Traditional Proportion Integration Differentiation Research Articles

Research on adaptive hydraulic drive optimization control of concrete mixing tank truck for open-pit mine.

The non-axisymmetric problem caused by the fluid sloshing in the tank of a mining concrete mixing tank truck during driving is affected by the excitation of complex road surfaces. The fluid sloshing is coupled with the dynamics of the vehicle body due to the excitation of the complex road surface. The traditional hydraulic drive proportional integral differential (PID) control method is not effective in dealing with such problems, which can easily lead to accidents such as overturning. To improve the accuracy and stability of the hydraulic drive control system, this paper proposes an optimized particle filter PID adaptive control method based on the elastic firefly (FA) algorithm to accelerate the convergence speed of control parameter optimization, and then analyzes its hydraulic drive control characteristics and structural applications, and discusses step steering and double lane change modes are simulated under filling rates of 1.5 and 2.0, respectively. The experimental results show that compared with traditional PID control, the proposed adaptive control method can significantly reduce the average speed error of hydraulic drive control to 0.03km/h and the maximum speed error to 0.17km/h. It also improves the control tracking performance and stability. The practicality of the adaptive hydraulic drive is verified in the filling rate experiments under step steering and double-lane shifting conditions. It has important reference value for the practical application of hydraulic drive control optimization of mining concrete mixing transport tank trucks.

A Deep Reinforcement Learning Approach to Injection Speed Control in Injection Molding Machines with Servomotor-Driven Constant Pump Hydraulic System

The control of the injection speed in hydraulic injection molding machines is critical to product quality and production efficiency. This paper analyzes servomotor-driven constant pump hydraulic systems in injection molding machines to achieve optimal tracking control of the injection speed. We propose an efficient reinforcement learning (RL)-based approach to achieve fast tracking control of the injection speed within predefined time constraints. First, we construct a precise Markov decision process model that defines the state space, action space, and reward function. Then, we establish a tracking strategy using the Deep Deterministic Policy Gradient RL method, which allows the controller to learn optimal policies by interacting with the environment. Careful attention is also paid to the network architecture and the definition of states/actions to ensure the effectiveness of the proposed method. Extensive numerical results validate the proposed approach and demonstrate accurate and efficient tracking of the injection velocity. The controller’s ability to learn and adapt in real time provides a significant advantage over the traditional Proportion Integration Differentiation controller. The proposed method provides a practical solution to the challenge of maintaining accurate control of the injection speed in the manufacturing process.

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.

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.

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.

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.

Fuzzy proportional integral differential control of magnetorheological clutch torque based on bee colony algorithm optimization

As an intelligent transmission device, the magnetorheological (MR) clutch cannot realize its accurate torque control because it is difficult to establish its accurate torque control mathematical model. This paper designs a squeeze shear MR clutch. In order to achieve accurate torque control of the MR clutch, this paper proposes a fuzzy proportional integral differential (PID) control strategy based on the optimization of the bee colony algorithm. Based on the mechanical transmission experiment platform of the MR clutch, the performance of the torque control of the MR clutch is experimentally studied by using the real-time operating system Phar Lap ETS built by LabVIEW real-time module, it is found that the fuzzy PID control strategy based on bee colony algorithm can not only improve the torque control accuracy of the MR clutch by 70% compared to traditional PID control strategies but also increase the response speed by more than 50%.

Multi-objective optimization of thermal power and outlet steam temperature for a nuclear steam supply system with deep reinforcement learning

The nuclear steam supply system (NSSS) is a critical component of a nuclear power plant that produces steam for electricity or cogeneration. However, the performance of current control strategies in the NSSS is limited since the coupling in the system is too complex and non-linear to be modeled precisely. Thus, there is an urgent need to study the model-free optimization method of NSSS. Motivated by this, this article proposes a novel multi-objective optimization approach based on deep reinforcement learning (DRL). With a hierarchical structure, the proposed method improves the response performances of both thermal power and outlet steam temperature by dynamically adjusting the reference values of existing proportional–integral–differential (PID) controllers within NSSS. This structure combines the stability of PID’s closed-loop control and the optimization capabilities of DRL for a safe and efficient operation. Moreover, a safe DRL method with an event-triggered mechanism is proposed to further ensure safety throughout the optimization process. The numerical simulations demonstrate the effectiveness and superiority of the proposed method, which results in significant improvements in the transient response compared to traditional PID controllers.

Incremental proportion integration differentiation control of all-terrain vehicle magnetorheological suspension system under low-frequency disturbances

In intelligent control systems of magnetorheological (MR) suspension of all-terrain vehicle (ATV), low-frequency disturbance (LFD) in the measured feedback signal (acceleration) makes the controller unable to calculate the theoretical control force accurately. In particular, when the frequency of the LFD signal is close to that of the actual acceleration signal, the LFD cannot be filtered by designing a traditional filter. Based on the above questions, this paper proposes an incremental proportion integration differentiation (IPID) strategy to address the issue of LFD in the measured feedback acceleration signal of the MR suspension system of ATV. First of all, the model of 1/4 vehicle suspension in consideration of LFD is established, the source of LFD is analyzed which is due to the transformation of Coriolis acceleration under the condition of vehicle body pitch and roll. Next, a semi-active IPID controller is designed by utilizing differential derivation of discrete PID and semi-active principle to eliminate the LFD component mixed in the acceleration signal by making a difference. The particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of the controller, which is then verified through numerical simulation. Subsequently, a real vehicle control experiment is carried out based on a 4 × 4 ATV equipped with the MR suspension system and implemented by DSP controller with the designed IPID algorithm. The effectiveness of the proposed method is evaluated under the speed 10 km h−1 and E road, and the designed method is compared with the traditional proportion integration differentiation (PID) control algorithm through simulation and experimentation to demonstrate the superiority and rationality of ‘filtering out’ LFD signal and improving control effectiveness.

Design of Observer-Based Discrete-Type PID Control for Reconstructed Jump Model of Interconnected Power System Under Hybrid Attacks

In this article, observed-based discrete proportion-integration-differentiation (PID) control for multi-area interconnected power systems is addressed under hybrid attacks. Firstly, a reconstructed jump model for interconnected power systems is proposed by taking the circumstance of data packet loss into consideration, which caused by the stochastic transmission delay of sampled data. Then, different from the traditional continuous-type PID control, an observer-based discrete-type PID control strategy is designed for discrete-time power system. Besides, due to the sampled data may be tampered or shield in open communication networks, the hybrid attacks, i.e., deception attacks and denial-of-service attacks, with random manners are considered. Furthermore, an input-to-state stable criterion is derived by constructing appropriate Lyapunov-Krasovskii functionals. Finally, the discrete-type PID controllers and observer are achieved, and the validity of proposed approach are also demonstrated in the illustrative examples section.

Design of Plc-based Pipe Pile Residual Slurry Recovery Equipment Control System

To solve the cost loss caused by the low recovery efficiency of pipe pile residual slurry, as well as the pollution of the environment during discharge, a control system of pipe pile residual slurry recovery equipment is designed. The equipment is controlled by PLC (Programmable Logic Controller), aiming at the problem that the traditional PID (Proportion Integration Differentiation) cannot modify the parameters in real time to meet the accuracy requirements of the residual slurry density. The fuzzy PID controller algorithm is proposed, and the simulation analysis is carried out by MATLAB at the same time. Through PLC selection, PLC I/O allocation, electronic control system design, program writing, and HMI (Human Machine Interface) establishment, the information interaction is achieved between equipment and people while monitoring the production process. Through on-site production tests, the equipment is proved to be reliable and efficient, which can make the residual slurry recovery utilization rate reach more than 90%, and reduce the production cost of pipe piles.

Improved active disturbance rejection controller for rotor system of magnetic levitation turbomachinery

<abstract> <p>The rotor of the magnetic suspension turbomachinery is supported by the magnetic suspension bearing without contact and mechanical friction, which directly drives the high-efficiency fluid impeller. It has the advantages of high efficiency, low noise, less fault and no lubrication. However, the system often has some unknown mutation, time variation, load perturbation and other un-certainties when working, and the traditional Proportion Integration Differentiation (PID) control strategy has great limitations to overcome the above disturbances. Therefore, this paper firstly establishes a mathematical model of the rotor of magnetic levitation turbomachinery. Then, a linear active disturbance rejection controller (LADRC) is presented, which can not only improve the above problems of PID control, but also avoid the complex parameter tuning process of traditional nonlinear active disturbance rejection control (ADRC). However, LADRC is easy to induce the overshoot of the system and cannot filter the given signal. On this basis, an improved LADRC with a fast-tracking differentiator (FTD) is proposed to arrange the transition process of input signals. The simulation results show that compared with the traditional PID controller and single LADRC, the improved linear active disturbance rejection control method with fast tracking differentiator (FTD-LADRC) can better suppress some unknown abrupt changes, time variation and other uncertainties of the electromagnetic bearing-rotor system. At the same time, the overshoot of the system is smaller, and the parameters are easy to be set, which is convenient for engineering application.</p> </abstract>

Research on sliding mode controller of the high-speed maglev train under aerodynamic load

The high-speed maglev train will be subjected to extremely obvious aerodynamic load and instantaneous aerodynamic impact during passing another train, which brings significant challenges to the train’s suspension stability and safe operation. It’s necessary to consider the influence of aerodynamic load and shock waves in the design of suspension control algorithms. Traditional proportion integration differentiation (PID) control cannot follow the change of vehicle parameters or external disturbance, which is easy to cause suspension fluctuation and instability. To improve the suspension stability and vibration suppression of the high-speed maglev train under aerodynamic load and impact, we design a siding mode controller introducing the primary suspension’s deformation to replace the aerodynamic load on the electromagnet. Furthermore, we establish the train’s dynamic simulation model with three vehicles and compare the designed controller and the PID controller for their performance in controlling the model suspension stability in the presence of the train operating in open air. Simulation results show that the sliding mode control (SMC) method proposed in this paper can effectively restrain the electromagnet fluctuation of the train under aerodynamic loads.

Research on PID Position Control of a Hydraulic Servo System Based on Kalman Genetic Optimization

With the wide application of hydraulic servo technology in control systems, the requirement of hydraulic servo position control performance is greater and greater. In order to solve the problems of slow response, poor precision, and weak anti-interference ability in hydraulic servo position controls, a Kalman genetic optimization PID controller is designed. Firstly, aiming at the nonlinear problems such as internal leakage and oil compressibility in the hydraulic servo system, the mathematical model of the hydraulic servo system is established. By analyzing the working characteristics of the servo valve and hydraulic cylinder in the hydraulic servo system, the parameters in the mathematical model are determined. Secondly, a genetic algorithm is used to search the optimal proportional integral differential (PID) controller gain of the hydraulic servo system to realize the accurate control of valve-controlled hydraulic cylinder displacement in the hydraulic servo system. Under the positioning benchmark of step signal and sine wave signal, the PID algorithm and the genetic optimized PID algorithm are compared in the system simulation model established by Simulink. Finally, to solve the amplitude fluctuations caused by the GA optimized PID and reduce the influence of external disturbances, a Kalman filtering algorithm is added to the hydraulic servo system to reduce the amplitude fluctuations and the influence of external disturbances on the system. The simulation results show that the designed Kalman genetic optimization PID controller can be better applied to the position control of the hydraulic servo system. Compared with the traditional PID control algorithm, the PID algorithm optimized by genetic algorithm improves the system’s response speed and control accuracy; the Kalman filter is a good solution for the amplitude fluctuations caused by GA-optimized PID that reduces the influence of external disturbances on the hydraulic servo system.

Optimization and Realization of the Coordination Control Strategy for Extended Range Electric Vehicle

This paper designed a fuzzy adaptive proportional integral differential (PID) control algorithm to optimize the overshoot of speed and torque, fuel consumption and exhaust emissions of the traditional PID control strategy in the process of working condition switching of an extended range electric vehicle. The simulation was carried out in Matlab/Simulink, and the optimization of the control strategy was verified by a bench test. The results show that the fuzzy adaptive PID control strategy effectively reduced the speed overshoot in the process of working condition switching compared with the traditional PID control strategy. The bench test proved that the fuzzy adaptive PID control strategy could effectively optimize the switching process, especially in the speed and torque reduction switching process, and the speed overshoot rate of the fuzzy PID control was greatly reduced to 0.7%, far less than that of the traditional PID control with 6.6%, while the torque overshoot rate was within 0.8%. Additionally, the fuzzy adaptive PID control could effectively reduce the fuel consumption, especially in the switching process of increasing the speed and torque, where the fuel consumption of the fuzzy adaptive PID control was 2.1% and 0.5% lower than that of the traditional PID control, respectively. Additionally, the fuzzy adaptive PID control could also reduce the particulate emissions, especially in the process of increasing the speed and torque, where the number of particles of the fuzzy PID control was 11% and 19% less than that of the traditional PID control, respectively. However, the NOx emissions based on the fuzzy PID control were slightly higher than those of the traditional PID control due to the smooth operation and improved combustion.

Control System of Liquid Fertilizer Variable-Rate Fertilization Based on Beetle Antennae Search Algorithm

In order to solve the problems of low precision of variable-rate fertilization and uneven fertilization flow of field liquid fertilizer applicator, a control system of variable-rate fertilization of liquid fertilizer based on beetle antennae search algorithm was proposed. First of all, this study established a mathematical model for the variable-rate fertilization control system of liquid fertilizer. Then, according to the control requirements, the search algorithm is used to optimize the three parameters of Proportion Integration Differentiation (PID). Finally, the response time and overshoot of the system are analyzed by software simulation, and the PID control based on beetle antennae search algorithm is compared and analyzed with fuzzy PID control and traditional PID control. The control effect of the control system is verified by a bench test. The results show that the actual response time of the variable-rate fertilization control system based on the beetle antennae search algorithm can reach 2 s, and the average relative error can reach 1.27%. Therefore, the control system of this study can achieve a better control effect, and the control method of this study provides a feasible scheme for the study of variable-rate fertilization.

Temperature and humidity management of PEM fuel cell power system using multi-input and multi-output fuzzy method

• A PEMFC temperature and humidity transient synergy control strategy is proposed. • This method can improve the stack dynamic response capability and power density. • The control performance of this method is verified by experiments. Dynamic hydrothermal management is the critical issue for improving the performance of Proton exchange membrane fuel cell (PEMFC). In this study, a multi-input and multi-output (MIMO) fuzzy control method is proposed for the hydrothermal management of PEMFC in real time. For PEMFC, stack temperature and relative humidity at cathode are chosen as the control objectives, and the cooling water flow rate and inlet gas humidity at cathode are set as the control variables. To establish the transient synergy control of temperature and humidity, a fuzzy logic controller is constructed. The input parameters of controller include the errors of temperature, humidity and their derivatives. The dynamic performances of temperature and humidity transient synergy control under the disturbance of load current and operating parameters are analyzed. The results show that the temperature and humidity transient synergy control based on MIMO fuzzy method presents faster response speed and better control performance than traditional Proportion Integration Differentiation (PID) control. The output voltage of the proposed control method is 3.14% and 4.21% higher than that of traditional PID controllers. Furthermore, the power density of THTSc is 9.37% higher than that of PID controller.

Application of Particle Swarm Optimization Algorithm in Photovoltaic Constant Pressure Water Supply Electrical Control System

The traditional constant water supply system has problems, low operation efficiency, and the strong nonlinear and large time delay of the control system. It did not meet the needs of water supply in remote and desert areas. To solve these problems, the PV constant voltage water supply system is designed to optimize the Proportion Integration Differentiation (PID) controller method based on the particle swarm optimization (PSO) algorithm. The programmable logic controller (PLC) and frequency converter are used as the core control mechanism of the system, the water pump motor as an actuator, the pressure sensor and the level sensor be used as a feedback mechanism, Photovoltaic power generation equipment is used in power supply agencies. In this paper, the objective utilizes the PSO algorithm to optimize the parameters of the PID controller, and the principle and control process of the PSO-PID algorithm are analyzed. Simulation results by MATLAB verify that the PSO-PID algorithm has better dynamic and steady-state performance than the traditional PID algorithm. Finally, the practicability and effectiveness of the water supply system are proved by the PLC of the OMRON-CP1H series and the touch screen of the WEINVIEW series.

Research on Adaptive Control of Air-Borne Bolting Rigs Based on Genetic Algorithm Optimization

Rotation speed and propulsive force are the two critical parameters in the work of the air-borne bolting rig. To address the problem that unreasonable rotation speed and propulsive force will induce the breakage of the drill pipe and the inability of the drill bit to cut coal adequately this paper proposes an adaptive control strategy for the air-borne bolting rig based on genetic algorithm optimization. Firstly, we obtain the corresponding coal hardness by the real-time acquisition of the working torque of the drill pipe. Then we calculate the reasonable rotation speed of the hydraulic motor and the propulsive force of the hydraulic cylinder on the coal of different hardness. Secondly, the genetic algorithm is applied to optimize the parameters of the PID (proportion integration differentiation) controller so that the system may attain the target value fast and reliably and achieve adaptive control. Finally, a simulation model of the slewing system and the propulsion system of the air-borne bolting rig are established in the AMESim hydraulic software, and the simulation tests were carried out under two distinct working conditions: single coal hardness and coal hardness of sudden change. The results indicate that the PID control strategy based on genetic algorithm optimization has a shorter response time, a smaller overshoot, and a lower steady-state error than the traditional PID control strategy.

AMT Starting Control as a Soft Starter for Belt Conveyors Using a Data-Driven Method

Automated mechanical transmission (AMT) is used as a soft starter in this paper. To improve the soft starting quality, a novel data-driven method is studied. By analyzing and comparing five common soft-starting acceleration curves, a segmented acceleration curve is put forward to be used as the soft-starting acceleration curve for the AMT. Based on the prototype model free adaptive control (MFAC) method, a modified MFAC method with jerk compensation is given to control the AMT output shaft’s angular acceleration and reduce driveline shock. Compared with the methods of prototype MFAC and traditional proportion integration differentiation (PID), the modified MFAC method with jerk compensation can better control the AMT output shaft’s angular acceleration and has excellent characteristics in terms of small tracking error and smaller shock. The research results provide a novel data-driven method for AMT as a soft starter.