Proportional Integral Differential Controller Research Articles

Neural network optimizer of proportional-integral-differential controller parameters

Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID 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.

Model-Free RBF Neural Network Intelligent-PID Control Applying Adaptive Robust Term for Quadrotor System

This paper proposes a quadrotor system control scheme using an intelligent–proportional–integral–differential control (I-PID)-based controller augmented with a radial basis neural network (RBF neural network) and the proposed adaptive robust term. The I-PID controller, similar to the widely utilized PID controller in quadrotor systems, demonstrates notable robustness. To enhance this robustness further, the time-delay estimation error was compensated with an RBF neural network. Additionally, an adaptive robust term was proposed to address the shortcomings of the neural network system, thereby constructing a more robust controller. This supplementary control input integrated an adaptation term to address significant signal changes and was amalgamated with a reverse saturation filter to remove unnecessary control input during a steady state. The adaptive law of the proposed controller was designed based on Lyapunov stability to satisfy control system stability. To verify the control system, simulations were conducted on a quadrotor system maneuvering along a spiral path in a disturbed environment. The simulation results demonstrate that the proposed controller achieves high tracking performance across all six axes. Therefore, the controller proposed in this paper can be configured similarly to the previous PID controller and shows satisfactory performance.

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.

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.

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.

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.

Design and Implementation of an Optimized PID Controller for Two-Limb Robot Arm Control

With developing technology, robot arms are used in more areas, and this is directly proportional to the work done for its development. Studies on robot arms are generally focused on control. The controllability of robot arms generally provides speed and precision.
 Within the scope of this study, the control optimization of a two-arm robot arm with an optimized proportional integral differential controller (PID) was carried out using a microcontroller. The kinematic operations required to control a two-arm robot arm have been developed by the MATLAB Support Package for Arduino Hardware. The transfer function required for the control system was used for a direct current (DC) brushed motor, using the values given in the motor data sheet. Feedback is provided for the control system thanks to the Hall effect encoder.
 The gripper end of the two-limbed robot arm follows the specified square-shaped reference. In this study, where PID controller was used, controller parameters were obtained with particle swarm, artificial bee colony and chaos game metaheuristic optimization algorithms for square orbit and these parameters were used on the produced robot arm.
 Many methods have been used in the literature to determine PID parameters. In this study, the chaos game metaheuristic optimization algorithm, which has become popular in recent years, was used to determine the parameters of the PID controller.

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.

Micro-vibration control of spaceborne payload with smart parallel struts

Currently, the observation resolution of Earth observation satellites and space telescopes is progressively increasing. However, vibrations induced by solar panel vibrations, control torque gyroscopic reaction forces, and internal satellite noise significantly impede the observation accuracy of space-borne precision optical payloads. To mitigate the impact of vibrations on precision payload, it is necessary to incorporate isolation devices into the transmission path between the payload and spacecraft bus. This paper aims to design a six-degree-of-freedom parallel struts for active control of vibration in sensitive payloads. An optical payload isolation system is constructed using a Gough-Stewart parallel configuration with flexible hinges. The dynamic model of the system is established by the Newton-Euler method. The dynamic characteristics and response of the isolation system are analyzed. Then the active controller is designed based on the Fuzzy Auto-Disturbance Rejection Control strategy, and the effectiveness of the controller is simulated and compared with the fuzzy Proportion Integration Differentiation (PID) control algorithm. The results show that the active parallel struts exhibit excellent vibration isolation performance and possess strong robustness, which provides a theoretical reference for further space applications.

Fuzzy adaptive PID control for path tracking of field intelligent weeding machine

In order to reduce the influence of external interference on the intelligent weeder, improve the stability of its path tracking system, enhance its weeding effect, and reduce the rate of seedling injury, the motion steering control of the four-wheeled weeder is studied. The control strategy of fuzzy adaptive proportional integral differential (PID) algorithm is determined by establishing the path tracking mathematical model of the field intelligent weeder; the fuzzy adaptive PID controller is designed with the input average steering angle deviation of the front wheels and the rate of change of the deviation so as to realize the automatic adjustment and optimization of the parameters. We use Simulink to build the control system model and to compare and analyze with the PID controller. The results show that under the action of the step signal, the rise time of the fuzzy adaptive PID and PID control system responses is 2.596 and 4.209 s, respectively; under the action of the impulse, the fuzzy adaptive PID has a significant advantage over the traditional PID control system in terms of the rise time and response time. In addition, this control system has a fast response speed, high adaptability, high anti-interference ability, and superior path tracking ability, which are necessary for improving the accuracy of the field intelligent weeder and reducing the rate of seedling injury.

Intelligent control of electric vehicle air conditioning system based on deep reinforcement learning

The existing AC system of EVs consumes a lot of energy in summer, resulting in reduced range. Additionally, the cabin temperature (Tcabin) fluctuates due to varying driving conditions. In this paper, an innovative cooling strategy is designed to address the above drawbacks using deep reinforcement learning algorithms. The primary objective of this strategy is to achieve high-precision temperature control while concurrently minimizing the energy consumption of the AC system. The strategy combines compressor speed (Ncompressor) and blower airflow (Bairflow) control to optimize the AC system's performance. The RL controller minimizes energy consumption by reducing Ncompressor while increasing Bairflow, thus ensuring a constant cooling capacity (Q). And it effectively reduces temperature fluctuations within the cabin. Finally, the RL control strategy was compared with on/off control and proportional integral differential (PID) control. The results showed that RL control performed well in the multi-objective optimization of the AC system. In terms of temperature control, it effectively reduces the amount of overshooting, and the minimum temperature fluctuation achieved through RL control is merely 0.07 °C, exhibiting an 84.4 % and 86.5 % decrease compared to traditional control methods. The average absolute temperature error stands at 0.06–0.07℃, maintaining a remarkable precision in preserving the target temperature. Furthermore, RL control not only ensures superior energy efficiency but also reduces energy consumption by up to 5.98 % and 7.65 % compared with traditional control methods.

Laser diode driver on a programmable system on a chip.

A comprehensive solution of a laser diode (LD) driver with temperature control using a programmable system on a chip is proposed as an alternative to dedicated devices. A digital proportional integral differential controller is used for regulating the LD temperature. The light-current characteristics and frequency response of the LD are measured using an external photodiode and interrogated by an integrated transimpedance amplifier. The 0.084% stability, 2kHz bandwidth, and 0.11% full scale current error are demonstrated. The LD overshoot protection is digitally implemented, providing a soft start. While the circuit is initially optimized for a 3W infrared LD, instructions are provided for fine-tuning the design according to specific LD requirements.