PID Parameters Research Articles

Research on PID Controller of Excavator Electro-Hydraulic System Based on Improved Differential Evolution

An electrical hydraulic control system (electro-hydraulic system) is thought to be a key component in excavator operation systems. Control methods with fixed parameters may not yield optimal system performances because a hydraulic system has various nonlinear uncertainties due to the leakage and compressibility of the fluid medium. Hence, a novel PID controller based on improved differential evolution (IDE) is introduced to excavator electro-hydraulic systems for interconnected hydraulic systems. The proposed algorithm not only adjusts the PID parameters of the different working conditions but also adjusts the scaling factor and crossover probability. Then, the proposed PID controller based on IDE and the excavator bucket control system are modeled and simulated on the MATLAB simulation platform. The simulation results demonstrate that the proposed controller has better performance in settling time, rise time, and convergence speed compared to the PID controller based on standard differential evolution and the Ziegler–Nichols (ZN) PID controller with a novel object function. Eventually, the IDE-PID controller is assessed on a 23-ton excavator, and good transient behavior and trajectory accuracy are obtained in comparison to the SDE-PID controller.

Mercury fractionation - Problems in method application

Mercury (Hg) is a global pollutant with a negative effect on human and ecosystem health. Mercury is toxic in all forms. The toxicity, however, varies depending on the form of mercury, determining its physical and chemical properties. Therefore, knowledge on the chemical speciation of mercury is key for the understanding of its transport and transformations in the environment. Analysis of mercury speciation, however, is time-consuming and involves high risk of contamination. The mercury thermodesorption method offers many new possibilities. The main advantages of this method are identifying which groups of compounds are being transformed in the atmosphere, sediment and soil, suspended matter and plankton, and in organisms from different trophic levels. A great advantage of the method is also its application in mercury analyzers, where it is possible to control the heating and cooling temperatures of. The standardisation of fractionation nomenclature for all matrices (both biotic and abiotic) will be helpful in application of this mercury fractionation method too. It has also disadvantages, mostly in the technical preparation of the analyzer. The analyzer must be prepared for fractionation: setting the ventilator and adjusting the PID parameters so that the pre-set heating (t1) and combustion (t2) times reach the set value in the method program. Also, any modification of the heater forces a re-optimisation of the method with mercury standards, as certified reference materials for Hg fractionation in environmental matrices are not available. The HgF2 fraction cannot be used as the methylmercury concentration, which is undoubtedly the biggest drawback of this method.

Real-time trajectory position error compensation technology of industrial robot

In order to meet the requirements of high positioning accuracy and trajectory accuracy of industrial robots in the field of high-precision operation, this paper studied the real-time compensation method of the robot trajectory error used laser tracker to measure the end position of robot in real time. This real-time error calculation method of robot trajectory based on continuous dynamic time warping (CDTW), which was studied to determine the position error of robot trajectory at each moment. Moreover, this paper researched the PID parameter tuning technology of response surface model method based on gray analysis and got a real-time compensation method. The compensation value was fed back to the robot for motion compensation in real time through KUKA RSI. The last, the influence of compensation parameters and period was analyzed and the error compensation experiment of positioning, linear trajectory and arc trajectory were carried out. After compensation, the absolute positioning error of the robot was less than 0.06 mm and the trajectory position error was less than 0.15 mm, which greatly improved the position accuracy of the robot.

Finite Element Simulation of Proportional, Integral, and Derivative-Controlled Bipolar Radiofrequency Ablation of Porcine Spinal Muscle

Abstract Radio frequency ablation (RFA) of the medial branch nerve is a widely used therapeutic intervention for back pain originating from the facet joint. However, multifidus denervation is a well-known adverse effect of this RFA procedure. Computational simulations of RFA can be used to design a new multifidus-sparing RFA procedure for facet joint pain. Unfortunately, there is not a computational model available for RFA of porcine spines (a common animal model for the translation of spinal treatments). The objective of this study is to develop and verify a computational model for bipolar radio frequency ablation of porcine spine muscle. To do this, the electrical and thermal conductivity properties were measured over a temperature range of 20–90 °C in ex vivo porcine spinal. A proportional, integral, and derivative (PID) controlled finite element (FE) model was developed and tuned to simulate the ablation process. Finally, tissue temperatures from simulations and experimental ablations were compared. Thermal conductivity values of spinal muscle ranged from 0.33 W/mK to 0.57 W/mK. Similarly, electrical conductivity varied from 0.36 S/m to 1.28 S/m. The tuned PID parameters for temperature-controlled model were KP=40, Ki=0.01, and Kd=0. A close agreement between experimental measurements of tissue temperature and simulations were observed in the uncertainty range with R-squared values between 0.88 and 0.98. The model developed in this study is a valuable tool for preclinical studies exploring new RFA methods of spinal nerves.

Design of Cross‐Coupled Nonlinear PID Controller Using Single‐Objective Evolutionary Algorithms

The effectiveness of evolutionary algorithms (EAs) such as differential search algorithm (DSA), Real‐Coded genetic algorithm with simulated binary crossover (RGA‐SBX), particle swarm optimization (PSO), and chaotic gravitational search algorithm (CGSA) on the optimal design of cross‐coupled nonlinear PID controllers is compared in this paper. A cross‐coupled multivariable PID controller structure for the binary distillation column was developed with two inputs and two outputs. EA simulations are run to lower IAE using two stopping criteria, namely, maximum number of functional evaluations (Fevalmax) and Fevalmax plus PID parameter and IAE tolerance. Over 20 separate trials were used to compare the performances of several EAs using statistical measures such as best, mean, standard deviation of outcomes, and average calculation time. This article presents the design of a cross‐coupled nonlinear PID controller using single‐objective evolutionary algorithms. Using evolutionary algorithms (EAs) with a multicrossover strategy, the results achieved by various EAs are compared to previously reported results. The results of a multivariable cross‐coupled system clearly show that a single‐objective nonlinear PID controller performs better. Simulations further show that all four techniques evaluated are suitable for PID controller tweaking off‐line. However, only the single‐objective evolutionary algorithms are acceptable for online PID tuning due to their higher consistency and shorter computation time.

PID Controller for PMSM Speed Control Based on Improved Quantum Genetic Algorithm Optimization

When traditional proportional integral and differential controllers are applied to speed control in permanent magnet synchronous motors(PMSM), their coefficients are basically determined based on experience, which inevitably leads to unsatisfactory results when using this parameter to control the speed stability of permanent magnet synchronous motors. Therefore, this paper proposes an improved quantum genetic algorithm using quantum states as the basic unit. Utilizing quantum properties for global optimization to optimize the coefficients of proportional integral and differential control, improving the rotation angle of quantum state particles through the idea of velocity changes in particle swarm optimization(PSO), introducing adaptive weight changes, using Hadamard gates to replace traditional algorithm mutation strategies, and incorporating disaster mechanisms. In addition, this paper uses four test functions to find the minimum value, thereby verifying that our algorithm has better performance in optimization iteration compared to other algorithms, providing the initial basis for the next step of application in PID parameter optimization. Prove that this method can solve the problem of traditional genetic algorithms falling into local optima due to improper selection, crossover, and mutation methods, which cannot effectively control the stability of motor speed. Finally, this paper uses Matlab2018a simulation to compare with the other four algorithms, and the results show that this algorithm can find better PID parameter values to achieve better results in motor oscillation, overshoot, and faster target speed.

Research and implementation of variable-domain fuzzy PID intelligent control method based on Q-Learning for self-driving in complex scenarios.

In the control of the self-driving vehicles, PID controllers are widely used due to their simple structure and good stability. However, in complex self-driving scenarios such as curvature curves, car following, overtaking, etc., it is necessary to ensure the stable control accuracy of the vehicles. Some researchers used fuzzy PID to dynamically change the parameters of PID to ensure that the vehicle control remains in a stable state. It is difficult to ensure the control effect of the fuzzy controller when the size of the domain is not selected properly. This paper designs a variable-domain fuzzy PID intelligent control method based on Q-Learning to make the system robust and adaptable, which is dynamically changed the size of the domain to further ensure the control effect of the vehicle. The variable-domain fuzzy PID algorithm based on Q-Learning takes the error and the error rate of change as input and uses the Q-Learning method to learn the scaling factor online so as to achieve online PID parameters adjustment. The proposed method is verified on the Panosim simulation platform.The experiment shows that the accuracy is improved by 15% compared with the traditional fuzzy PID, which reflects the effectiveness of the algorithm.

Automatic control method of automobile steering-by-wire based on fuzzy PID

Automatic driving is gradually being popularised, and automatic control of automobile steering by wire has also become a topic worthy of research. The research combines the fuzzy control algorithm and PID to construct a fuzzy PID control strategy, and integrates the genetic algorithm into the basic fuzzy rules to form an improved new algorithm. The algorithm mainly focuses on multiple changes in the steering-by-wire of the car, including angular momentum, angular velocity, linear velocity and linear acceleration, to build more complex fuzzy rules and then use the three most basic parameters of PID. The results show that the average accuracy rate, average recall rate and average precision rate of the improved algorithm are 91.27%, 73.67% and 80.94%, which are significantly higher than the other two algorithms for comparison, indicating that the improved algorithm has better performance.

Development of DCS SCADA Teaching Module on A Pid-Based Water Level Control Case Using Labview and Factory I/O

The DCS SCADA course is one of the latest courses for the 2022 curriculum at Politeknik ATI Makassar. This course has case study-based learning from the industrial world and involves very expensive instrumentation devices for Real Plant, so a Teaching Module with factory i/o was created. This research aims to develop a DCS SCADA Teaching Module based on the Water Level Control case using Labview and Factory I/O. The virtual plant uses a water tank with instruments consisting of two control valves, namely the fill valve and the discharge valve, and two transducers, namely the level meter and the flow meter. To connect the factory I/O to NI Labview, the open protocol Modbus Ethernet is used. Based on the testing results, the calibration for the level measurement has an accuracy of 99.59% and a precision of 0.69% with the interpolation y = 0.301x -0.536 for a 100% correlation. For the system response with SP = 150 without disturbance, the PID parameters should be set to Kp = 25, Ti = 0.1, and Td = 0 with a system response consisting of Rise Time = 396 seconds, no Overshoot, Settling Time = 430 seconds, and Steady state error = 0.566 cm. For the system response with SP = 150 with a DV = 50% disturbance, the PID parameters should be set to Kp = 25, Ti = 0.1, and Td = 0 with a system response consisting of Rise Time = 382 seconds, no Overshoot, Settling Time = 462 seconds, and Steady state error = 1.469 cm.

Deaerator system pressure and liquid level PID regulation control strategy

Excessive oxygen content in the feed water of industrial boilers will cause oxidative corrosion on the metal surface, resulting in grooves or even thinning of the metal tube wall, resulting in trachoma until it is completely damaged. Therefore, the quality of the deoxidation effect greatly impacts the service life of industrial boilers and directly affects the economic benefits of enterprises. Automatic equipment and PID parameter adjustment are used to improve the deoxygenation performance of the deaerator system, making the oxygen content of the water in the industry meet the standard, thereby reducing the oxidation and corrosion of pipelines and prolonging the practical life of the equipment. This article mainly introduces Shanxi Yaxin Energy Group Co., Ltd. and Xinneng Technology Co., Ltd. 's 220-ton CDQ coke oven with co-production 120T SS98 high-temperature and high-pressure steam boiler, supporting a set of XNC-130 deaerator produced by Lianyungang Huagang Power Equipment Co., Ltd., the design output is 130t/h, the working pressure is 0.03MPa, the working temperature is 104℃, the design pressure is 0.3MPa, the design temperature is 120℃, the water tank volume is 50m³, and the material Q345R. The system uses Siemens PLC, Rosemount pressure transmitter, regulating valve and other equipment combined with PID control algorithm to achieve pressure and liquid level adjustment and the best deaeration effect.

Research on Engine Speed Control Based on Tuna Swarm Optimization

Accurate control of engine speed can effectively improve fuel economy and comfort. Currently, the commonly used PID parameter setting methods for engine speed control include Ziegler-Nichols method and gradient method, etc. Although they have good performance in parameter setting, they still have shortcomings such as slow response to the control process and long stability time. In this paper, the tuna swarm optimization is used to adjust the PID parameters, and the optimized results are compared with the traditional setting results. The experimental results show that under the same test conditions, the response speed can be reduced by 3.2-6.2s, the maximum overshoot can be reduced by 0.5%-5%, and the maximum steady-state error can be reduced by 0.6%-0.8%.The tuna swarm optimization has an obvious effect in PID parameter optimization of engine speed control, which provides a theoretical basis for the application of other group optimization algorithms in PID parameter optimization of engine speed control.

New energy vehicle engine speed control method based on vehicle networking technology

In view of the current new energy vehicles only implement torque control on the drive motor, resulting in the engine speed fluctuation, affecting the vehicle running stability and reducing the engine efficiency and energy saving effect, a new energy vehicle engine speed control method based on Internet of vehicles technology is proposed. The battery and operation data are obtained through the sensor of the new energy vehicle Internet of vehicles, and the data are controlled and transmitted by the controller area network. The overall external dynamic characteristics of the engine are analyzed by using the average value model. According to the principle of engine dynamic system, the throttle intake manifold model, combustion torque model and power output sub model are constructed respectively. The speed is controlled by double working points According to the variable parameter PID control of the engine, the load prediction is introduced to optimize the speed control, and the internal pressure value of the accumulator is obtained through the SOP value to realize the speed control of the new energy vehicle engine. The experimental results show that the engine speed control method can effectively improve the energy-saving effect and efficiency of the engine, reduce the engine speed fluctuation, and improve the vehicle running stability under the condition of meeting the driver’s torque demand.

Research on PID Parameter Tuning and Optimization Based on SAC-Auto for USV Path Following

Unmanned surface vessels (USVs) are required to follow a path during a task. This is essential for the USV, especially when following a curvilinear path or considering the interference of waves, and this work has been proven to be complicated. In this paper, a PID parameter tuning and optimizing method based on deep reinforcement learning were proposed to control the USV heading. Firstly, the Abkowite dynamics model with three degrees of freedom (DOF) is established. Secondly, the guidance law on the line-of-sight (LOS) method and the USV heading control system of the PID controller are designed. To satisfy the time-varying demand of PID parameters for guiding control, especially when the USV moves in waves, the soft actor–critic auto (SAC-auto) method is presented to adjust the PID parameters automatically. Thirdly, the state, action, and reward functions of the agent are designed for training and learning. Finally, numerical simulations are performed, and the results validated the feasibility and validity of the feasibility and effectiveness of the proposed method.

Parameter Optimization of PID Controller Based on Improved Beetle Herd Algorithm

The PID controller, although used in 95% of the loop operations in the automation field for practical control, still has a lot of shortcomings such as nonlinearity, hysteresis, and time-varying uncertainty, resulting in unsatisfactory control effects. To solve this problem, this paper proposes an improved beetle herd algorithm for PID parameter optimization. With the combination of the group collaborative optimization ability of particle swarm optimization, the global search and local search capability is strengthened through the test of the error-index ITAE and the optimized PID parameters. Finally, with the help of the PID controller simulation, the effectiveness of the algorithm is verified by comparing the effects of the four algorithms before and after the improvement.

Optimal Quadratic Regulator PID for Motor DC

There are various methods for optimizing the parameters of PID, such as Genetic Algorithm, Flower Pollination Algorithm, and many more. Those metaheuristics methods can be used on various applications. Nevertheless, the computational costs are quite expensive. In this paper, we design a PID controller based on the optimal regulator for a DC motor. It provides such an optimal solution that does not require highly computational resources. We simulate the responses of the closed-loop system using MATLAB. The results verify that the optimal regulated (PID-QR) has better response than Ziegler-Nichols based PID. It has no overshoot and smaller error tracking than the controller based on Ziegler-Nichols.

Design of Robust PID Controller Using PSO-Based Automated QFT for Nonminimum Phase Boost Converter

This brief reports a novel method of synthesizing a robust PID controller for regulating boost DC-DC converter that exhibits non-minimum phase (NMP) dynamics and complex-conjugate poles in its linear model. The NMP dynamics restrict the achievable closed-loop bandwidth and make tuning of PID parameters a difficult task. To solve this problem, the proposed design method uses the Quantitative Feedback Theory (QFT) in conjunction with particle swarm optimization (PSO) to carry out automatic loop-shaping. Due to the global optimization capability of PSO, the proposed multi-objective function approach with PID simplifies the loop-shaping to compensate the converter having complex-conjugate zeros. An attractive feature of this design method is that it accounts for the worst-case scenario due to parametric variations in the converter model. The real-time simulation results and the experiments reveal the efficacy of the proposed method.

HVAC multivariable system modelling and control

Heating, Ventilation and Air Conditioning (HVAC) is a multivariable process where any alteration with one system input affects most or all of the system’s outputs simultaneously. Owing to its comprehensiveness, a readily derived multivariable HVAC mathematical model is selected for this work, mainly a hybrid distributed-lumped parameters model. As the transfer function matrix was not established in the selected HVAC model, it was exclusively developed in this study, using the time domain graphical responses of the chosen model. Based on the developed transfer function matrix, a conceptual two-step approach was followed to control HVAC model performance. The first was decoupling the interactions that affect all the system outputs, and the second was designing proper PID controllers for each decoupled loop similar to those used for single input single output (SISO) systems. A direct Nyquist Array (DNA) multivariable control strategy was used for this purpose and successfully decoupled the HVAC system into three separate (SISO) loops. Three PID controllers afterwards were applied for each decoupled loop. The results showed quite decoupled system outputs with a minor coupling percentage so that any change in a system input only affected the corresponding system output. The output responses are also underdamped with almost zero steady-state error confirming the effectiveness of the selected PID parameters. The values of steady-state responses are obtained in (10–15) s compared with (200–600) s of open-loop response time. However, various overshoot percentages in the responses are encountered but are relatively small, with a short settling time, so they don’t affect the thermal comfort of the ventilated volume. System stability using the Nyquist criterion has also been examined and found to satisfy the criterion. The multivariable DNA control technique and the SISO closed-loop PID controllers have shown the capability to suppress external disturbances and restore the system to its original functional steady-state values.

Simulation and Validation of Optimized PID Controller in AGV (Automated Guided Vehicles) Model Using PSO and BAS Algorithms.

Automated guided vehicles (AGVs) are popular subsets of robots that come in various shapes and sizes. The group's use in the industry ranges from applications for carrying pallets, carts, and utensils to helping the elderly or transporting medicine to hospitals. Even recently, they have been used in libraries to carry books on shelves. The main part of an AGV includes its body, motor, driver, processor, and sensors, which are more or less the same in all types of AGVs, and addons vary depending on the application and the work environment. The part that affects AGV performance is the control strategy, to which researchers have shown different approaches. Using various techniques and simulations to obtain a model is the key and can help to improve and evaluate the performance of the strategy of the robot. In this study, based on the actual design of the AGV system, all data and components are described, and the simulation is performed in MATLAB software. Then, for controlling the platform based on the PID controller tuning, four methods of Ziegler Nichols, empirical, Particle Swarm Optimization (PSO), and Beetle Antennae Searching (BAS) (optimizer) are discussed, and the results are compared in the four paths including the circle, ellipse, Spiral and 8-shaped paths by observing and testing the tuned PID parameters. Finally, a series of subsequent experiences were carried out in CoppeliaSim (VREP) as a famous robot simulator to overcome the environmental constraints for the same paths that were used in Matlab based on the extracted PID values. Based on the results, the empirical methods, PSO, and BAS errors are very close together. But in general, the BAS algorithm is the fastest, and the PSO had better performance. In general, the maximum error is linked to the path of 8 shapes and the minimum is related to circle shape one. Finally, the analysis of results in different paths in both simulators shows the same results. Therefore, concerning the limited test on the real platform and using the PID coefficients obtained from the simulation shows the model's ability for the researchers in robotic research.

Simulation-Based Comparison of PID with Sliding Mode Controller for Matrix-Converter-Based Dynamic Voltage Restorer under Variation of System Parameters to Alleviate the Voltage Sag in Distribution System

A constant power supply is a basic need for each consumer due to the increase in sensitive equipment day by day. As per IEEE standards, a 10% reduction in voltage from the supply voltage is not acceptable and may cause the failure of equipment. Previously, different techniques have been used to alleviate the voltage sag, such as STATCOM, DSTATCOM, SVC, and shunt capacitors, but these devices are connected in parallel, which compensates for the low value of voltage sag, and they have high maintenance costs involved. Compensation for the low and high values of voltage sag is possible through a series-connected device such as a dynamic voltage restorer. In this paper, a matrix converter is presented for DVR to convert AC to AC voltage directly and free from batteries, capacitors, and multiple conversions as needed in a voltage source inverter, resulting in a reduced cost of DVR topology. The DVR is meaningless in the absence of a controller, so it is necessary to select a suitable controller for the satisfactory operation of the DVR under a variation of system parameters. In this paper, the performance of a linear PID controller is analyzed and compared with a nonlinear controller, such as a sliding-mode controller, under variation of power system parameters inorder to select a robust controller that performs satisfactorily for DVR. Earlier trial-and-error methods were used to obtain the parameters of PID gains, but they require a large time to obtain the parameters of the PID gains and there is a chance of inaccuracy. A genetic algorithm was used to obtain the gain parameters, but it has more convergence time and the particle swarm optimization technique has involves less reliability. In this research paper, the sliding surface coefficient parameters such as and Ki for the PI sliding surface of SMC and PID gains are taken through an ant colony algorithm to obtain the robustness of the controllers. The purpose of this paper is to introduce the best DVR topology with reduced cost. MATLAB simulation software was utilized to analyze the performance of the DVR with PID and SMC controllers under different fault conditions and also the THD% of proposed controllers was analyzed through FFT.

Dynamic pressure-based rocket PID parameters adaptive adjustment method and application

This paper establishes a dynamic pressure-based PID parameter adjustment method, which uses the function of the ratio of the maximum dynamic pressure of the rocket to the actual dynamic pressure of the current flight to correct the rocket control parameters. Compared with the traditional time-based PID parameter gain scheduling method, the number of PID parameters to be adjusted is significantly reduced from nearly 50 to 10, and the adjustment complexity is significantly reduced. Based on the method in this paper, the PID parameter debugging of the attitude control of a model-guided rocket is completed. The six-degree-of-freedom ballistic simulation results show that the control law obtained by the method in this paper meets the requirements of the attitude control of the model-guided rocket, and is superior to the traditional time-based PID parameter gain adjustment method in terms of response rate.