Proportion Integration Differentiation Research Articles

Synchronization of complex networks with PID pinning controller

Recently, synchronization of complex networks has been studied deeply; however, most existing works take the standard proportional control strategy approach to pinning controllers, which produces a steady-state error and has a significant time of fluctuation. In order to solve this problem, we propose a new method using the Proportional-Integral-Differential (PID) control to design a pinning control method for synchronization of complex networks. Based on Lyapunov theory, the stability of the network is proved, and sufficient synchronization conditions are obtained. Finally, examples are given to evaluate the effectiveness of the proposed PID pinning method and compare it to the traditional proportional pinning controller and adaptive pinning controller.

Novel study on active reliable PID controller design based on probability density evolution method and interval-oriented sequential optimization strategy

Vibration active control is a significant problem in practical engineering, but the influence of hybrid uncertainties in practical engineering cannot be ignored. For the uncertainties with adequate data, probability theory can be used while for the uncertainties with insufficient data, probability theory is limited and interval quantification can be used. Taking the widely used proportional-integral-differential (PID) control as an example, a novel study on controller design based on probability density evolution method and interval-oriented sequential optimization strategy considering interval and random uncertainties is proposed. Firstly, under a certain sample point of interval uncertainties, probability density evolution equation is utilized to calculate the probability density function of uncertain response considering only random uncertainties. In this way the probabilistic reliability corresponding to the sample point can be obtained. Then the hybrid reliability is defined as the average probability within the interval uncertainty and the adaptive dichotomy method is utilized to choose proper sample points of interval uncertainties, in which the reliability curve and the interval uncertainty axis is regarded as the judgement of convergence. Next the hybrid reliability is introduced to the optimization as a constraint and the PID gains reliable design optimization is established to balance the energy-consuming and safety. Finally, the sequential optimization strategy is adopted to improve the efficiency of optimization. In every iteration step of the optimization, the constraint of deterministic optimal design is adjusted according to the static reliability at the moment before the passage or reaching the maximum value. Three numerical examples are presented to demonstrate the accuracy and practicability of the proposed framework.

First realization of LHW-plasma coupling feedback control for long pulse operation in EAST

Abstract In order to sustain good LHW (lower hybrid wave) -plasma coupling for long pulse plasma operation, it is the first time that the coupling feedback control is designed and realized in EAST through PID (Proportion Integration Differentiation) method by choosing RC (reflection coefficient of LHW power) as the reference for the feedback of gas puffing, including one pulse test and multi-pulse experiments. Experiments show that such feedback control can work correctly and keeps good LHW-plasma coupling effectively for long time, suggesting the possibility of feedback control application on LHW-plasma coupling in long pulse plasma. Furthermore, during the process of feedback control of multi-pulse SMBI (supersonic molecular beam injection), the stored energy changes from 29kJ to 58kJ, and the energy confinement factor (H89) increases from 0.98 to 1.45, implying a positive effect of coupling feedback on plasma performance. Experiments between SMBI puffing and the gas-puffing fed by piezoelectric valve near the antenna are further investigated, showing that the response time on RC with SMBI is faster than that by the piezoelectric valve and that SMBI puffing in the electric drift side of LHW antenna is a little quicker than that in the ion drift side. Studies suggest that such feedback control is effective for long pulse LHW-plasma coupling and the gas puffing by SMBI in the electric drift side of LHW antenna could offers an effective way to sustain good LHW coupling in steady state operation in future. Further optimization will be continued later.

Multi-timescale neuromodulation strategy for closed-loop deep brain stimulation in Parkinson’s disease

Objective. Beta triggered closed–loop deep brain stimulation (DBS) shows great potential for improving the efficacy while reducing side effect for Parkinson’s disease. However, there remain great challenges due to the dynamics and stochasticity of neural activities. In this study, we aimed to tune the amplitude of beta oscillations with different time scales taking into account influence of inherent variations in the basal ganglia–thalamus–cortical circuit. Approach. A dynamic basal ganglia–thalamus–cortical mean–field model was established to emulate the medication rhythm. Then, a dynamic target model was designed to embody the multi-timescale dynamic of beta power with milliseconds, seconds and minutes. Moreover, we proposed a closed-loop DBS strategy based on a proportional–integral–differential (PID) controller with the dynamic control target. In addition, the bounds of stimulation amplitude increments and different parameters of the dynamic target were considered to meet the clinical constraints. The performance of the proposed closed-loop strategy, including beta power modulation accuracy, mean stimulation amplitude, and stimulation variation were calculated to determine the PID parameters and evaluate neuromodulation performance in the computational dynamic mean–field model. Main results. The Results show that the dynamic basal ganglia–thalamus–cortical mean–field model simulated the medication rhythm with the fasted and the slowest rate. The dynamic control target reflected the temporal variation in beta power from milliseconds to minutes. With the proposed closed-loop strategy, the beta power tracked the dynamic target with a smoother stimulation sequence compared with closed-loop DBS with the constant target. Furthermore, the beta power could be modulated to track the control target under different long-term targets, modulation strengths, and bounds of the stimulation increment. Significance. This work provides a new method of closed–loop DBS for multi-timescale beta power modulation with clinical constraints.

Variable Universe Fuzzy-Proportional-Integral-Differential-Based Braking Force Control of Electro-Mechanical Brakes for Mine Underground Electric Trackless Rubber-Tired Vehicles.

Currently, the main solution for braking systems for underground electric trackless rubber-tired vehicles (UETRVs) is traditional hydraulic braking systems, which have the disadvantages of hydraulic pressure crawling, the risk of oil leakage and a high maintenance cost. An electro-mechanical-braking (EMB) system, as a type of novel brake-by-wire (BBW) system, can eliminate the above shortcomings and play a significant role in enhancing the intelligence level of the braking system in order to meet the motion control requirements of unmanned UETRVs. Among these requirements, the accurate control of clamping force is a key technology in controlling performance and the practical implementation of EMB systems. In order to achieve an adaptive clamping force control performance of an EMB system, an optimized fuzzy proportional-integral-differential (PID) controller is proposed, where the improved fuzzy algorithm is utilized to adaptively adjust the gain parameters of classic PID. In order to compensate for the deficiency of single-close-loop control and adjusting the brake gap automatically, a cascaded three-closed-loop control architecture with force/position switch technology is established, where a contact point detection method utilizing motor rotor angle displacement is proposed via experiments. The results of the simulation and experiments indicate that the clamping force response of the proposed multi-close-loop Variable Universe Fuzzy-PID (VUF-PID) controller is faster than the multi-closed-loop Fuzzy-PID and cascaded three-close-loop PID controllers. In addition, the chattering of braking force can be suppressed by 17%. This EMB system may rapidly and automatically finish the operation of the overall braking process, including gap elimination, clamping force tracking and gap recovery, which can obviously enhance the precision of the longitudinal motion control of UETRVs. It can thus serve as a BBW actuator of mine autonomous driving electric vehicles, especially in the stage of braking control.

Research on the Autonomous Control Technology Used in the Slurry Mixing System of Cementing Units

Cementing is a critical link in oil and gas exploitation, in which slurry density control is particularly important. In this study, we examined a slurry mixing control system in order to solve the problem of time delays in the mixing system. The model of a slurry mixing system was built in accordance with the system’s structure. A Smith fuzzy PID (proportion integration differentiation) composite control solution is proposed herein, and the simulation results show that the adjustment time and overshoot are lower than those of the conventional PID control and Smith predictive compensation control. A genetic algorithm is utilized to optimize the quantization factor and scale factor of the Smith fuzzy PID controller. Following optimization, the rise time of the controller was found to be 0.45 s, which represents a decrease of 35.9%, the overshoot was reduced by 0.4%, and the stabilization time was reduced by 36.6%. Afterward, we built a cementing slurry mixing simulation experimental platform, and experiments were used to verify the feasibility and superiority of the Smith fuzzy PID controller optimized by the genetic algorithm in comparison with the conventional controllers. The study results thus provide a scientific basis for the engineering application of the autonomous control technology of the slurry mixing system in cementing units.

Application of improved particle swarm optimization algorithm in the proportional integral differential-controlled semi-active suspension system

To enhance the control performance of semi-active suspension systems, this research proposes a particle swarm optimization algorithm (PSO) with adaptive nonlinear correction of inertia weights, which is then integrated with a proportional integral differential (PID) algorithm. To this end, this research establishes quarter semi-active and passive suspension models of automobiles by utilizing the Matlab/Simulink simulation platform. In this foundation, this research further compares the advantages and disadvantages regarding performance indexes of semi-active suspension controlled by the adaptive inertia weighted particle swarm optimization (APSO) algorithm and the PID algorithm, as well as the PID-controlled semi-active suspension and passive suspension through simulation. Simulation results indicate that performance indicator values for different suspension types increase with higher pavement grades. Compared with passive suspension, the semi-active suspension controlled by APSO and PID algorithms presents significantly improved performance indexes, with reductions of at least 31.61% in root mean square (RMS) concerning body vertical acceleration, 1.78% in suspension dynamic deflection, and 22.13% in tire dynamic loads. Moreover, analysis of suspension system frequency response characteristics demonstrates a significant decrease in droop acceleration transmission rate for the semi-active suspension with APSO and PID algorithms across the whole frequency range compared with that of the PID-controlled suspension and passive suspension. On the same note, despite the higher values of suspension dynamic deflection and tire dynamic load transfer rate in certain frequency bands, they are generally within acceptable suspension limits. Simply put, the findings confirm the feasibility of applying the APSO algorithm in PID-controlled semi-active suspension systems, which effectively improves both vehicle ride comfort and handling stability.

Oxygen starvation control of proton exchange membrane fuel cell through fusion control strategy

Oxygen excess ratio (OER) is regarded as a significant indicator of the safety and efficient operation of the open-cathode proton exchange membrane fuel cell (PEMFC) system. This is because inappropriate OER is prone to the phenomenon of oxygen starvation and oxygen saturation under load conditions change frequently. To avoid the phenomenon, this paper integrates linear active disturbance rejection control (LADRC) and proportional integral differential (PID) control to regulate OER of fuel cell air supply system, which effectively combines model-based and data-driven LADRC structure. As the controller parameters directly affect the control performance and it is difficult to obtain the optimal control parameters. Especially, when the load conditions change, the control parameters are difficult to adapt the load changes. To this end, a snake optimization (SO) algorithm with easy implementation and fast convergence is used to realtime update the controller parameters, which can further improve the control effects. Firstly, an equivalent linear model of fuel cell air supply system is established. Secondly, a hybrid LADRC method is developed by combining the merits of the LADRC and PID methods. Thirdly, the SO algorithm is used to optimize the controller parameters, which can achieve the optimal control parameters. Finally, the results revealed that the proposed method has a faster response with less noise sensitivity against the load changes and stronger robustness than the other methods.

A New Kind of Atomic Force Microscopy Scan Control Enabled by Artificial Intelligence: Concept for Achieving Tip and Sample Safety Through Asymmetric Control

This paper introduces a paradigm shift in atomic force microscope (AFM) scan control, leveraging an artificial intelligence (AI)-based controller. In contrast to conventional control methods, which either show a limited performance, such as proportional integral differential (PID) control, or which purely focus on mathematical optimality as classical optimal control approaches, our proposed AI approach redefines the objective of control for achieving practical optimality. This presented AI controller minimizes the root-mean-square control deviations in routine scans by a factor of about 4 compared to PID control in the presented setup and also showcases a distinctive asymmetric response in complex situations, prioritizing the safety of the AFM tip and sample instead of the lowest possible control deviations. The development and testing of the AI control concept are performed on simulated AFM scans, demonstrating its huge potential.

Suppression of Torque Ripple in Switched Reluctance Motors Which is Based on Synchronization Technology

The double salient pole structure of Switched Reluctance Motor (SRM) makes its electromagnetic field exist nonlinear saturation characteristics, resulting in its large torque pulsation in operation, so it is difficult to achieve speed regulation smoothly by traditional control methods. In view of this problem, a sliding mode control strategy which is based on synchronous transmission technology was proposed.Firstly, the basic structure of switched reluctance motor was analyzed, and the mathematical model of mechanical motion of switched reluctance motor was established. Secondly, an improved sliding mode controller which is based on synchronous signal transmission technology was designed by analyzing the reason of large torque ripple of switched reluctance motor, and the stability of the system was proved. Finally, simulation is used to verify the effectiveness of the control strategy.Compared with the traditional PID (Proportional Integral Differential) control algorithm, this control technology not only suppresses the SRM torque ripple effectively , but also makes the sliding mode controller output the precise target electromagnetic torque quickly by increasing the control variables. The results of research indicate that this design can not only restrain the torque ripple effectively, but also adjust the convergence speed and overshoot of the controller by adjusting the design parameters.

Data‐driven model‐free predictive control for microstage with coupling and hysteresis nonlinearities

SummaryThe precision motion control problem is investigated in this paper for microstages with cross‐axial coupling and hysteresis. Cross‐axis coupling generally results in stress‐stiffening effects, thereby causing time‐varying dynamics in the microstages. Additionally, when a microstage is driven by piezoelectric actuators (PEAs), the hysteresis effect of the actuator itself must also be considered. Modeling the microstages becomes complicated when both nonlinear characteristics, coupling and hysteresis, coexist. To address this challenge without the need for modeling, a novel data‐driven model‐free predictive control scheme called first‐order tensor‐vector product polynomial approximation based model‐free predictive control (TPPA ‐MFPC is proposed. TPPA ‐MFPC solely relies on the sampling input/output (I/O) data of the systems. The main concept behind TPPA ‐MFPC is to derive a linear approximation model using the I/O data collected during operation. This linear approximation model then serves as a nominal model in a predictive controller, enabling the control of the microstages. Finally, the effectiveness of the proposed TPPA ‐MFPC scheme and the performance improvement over existing model‐free schemes, for example, proportion integration differentiation control (PID), model‐free adaptive control (MFAC), model‐free adaptive predictive control (MFAPC), data‐dependent LMI (DDLMI), and data‐enabled predictive control (DeePC) are demonstrated in the simulation examples with a 2‐degree of freedom (DOF) multileaf spring‐based microstage driven by PEA.

Synchronous Control of High-Speed Train Lift Wing Angle of Attack Drive System Based on Chaotic Particle Swarm Optimization and Linear Auto-Disturbance Resistant Controller

In this study, a control scheme is proposed based on Chaotic Particle Swarm Optimization (CPSO) to enhance the Linear Auto-Disturbance Rejection Controller (LADRC). The focus is on addressing the challenge of high-precision variations in angle-of-attack through dual-motor cooperative control within the lifting wing of a high-speed train. The scheme initiates with the design of a dual-loop structure for LADRC, integrating position and current control. The position loop is further refined. Subsequently, the CPSO algorithm is employed to optimize the parameters of the LADRC controller. Ultimately, the loop is closed by feeding back the position error in the cross-coupled structure to the current loop, thereby achieving high-precision control. The performance of the proposed structure is validated through both Matlab/Simulink simulations and an experimental platform. The experimental results demonstrate that CPSO-LADRC, in comparison to traditional LADRC and Proportion-Integration-Differentiation (PID) control, exhibits an increase in the maximum response time by 3.76 s and 3.3 s, respectively, a reduction in overshoot by 1.12% and 0.8%, as well as a decrease in the maximum synchronization error by 0.45 cm and 1 cm, respectively. These findings validate the effectiveness of the proposed synchronous loop controller method in simplifying computational complexity, enhancing system responsiveness, robustness, and synchronization performance. Additionally, our approach facilitates precise angle-of-attack transformation for the lifting wings of high-speed trains effectively.

Virtually constrained generalized relative motion modeling and a control parameter optimizer for automatic carrier landing

PurposeThis paper aims to propose a novel control scheme and offer a control parameter optimizer to achieve better automatic carrier landing. Carrier landing is a challenging work because of the severe sea conditions, high demand for accuracy and non-linearity and maneuvering coupling of the aircraft. Consequently, the automatic carrier landing system raises the need for a control scheme that combines high robustness, rapidity and accuracy. In addition, to exploit the capability of the proposed control scheme and alleviate the difficulty of manual parameter tuning, a control parameter optimizer is constructed.Design/methodology/approachA novel reference model is constructed by considering the desired state and the actual state as constrained generalized relative motion, which works as a virtual terminal spring-damper system. An improved particle swarm optimization algorithm with dynamic boundary adjustment and Pareto set analysis is introduced to optimize the control parameters.FindingsThe control parameter optimizer makes it efficient and effective to obtain well-tuned control parameters. Furthermore, the proposed control scheme with the optimized parameters can achieve safe carrier landings under various severe sea conditions.Originality/valueThe proposed control scheme shows stronger robustness, accuracy and rapidity than sliding-mode control and Proportion-integration-differentiation (PID). Also, the small number and efficiency of control parameters make this paper realize the first simultaneous optimization of all control parameters in the field of flight control.

Application of a Neural Network on Steam Drum Pressure Control in a Fixed-Bed Reactor for Exothermic Reactions

The control of steam drums, used to remove heat from Fischer–Tropsch synthesis or diethyl oxalate hydrogenation, is confronted with a challenge on controlling quality. The traditional proportional–integral–differential (PID) controllers with fixed parameters are dissatisfying upon deployment. The backward-propagation neural network (BPNN) self-tuning PID control algorithm was thus developed and implemented via a Python and KINGVIEW software combination. Application experiments showed that, in both setpoint control and step change control of the steam drum pressure, static deviation and the maximum error were less with the BPNN self-tuning PID controller, in comparison to the conventional PID controller. Moreover, it seemed that certain adaptations occurred to the nonlinear change in the reaction system, revealing that it was superior to the traditional PID controller. It is shown that the backward-propagation neural network will improve the control quality in boiling water drum systems for exothermic reactions. It can be predicted that the backward-propagation neural network is qualified for process condition control in the chemical industry.

Research of the readout electronics for X-ray beam-position feedback system of SAPS

For 4th generation synchrotron radiation (SR) light sources, the X-ray beam-position stability is one of the crucial factors in cutting-edge experiments. Instead of traditional passive vibration isolation techniques, active vibration isolation measures based on feedback control technology have begun to play an important role for stabilizing the beam-position. The readout electronics of the feedback control system is presented in the paper. The proposed design is based on the integration of proportion integral differential (PID) controller and system on chip (SoC) module. By using the ARM for analyzing the convergence speed of PID controller with different parameters and configuring it, the readout electronics flexibly adapts to different vibration environments. The readout circuits based on this method is implemented for southern advanced photon source, and tests are carried out in the laboratory to evaluate the readout electronics. The results show that it can weaken the influence of vibration sources below 5Hz on the beam-position.

Dynamic characteristics and deep reinforcement learning of proportional-integral-differential controller for quadruped stator-based ultrasonic linear motor

Ultrasonic linear motors have many advantages, such as small size, light weight, and a lack of electromagnetic interference. In our previous study, a quadruped stator-based ultrasonic linear motor that mimics the motion of an inchworm based on longitudinal and bending modes was proposed. However, our previous studies focused on voltage control, overlooking the influence of phase, which made accurate and sustainable operation difficult. This study measures the dynamic characteristics of this motor using polymer-based rods and combines deep reinforcement learning (DRL) with proportional-integral-differential (PID) algorithms for both position and speed control through phase manipulation. The proposed DRL-PID controller outperforms voltage control in both simulation and real-world experiments.

Temperature control of beer fermentation based on variable domain fuzzy PID and neural network technology and its application analysis

AbstractTemperature control in the beer manufacturing process is crucial for product quality. Given the gap between China's automation in beer production and the international level, improving the technology in this area has gradually become a core issue in optimizing domestic beer production. This study combines a proportional integral derivative controller with a fuzzy modeling strategy and incorporates a variable‐domain structure to propose a variable‐domain fuzzy proportional integral derivative controller control method. To cope with the challenges of production interaction, the study also introduces neural network technology. The experimental data indicated that the variable‐domain fuzzy proportional integral derivative controller outperforms the conventional proportional integral derivative controller and the fuzzy proportional integral differential controller in terms of overshooting, with a maximum overshoot of only 1.0, compared with 0.50 and 0.70, respectively. The variable‐domain fuzzy proportional integral differential controller exhibited a minimal overshoot of only 0.01 when the model parameter is increased by 20%. In comparison, the other methods reach overshoot values of 0.92 and 1.0. The proposed method maintained superior stability even under the influence of impulse disturbance, step disturbance, and modeling variations. These results demonstrated that the research method is significantly more stable than both the proportion integration differentiation (PID) controller and fuzzy PID controller in complex dynamic parameter environments. The proposed method involved 60 rounds of neural network control, which was successfully implemented. The temperature readings T1 and T2 remained stable within the range of 1.0%–1.02% throughout the experiments. The study demonstrates that the proposed methods have higher accuracy and less fluctuation in actual application, making them more available. Taken together, the above results show that the combination of variable‐domain fuzzy PID controller and neural network technology in beer production has achieved excellent control results. This study not only provides a strong technical support for the progress of beer production technology in China, but also has important industrial application value and wide promotion prospects.

On the use of PID control to improve the stability of the quad-rotor UAV

The recent development of drones has aroused great concern at home and abroad. For civil industry and agricultural plant protection industry, it can provide safer and more stable flight and improve production efficiency. For the exploration industry, the application of UAV (Unmanned Aerial Vehicle) will reduce unnecessary casualties. In places where damage is serious, UAV will bring accurate on-site information and data more quickly. It can make drones to replace people in high-risk operations, scientific and technological agricultural development and survey more stable and safe; For the military industry, it can be applied to different combat environments to increase its stability. In recent years, although UAV combat and reconnaissance are not the mainstream combat methods, UAV has become or an indispensable part. Improving the stability of UAV will also increase the military combat capability, so UAV has good development prospects and economic benefits. Nowadays, PID (Proportion Integration Differentiation) control plays an indispensable role in quad-rotor UAV. How to use different types of PID control combined with existing intelligent technology to improve the stability of quad-rotor UAV is a hot issue in recent years. This paper summarizes the previous research results in this field, briefly describes the motion mechanism of four-rotor UAV, wind disturbance resistance mechanism, the action principle and contract between traditional PID control and fuzzy PID control, as well as the effect of intelligent algorithm on PID control addition, and puts forward suggestions for PID control algorithm of the future UAV.

A temperature control method of hot-bar soldering based on extended Kalman filter

PurposeThis study aims to introduce a novel technique for nonlinear sensor time constant estimation and sensor dynamic compensation in hot-bar soldering using an extended Kalman filter (EKF) to estimate the temperature of the thermocouple.Design/methodology/approachTemperature optimal control is combined with a closed-loop proportional integral differential (PID) control method based on an EKF. Different control methods for measuring the temperature of the thermode in terms of temperature control, error and antidisturbance are studied. A soldering process in a semi-industrial environment is performed. The proposed control method was applied to the soldering of flexible printed circuits and circuit boards. An infrared camera was used to measure the top-surface temperature.FindingsThe proposed method can not only estimate the soldering temperature but also eliminate the noise of the system. The performance of this methodology was exemplary, characterized by rapid convergence and negligible error margins. Compared with the conventional control, the temperature variability of the proposed control is significantly attenuated.Originality/valueAn EKF was designed to estimate the temperature of the thermocouple during hot-bar soldering. Using the EKF and PID controller, the nonlinear properties of the system could be effectively overcome and the effects of disturbances and system noise could be decreased. The proposed method significantly enhanced the temperature control performance of hot-bar soldering, effectively suppressing overshoot and shortening the adjustment time, thereby achieving precise temperature control of the controlled object.

Dynamic performance of strategy for transitioning from conventional bottom otter trawling to semi-pelagic trawling through towing tank experiments and numerical simulations

An innovative trawling strategy for transitioning from conventional bottom trawling to semi-pelagic trawling (elevating the otter board but allowing the trawl net to contact the seabed) was developed to mitigate the effects of seabed impact, and its dynamic performance was evaluated. Experiments were carried out using a 1/18 scale physical model of the otter trawl at a speed of 0.6 m/s, and the warp length was tuned in the range of 4–8 m under constant-speed control of the trawl winch for validating the numerical simulations. Numerical simulations were then conducted to investigate the effects of towing speed and winch speed on the dynamic loads of the otter trawl system and movement of the otter board, eventually develop a fuzzy or proportion-integration-differentiation (PID) controller for the trawl system. Numerical results showed the sharp increase of the warp tension and the overshoot of the otter board at the constant-speed control occurred, and the magnitudes of both raised as the towing speed and winch speed increased. The otter board spread was significantly reduced during the transition to semi-pelagic trawling at a low towing speed. The use of fuzzy and PID control successfully eliminated the overshoot and diminished the high warp tension.