffic flow, enhancing road safety and efficiency. This innovative approach not only facilitates faster clearance for emergency services without human intervention but also reduces congestion and accident rates. This research creates a model for a prioritized traffic signal control system. When the vehicular volume at the intersection varies, the signal time alters autonomously. It identifies the ambulance/emergency vehicles and allows the green light for emergency vehicles like ambulances, and fire engines. This approach may be used to detect traffic accidents and infractions of automobile spiral motions. When erected on the road, the entire system allows for quick traffic clearing for rescue vehicles without requiring a policeman. The system's design eliminates the need for sensors or radio frequency identification (RFID) tags, simplifying traffic management. Simulations validate that emergency vehicle travel time is significantly reduced, proving the system's effectiveness in streamlining urban traffic flows.

Characteristic analysis method for integrated multi-parameter hydro-viscous speed control system

Hydro-viscous clutch has become an inevitable choice for special vehicle transmissions. As a nonlinear dynamic system with large lagging link, its timing performance is affected by input rotational speed, lubricating oil temperature and pressure and other factors. However, from the control perspective, the speed regulation law, formation mechanism control characteristics, global model of hydro-viscous speed control system (HSCS) are unclear. To solve these problems, this paper presents a comprehensive analysis of the Hydro-viscous Speed Control System (HSCS), focusing on its steady-state and dynamic speed control characteristics. A data-driven model is established to describe the relationship between input rotational speed, output speed, control oil pressure, and lubricating oil temperature. The findings provide a foundation for optimizing HSCS structure and parameters, enhancing the performance and reliability of such systems in special vehicle transmissions and establishing a temperature-speed control system for special vehicle transmissions.

Adaptive Weighted Particle Swarm Optimization for Controlling Multiple Switched Reluctance Motors with Enhanced Deviatoric Coupling Control

Switched reluctance motors (SRMs) are widely used in industrial applications due to their advantages. Multi-motor synchronous control systems are crucial in modern industry, as their control strategies significantly impact synchronization performance. Traditional deviation coupling control structures face limitations during the startup phase, leading to excessive tracking errors and exacerbated by uneven load distribution, resulting in desynchronized motor acceleration and increased speed synchronization errors. This study proposes a modified deviation coupling control method based on an adaptive weighted particle swarm optimization (PSO) algorithm to enhance multi-motor synchronization performance. Traditional deviation coupling control applies equal reference torque inputs to each motor’s current loop, failing to address uneven load distribution and causing inconsistent accelerations. To resolve this, a gain equation based on speed deviation is introduced, incorporating self-tracking error and gain coefficients for dynamic synchronization error compensation. The gain coefficients are optimized using the adaptive weighted PSO algorithm to improve system adaptability. A simulation model of a synchronization control system for three SRMs was developed in the Matlab/Simulink R2023b environment. This model compares the synchronization performance of traditional deviation coupling, Fuzzy-PID improved structure, and adaptive PSO improved structure during motor startup, sudden speed increases, and load disturbances. The validated deviation coupling control structure achieved the initial set speed in approximately 0.236 s, demonstrating faster convergence and a 6.35% reduction in settling time. In both the motor startup and sudden speed increase phases, the two optimized methods outperformed the traditional structure in dynamic performance and synchronization accuracy, with the adaptive PSO structure improving synchronization accuracy by 54% and 37.17% over the Fuzzy-PID structure, respectively. Therefore, the PSO-optimized control system demonstrates faster convergence, improved stability, and enhanced synchronization performance.

Development of mathematical modeling of a mobile robot's motion control system

This study focuses on developing a mathematical model for precise control and stabilization of unmanned aerial vehicles (UAVs) in various spatial conditions. Addressing the problem of achieving precise control and stability, the proposed solution designs a control system based on a linear-quadratic controller (LQR) and simulates it using a proportional-derivative (PD) controller implemented in Matlab/Simulink. The results demonstrate high precision and stability in controlling the UAV motion parameters – roll, pitch, yaw, and altitude. This important level of performance is achieved due to the adaptivity of the LQR-based control system, which optimizes control actions according to the unsteady dynamics of the UAV. The integration of the PD controller improves responsiveness and stability, providing precise motion control over a range of spatial states. These features effectively solve the problem by handling the complex dynamics of the UAV and providing precise control. The results are explained by the ability of the LQR to provide optimal control laws that minimize deviations using a quadratic cost function, while the PD controller quickly corrects errors and responds to disturbances. The benefits of this approach include a significant reduction in control errors by about 25–30 %, increased response speed to external disturbances, and reduced computational latency due to efficient processing compared to more resource-intensive methods such as model predictive control. The developed mathematical model can be applied in practice in conditions requiring robust real-time control and adaptation to dynamic changes in the environment. It is especially suitable for industries such as logistics, surveillance, and environmental monitoring, providing an effective and optimal solution for stabilizing and controlling the motion of UAVs in various spatial states. This approach improves the performance of UAVs and expands their capabilities in various operating conditions.

Swiftly Chasing Gravitational Waves across the Sky in Real Time

We introduce a new capability of the Neil Gehrels Swift Observatory, dubbed “continuous commanding,” that achieves 10 s latency response time on orbit to unscheduled target-of-opportunity requests received on the ground. We show that this will allow Swift to respond to premerger (early-warning) gravitational-wave (GW) detections, rapidly slewing the Burst Alert Telescope (BAT) across the sky to place the GW origin in the BAT field of view at or before merger time. This will dramatically increase the GW/gamma-ray burst (GRB) codetection rate and enable prompt arcminute localization of a neutron star merger. We simulate the full Swift response to a GW early-warning alert, including input sky maps produced at different early-warning times, a complete model of the Swift attitude control system, and a full accounting of the latency between the GW detectors and the spacecraft. 60 s of early warning can double the rate of a prompt GRB detection with arcminute localization, and 140 s guarantees observation anywhere on the unocculted sky, even with localization areas ≫1000 deg2. While 140 s is beyond current GW detector sensitivities, 30–70 s is achievable today. We show that the detection yield is now limited by the latency of LIGO/Virgo cyberinfrastructure and motivate a focus on its reduction. Continuous commanding has been integrated as a general capability of Swift, significantly increasing its versatility in response to the growing demands of time-domain astrophysics. We demonstrate this potential on an externally triggered fast radio burst (FRB), slewing 81° across the sky, and collecting X-ray and UV photons from the source position <150 s after the trigger was received from the Canadian Hydrogen Intensity Mapping Experiment, thereby setting the earliest and deepest such constraints on high-energy activity from nonrepeating FRBs. The Swift Team invites the community to consider and propose novel scientific applications of ultra-low-latency UV, X-ray, and gamma-ray observations.

Theoretical Analysis on Active Polarization Control of Fiber Laser Based on Root Mean Square Propagation Algorithm

High-power linearly polarized fiber lasers are widely used in coherent beam combination, nonlinear frequency conversion, and gravitational wave detection. With the increase in output power, it is challenging for fiber lasers to maintain a high polarization extinction ratio (PER). Combined with intelligent techniques, active polarization control is a prospective method to obtain the laser output with high PER and high stability. We demonstrate a comprehensive model of an active polarization control system. The root mean square propagation (RMS-Prop) algorithm is used to control the non-polarization-maintaining (non-PM) fiber laser to generate linearly polarized laser. The parameters of the RMS-Prop algorithm are theoretically analyzed, including cost function, perturbation amplitude, and global learning rate. The simulation results show that PER is the optimal cost function. When the perturbation amplitude is 0.06 and the global learning rate is 0.6, the system can achieve the optimal control speed and accuracy. By comparison with the stochastic parallel gradient descent (SPGD) algorithm, the RMS-Prop algorithm has an advantage in obtaining higher PER.

Analytical model of an active noise control system for performance analysis of adaptive algorithms in HVAC system

In real-world acoustic scenarios related to the problem with the low-frequency noise such as HVAC systems, the signal is a stochastic, non-stationary and time-varying process where the performance of the active noise control technique mainly depends on the characteristics of the adaptive filtering techniques. In this paper, a method for analytical modeling of active noise control of signals in MATLAB/Simulink is presented, in which the acquisition of audio signals is provided by using a National Instruments control and acquisition module. For the realization of the active noise control, an adaptive filtering system model for signal analysis has been developed, using three adaptive algorithms (LMS,RLS and NLMS). The analysis of the characteristics of each of the three investigated adaptive algorithms is performed according to four parameters: Mean Square Error (MSE), convergence speed, stability and robustness. The conclusions are presented through a comparative analysis of the results obtained from the implemented methodology. The individual characteristics and parameters that are observed for correct adaptation and optimal results of each of the adaptive filters are explained in detail.

A Target Tracking Method Based on the Self-organizing Fuzzy Extended Kalman Filter for UAV

T his paper proposed a novel approach based on integrated tracking and positioning for Unmanned Aerial Vehicle (UAV) systems. In the proposed scheme, a self-organizing fuzzy Extended Kalman Filter (EKF) system was studied to track the flight trajectory of an UAV. With this goal, the control model of the UAV system with the disturbances was established based on the dynamic equation firstly. Secondly, the self-organizing Fuzzy EKF system was designed to identify and estimate the errors of the UAV position adaptively. And then, the trajectory tracking control was determined for the UAV according to the obtained errors. Finally, the proposed strategy was applied to a UAV to demonstrate the effectiveness.