Proportional Integral Derivative Control Algorithm Research Articles

Temperature Control of Quartz-Glass Melting Areas in Laser Additive Manufacturing.

Direct energy deposition is an additive technology that can quickly manufacture irregularly shaped quartz-glass devices. Based on this technology and coaxial laser/wire feeding, open-loop tests were conducted under different process parameters. A closed-loop temperature control system was designed and built for the molten pool temperature in quartz-glass additive manufacturing. It was based on a PID (proportional-integral-derivative) control algorithm for adjusting laser power. Changes in the macroscopic morphology, microstructure, and other qualities of the final additive result before and after the temperature control of the quartz glass were examined. Relative to constant laser powers of 120 W and 140 W, the temperature control of the multi-pass single-layer lateral additives produced dense surface microstructures of the additively produced quartz glass, and the molding quality was better.

Fast and Intelligent Proportional–Integral–Derivative (PID) Attitude Control of Quadrotor and Dual-Rotor Coaxial Unmanned Aerial Vehicles (UAVs) Based on All-True Composite Motion

The construction of a six-degree-of-freedom (6-DOF) model for the composite motion of the actual mechanical structure (defined as an all-true composite motion model) of unmanned aerial vehicles (UAVs) is a prerequisite for achieving stable control of rotorcraft UAVs. Therefore, this paper proposes a construction approach for a nonlinear 6-DOF model of quadrotor and dual-rotor coaxial UAVs based on all-true composite motion. Two types of attitude–altitude control systems for rotorcraft based on a self-optimizing intelligent proportional–integral–derivative (PID) control method are constructed. Three-dimensional geometric models of the two rotorcraft types, incorporating their physical characteristics, are built. The attitude responses to different pulse width modulation (PWM) inputs are tested, thereby verifying the accuracy of the all-true composite model and analyzing the stability of the two types of UAVs. Furthermore, two types of attitude–altitude control inner loop controllers are designed, and the intelligent PID control algorithm is used to optimize the control parameters. Further verification of the robustness of the optimized parameters is carried out, and the designed attitude controllers are verified via experiment using a turntable. The simulation and experimental results show that the proposed all-true composite motion model and controller design method can accurately simulate the dynamic characteristics of the two types of UAVs and maintain stable attitude control, thus providing a valuable reference for the accurate attitude control of rotorcraft UAVs based on all-true composite motion.

A BP-Neural-Network-Based PID Control Algorithm of Shipborne Stewart Platform for Wave Compensation

In order to carry out offshore operations smoothly in severe sea conditions, a shipborne Stewart platform for wave compensation is required. Due to the random characteristics of waves, traditional control algorithms cannot accurately compensate for the motion caused by a wave. For the electric shipborne Stewart platform, this paper proposes a backpropagation (BP)-neural-network-based proportional–integral–derivative (PID) control algorithm where the PID parameters are adaptively adjusted by a BP neural network. The control algorithm can improve the robustness and wave compensation precision of the wave compensation system. First, a numerical system model of the shipborne Stewart platform was established according to the classical kinematic model and dynamic model. Then, the BP-PID control algorithm was designed based on the joint space control. In order to reduce the network’s sensitivity to local details and quickly find the global minimum, the gradient descent method with the momentum term is used in the neural network. At last, the availability and rationality of the new method were substantiated through a simulation comparison under various sea conditions. The simulation results indicate that the proposed control method achieves a higher compensation accuracy in three directions under various sea states, compared with traditional PID control algorithm. Under the irregular wave disturbance, the new control method can reduce the position deviation by about 6.56 times compared with a traditional PID control algorithm. The new control algorithm will play an active role in the control of the shipborne Stewart platform.

PID control algorithm based on multistrategy enhanced dung beetle optimizer and back propagation neural network for DC motor control.

Traditional Proportional-Integral-Derivative (PID) control systems often encounter challenges related to nonlinearity and time-variability. Original dung beetle optimizer (DBO) offers fast convergence and strong local exploitation capabilities. However, they are limited by poor exploration capabilities, imbalance between exploration and exploitation phases, and insufficient precision in global search. This paper proposes a novel adaptive PID control algorithm based on enhanced dung beetle optimizer (EDBO) and back propagation neural network (BPNN). Firstly, the diversity of exploration is increased by incorporating a merit-oriented mechanism into the rolling behavior. Then, a sine learning factor is introduced to balance the global exploration and local exploitation capabilities. Additionally, a dynamic spiral search strategy and adaptive [Formula: see text]-distribution disturbance are presented to enhance search precision and global search capability. The BPNN is employed to fine-tune both PID and network parameters, leveraging its powerful generalization and learning ability to model nonlinear system dynamics. In the simplified motor experiments, the proposed controller achieved the lowest overshoot (0.5%) and the shortest response time (0.012s), with a settling time of 0.02s and a steady-state error of just 0.0010. In another set of experiments, the proposed controller recorded an overshoot and response time of 0.7% and 0.0010s, across five DC motor tests. These results demonstrate the proposed adaptive PID control algorithm has superior performance in optimizing control system parameters, as well as improving system robustness and stability.

DDPG‐based heliostats cluster control of solar tower power plant

AbstractThe control of heliostat is crucial for the development of solar tower power plant. Currently, most power plants use open‐loop control, which has low cost but low efficiency, closed‐loop control has high concentrating efficiency, but each heliostat requires sensors and has high cost, and Proportional‐Integral‐Derivative (PID) controller has good control effect, but the parameter adjustment is difficult and overshooting problem occurs. In this paper, we propose a DDPG‐based heliostat cluster control aimed at improving the heliostat control effect and reducing the control cost. A leader‐follower strategy is used to control the heliostat, where the whole heliostat field is divided into several groups, each group is assigned a leader heliostat, and the rest of the heliostats follow the leader heliostat to rotate. The leader acquires the control error by means of a photoelectric sensor or a camera device. The following heliographs rotate with the leader to obtain the control signal, so there is no need for sensors, which reduces the number of sensors and lowers the cost. To address the shortcomings of traditional PID, we propose a DDPG‐based PID control algorithm. The algorithm is trained to find out the optimal value at each moment, which ensures that the controller parameters are optimal at each moment. The results show that the tracking error is below 0.0001 rad for both cluster control and individual control. This ensures effective tracking performance while reducing the sensor cost. The controller based on the DDPG algorithm eliminates overshoots, reduces errors, and shortens the stabilization time by 0.5 seconds.

Research on constant tension yarn transfer control technology based on AGA-PID and FOC algorithm

To solve the problems of poor real-time tension tracking, large overshoot, long adjustment time, and large tension fluctuation error that exist in the current control system for yarn feeding in the circular weft knitting machine, this paper presents an optimized and improved adaptive genetic algorithm (AGA) for adjusting the PID (Proportional Integral Derivative) parameters of the tension control part of the yarn-conveying system (AGA-PID). The algorithm is combined with the field-oriented control technology of the permanent magnet synchronous motor to design a closed-loop control strategy for the yarn-conveying system of a circular weft knitting machine. The MATLAB-Simulink simulation model was used to analyze the external tension loop-based AGA-PID controller and permanent magnet synchronous motor dual closed-loop control strategy of the yarn transport control system. The experimental verification was conducted by constructing an experimental platform that simulates the real process of yarn transportation. The outcomes of system simulations and experiments have consistently demonstrated that the AGA-PID control algorithm outperforms the GA-PID and PID control algorithms in terms of overshooting, response speed, stability, and anti-jamming ability. The yarn transport control system, which is designed to operate based on an AGA-PID control algorithm, is capable of reducing the fluctuations in tension that occur during the transportation of yarn and exhibits enhanced control accuracy, effectively stabilizing yarn tension control during the conveying process. The application of this algorithm can enhance the overall elasticity uniformity and surface flatness of knitted fabrics, thereby facilitating the realization of adjustable and controllable local elasticity in fabrics.

Implementation of Rapid Nucleic Acid Amplification Based on the Super Large Thermoelectric Cooler Rapid Temperature Rise and Fall Heating Module.

In the rapid development of molecular biology, nucleic acid amplification detection technology has received more and more attention. The traditional polymerase chain reaction (PCR) instrument has poor refrigeration performance during its transition from a high temperature to a low temperature in the temperature cycle, resulting in a longer PCR amplification cycle. Peltier element equipped with both heating and cooling functions was used, while the robust adaptive fuzzy proportional integral derivative (PID) algorithm was also utilized as the fundamental temperature control mechanism. The heating and cooling functions were switched through the state machine mode, and the PCR temperature control module was designed to achieve rapid temperature change. Cycle temperature test results showed that the fuzzy PID control algorithm was used to accurately control the temperature and achieve rapid temperature rise and fall (average rising speed = 11 °C/s, average falling speed = 8 °C/s) while preventing temperature overcharging, maintaining temperature stability, and achieving ultra-fast PCR amplification processes (45 temperature cycle time < 19 min). The quantitative results show that different amounts of fluorescence signals can be observed according to the different concentrations of added viral particles, and an analytical detection limit (LoD) as low as 10 copies per μL can be achieved with no false positive in the negative control. The results show that the TEC amplification of nucleic acid has a high detection rate, sensitivity, and stability. This study intended to solve the problem where the existing thermal cycle temperature control technology finds it difficult to meet various new development requirements, such as the rapid, efficient, and miniaturization of PCR.

Dynamics modeling and nonlinear attitude controller design for a rocket-type unmanned aerial vehicle

This paper presents an altitude and attitude control system for a newly designed rocket-type unmanned aerial vehicle (UAV) propelled by a gimbal-based coaxial rotor system (GCRS) enabling thrust vector control (TVC). The GCRS is the only means of actuation available to control the UAV’s orientation, and the flight dynamics identify the primary control difficulty as the highly nonlinear and tightly coupled control distribution problem. To address this, the study presents detailed derivations of attitude flight dynamics and a control strategy to track the desired attitude trajectory. First, a Proportional-Integral-Derivative (PID) control algorithm is developed based on the formulation of linear matrix inequality (LMI) to ensure robust stability and performance. Second, an optimization algorithm using the Levenberg–Marquardt (LM) method is introduced to solve the nonlinear inverse mapping problem between the control law and the actual actuator outputs, addressing the nonlinear coupled control input distribution problem of the GCRS. In summary, the main contribution is the proposal of a new TVC UAV system based on GCRS. The PID control algorithm and LM algorithm were designed to solve the distribution problem of the actuation model and confirm altitude and attitude tracking missions. Finally, to validate the flight properties of the rocket-type UAV and the performance of the proposed control algorithm, several numerical simulations were conducted. The results indicate that the tightly coupled control input nonlinear inverse problem was successfully solved, and the proposed control algorithm achieved effective attitude stabilization even in the presence of disturbances.

Learning From Human Educational Wisdom: A Student-Centered Knowledge Distillation Method.

Existing studies on knowledge distillation typically focus on teacher-centered methods, in which the teacher network is trained according to its own standards before transferring the learned knowledge to a student one. However, due to differences in network structure between the teacher and the student, the knowledge learned by the former may not be desired by the latter. Inspired by human educational wisdom, this paper proposes a Student-Centered Distillation (SCD) method that enables the teacher network to adjust its knowledge transfer according to the student network's needs. We implemented SCD based on various human educational wisdom, e.g., the teacher network identified and learned the knowledge desired by the student network on the validation set, and then transferred it to the latter through the training set. To address the problems of current deficiency knowledge, hard sample learning and knowledge forgetting faced by a student network in the learning process, we introduce and improve Proportional-Integral-Derivative (PID) algorithms from automation fields to make them effective in identifying the current knowledge required by the student network. Furthermore, we propose a curriculum learning-based fuzzy strategy and apply it to the proposed PID control algorithm, such that the student network in SCD can actively pay attention to the learning of challenging samples after with certain knowledge. The overall performance of SCD is verified in multiple tasks by comparing it with state-of-the-art ones. Experimental results show that our student-centered distillation method outperforms existing teacher-centered ones.

Fiber-end control algorithm based on back-propagation neural network

The evenness of the trajectory of the optical fiber grinding track has a substantial effect on the quality of the transmission of optical signals. To boost this transmission quality, we present a blueprint for the trajectory of the optical fiber grinding track, together with the recently introduced equipment for shaping the end-face of the optical fiber. This model establishes the correlation between the grinding trajectory and the rotational speed of the grinding motor, enabling control of the grinding trajectory of the optical fiber connector by manipulating the motor’s rotation speed. Three-speed control methods, namely the Proportional-Integral-Derivative (PID) controller, fuzzy control algorithm, and Back-Propagation (BP) neural network PID control algorithm, were compared for effectiveness using Matlab/Simulink simulation software. The BP neural network displayed exceptional performance, leading us to implement the PID control algorithm to govern the speed of the grinding motor. Our experimental findings validate the superior efficacy of the BP neural network PID control algorithm in governing the speed of the grinding motor, thereby ensuring a consistent grinding trajectory for optical fibers.

Polymerase Chain Reaction Microchip and PID Controller Based Thermal Cycler Design

Numerous specific procedures are needed for nucleic acid detection using traditional approaches. Because of the time and instrumentation requirements, poor detection limits, and other factors, they are behind in creating point-of-care (POC) testing. In addition, some of these traditional approaches can be extremely sensitive. Microfluidic technologies are being used to overcome these challenges by enabling the efficient and automatic performance of all analytical stages, such as sample pre-treatment, reactions, separations, and diagnostics in microchannels on a small chip. The use of microfluidics technology in polymerase chain reaction (PCR) technology has resulted in many advantages. PCR microchips and portable thermal cyclers that can be used for POC testing have enabled faster amplification with accurate results. This paper proposes a low-cost, fast enough thermal cycler for POC testing that uses a polymer-glass microchip and an affordable Peltier element to quickly heat samples. A laser cutter was used to build the microchip needed to perform PCR, which was designed using a computer-aided design (CAD) software. The heating and cooling elements employed in the creation of the thermal cycler were Peltier elements, heat sink, centrifugal fan, and CPU fan. A microcontroller and H-Bridge modules were used to control the temperature with the help of proportional-integral-derivative (PID) controller algorithm. After that, the circuit components and PID control algorithm—which was created for temperature control—were combined to create the PCR cycle. The analysis focuses on the heating and cooling performances of suggested thermal cycler.

CONTROL OF A HEAT PUMP COMPRESSOR WITH VARIABLE FREQUENCY DEVICE DRIVEN BY PID

Integrating advanced control technologies to enhance energy efficiency and environmental sustainability in heat pump systems is of growing significance. This article offers a comprehensive review on the utilization of Proportional-Integral-Derivative (PID) control and Variable Frequency Drive (VFD) technology to govern the compressor's operation in a heat pump. The primary objective is to optimize energy consumption and thermal output under varying loads and environmental conditions, thus enhancing the heat pump's performance.&#x0D; &#x0D; The article delves into the fundamental principles of heat pump operations, emphasizing the compressor's pivotal role in maintaining the delicate balance between energy consumption and desired thermal output across applications, spanning residential HVAC to industrial processes. The PID control algorithm is introduced to adapt the compressor's speed and power consumption dynamically. The VFD is incorporated into the control system, enabling variable speed compressor operation for a responsive reaction to load fluctuations. Combining PID and VFD control is explored to achieve peak system performance and energy efficiency.&#x0D; &#x0D; Through practical experiments and simulations, the research investigates the influence of PID and VFD control on energy efficiency, stability, and performance. The results underscore substantial energy savings and environmental impact reduction potential, particularly in scenarios with variable thermal loads and fluctuating environmental conditions.&#x0D; &#x0D; This study advances our understanding of advanced control strategies in heat pump technology and underscores the pivotal role PID and VFD control play in creating energy-efficient heating and cooling solutions. The findings offer practical implications for diverse applications, from residential settings to industrial processes, and provide insights into sustainable heat pump technology use in the context of energy conservation and climate change challenges.

A new cooperative control solution of subway BAS: an improved fuzzy PID control algorithm.

The building automation system (BAS) of a subway is the core component of monitoring and managing urban rail transit systems. For the current problems such as low control efficiency, insufficient accuracy, and poor stability of metro BAS, this article proposes a cooperative control framework based on an improved fuzzy proportional-integral-derivative (PID) algorithm. Firstly, the concept of an integrated supervisory control system (ISCS) for subways is introduced by summarizing the previously implemented engineering construction and combining it with advanced automation technology. The system's overall design under the ISCS framework is also improved by integrating it with the fire alarm system (FAS) with the BAS as the core unit of the reliance. Then, an improved seeker optimization algorithm (ISOA) is employed to optimize the parameters of the fuzzy PID control algorithm to achieve a coordinated control of the system based on considering the time lag problem. Finally, the accuracy, efficiency, and stability of the coordinated control response of the BAS under the ISCS framework are tested experimentally. The results suggest that the proposed cooperative control solution of BAS employing the improved fuzzy PID algorithm has good control accuracy and response efficiency and can also ensure the BAS's higher stability in the coordinated control process, which thus greatly improves the automation level of the subway and provides a safer and more reliable high-performance for the ISCS of the subway in the urban rail transportation industry.

Photovoltaic charging multi-station with modular architecture for Light Electric Vehicles

This paper deals with a modular architecture for recharging the batteries of light electric vehicles (LEVs) using a photovoltaic (PV) generator. The architecture is organized into two hierarchical levels. At the top level (master), a microcontroller tracks the maximum power point of the PV generator. This microcontroller executes a proportional-integral-derivative (PID) control algorithm whose output is the setpoint of the microcontrollers of the lower level. At the lower level (slaves), there is a microcontroller for each vehicle charging station. Each microcontroller controls the recharge current of the vehicle battery connected to the station by executing another PID control algorithm. Modular architecture allows the number of charging stations to be extended up to 112. Other characteristics of the system are the automatic detection of the nominal voltage of the battery (it allows to recharge batteries of 24V, 36V or 48V) and the inclusion of protection functions as battery overload or detection of not allowed batteries.

Design model-free adaptive PID controller based on lazy learning algorithm

Abstract The nonlinear system is difficult to achieve the desired effect by using traditional proportional integral derivative (PID) or linear controller. First, this study presents an improved lazy learning algorithm based on k-vector nearest neighbors, which not only considers the matching of input and output data, but also considers the consistency of the model. Based on the optimization index of an additional penalty function, the optimal solution of the lazy learning is obtained by the iterative least-square method. Second, based on the improved lazy learning, an adaptive PID control algorithm is proposed. Finally, the control effect under the condition of complete data and incomplete data is compared by simulation experiment.

SYSTEM DEVELOPMENT FOR ENHANCING BOILER PERFORMANCE: FROM PID CONTROL TO ADAPTIVE AND MODEL PREDICTIVE CONTROL FOR MORE EFFECTIVE OPTIMIZATION OF TEMPERATURE, PRESSURE, AND LEVEL CONTROL IN BOILER SYSTEM

When discussing control systems for boilers, the PID (Proportional-Integral-Derivative) control algorithm is one of the most well-known and widely used. The PID control algorithm has been proven effective in controlling temperature and pressure within boiler systems. However, as technology advances, there have been advancements in more sophisticated and efficient control systems. One of these advancements is the utilization of adaptive control and model predictive control. Adaptive control can adapt to changes in operational conditions and provide better performance compared to PID control. Meanwhile, model predictive control utilizes a mathematical model of the system to predict future behavior and make control decisions based on these predictions. Nevertheless, the PID control algorithm remains crucial and can deliver satisfactory performance in temperature and pressure control within boilers. The key to effective use of PID control lies in the selection of appropriate parameters and proper tuning. By employing the correct tuning methods, PID control can achieve optimal performance and high efficiency in boiler systems. Overall, the use of the PID control algorithm remains a good choice for temperature and pressure control in boiler systems, despite advancements in more advanced and efficient control systems. Model predictive control, on the other hand, utilizes a mathematical model of the boiler system to predict its future behavior. By solving an optimization problem over a predictive horizon, it determines the optimal control actions to be applied. This proactive approach allows model predictive control to account for constraints, nonlinearity, and system dynamics, leading to improved control performance and energy efficiency. In conclusion, while PID control remains a viable option for temperature and pressure control in boiler systems, the advancements in adaptive control and model predictive control offer more sophisticated and efficient alternatives. The choice of control strategy depends on the specific requirements, complexity, and desired performance of the boiler system. Combining different control techniques or employing advanced control algorithms can further enhance the overall control performance and optimize the operation of the boiler system.

Research on the Sliding Mode – PID control algorithm tuned by fuzzy method for vehicle active suspension

The road surface's roughness is the primary source of vehicle oscillations when in motion. An active suspension is employed to enhance road holding, ride comfort, and stability. This research studies a dynamics model with 5 state variables to simulate vehicle oscillations based on four excitation scenarios from the road surface. In each instance, four distinct circumstances were discovered. Besides, the complicated control solution for an active suspension was created by this research, and it is called SMPIDF (Sliding Mode – PID tuned by Fuzzy). This is an entirely innovative algorithm with several significant benefits. The system's ultimate control signal is synthesized from the signals of the linear controller PID (Proportional – Integral – Derivative) and the nonlinear controller SMC (Sliding Mode Control). The defined fuzzy rule will continually alter the controller's settings. The simulation findings indicate that the acceleration and displacement of a car body are drastically decreased when an active suspension is managed to utilize the SMPIDF algorithm. The displacement and acceleration values do not surpass twenty percent and eighty percent, respectively, when compared to a car using a standard passive suspension. In addition, the characteristic of "chattering" does not occur when the controllers are combined. The overall effectiveness of this algorithm is rather significant. This approach applies to increasingly complicated models.

Design of a temperature-feedback controlled automated magnetic hyperthermia therapy device.

Magnetic hyperthermia therapy (MHT) is a minimally invasive adjuvant therapy capable of damaging tumors using magnetic nanoparticles exposed radiofrequency alternating magnetic fields. One of the challenges of MHT is thermal dose control and excessive heating in superficial tissues from off target eddy current heating. We report the development of a control system to maintain target temperature during MHT with an automatic safety shutoff feature in adherence to FDA Design Control Guidance. A proportional-integral-derivative (PID) control algorithm was designed and implemented in NI LabVIEW®. A standard reference material copper wire was used as the heat source to verify the controller performance in gel phantom experiments. Coupled electromagnetic thermal finite element analysis simulations were used to identify the initial controller gains. Results showed that the PID controller successfully achieved the target temperature control despite significant perturbations. Feasibility of PID control algorithm to improve efficacy and safety of MHT was demonstrated.

Fuzzy Feedback Control for Electro-Hydraulic Actuators

Electro-hydraulic actuators (EHA) have recently played a significant role in modern industrial applications, especially in systems requiring extremely high precision. This can be explained by EHA’s ability to precisely control the position and force through advanced sensors and innovative control algorithms. One of the promising approaches to improve control accuracy for EHA systems is applying classical to modern control algorithms, in which the proportional–integral–derivative (PID) algorithm, fuzzy logic controller, and a hybrid of these methods are popular options. In this paper, we developed a novel version of the fuzzy control algorithm and linear feedback control method, namely fuzzy linear feedback control, to improve the control performance. To achieve the highest performance, we first designed a mathematical EHA model based on the Matlab/Simulink software packages thanks to the selected parameters, which are similar to a real EHA system. Then, we respectively applied PID, fuzzy PID (FPID), and fuzzy linear feedback control (FLFC) before comparing them to have a full view of the outstanding advantages of the proposed algorithm. The simulation results showed that the proposed FLFC algorithm is approximately 99% and 77% superior in performance to the PID and feedback control algorithms, respectively.

A self-tuning PID controller based on analog-digital hybrid computing with a double-gate SnS2 memtransistor.

Most commercial drones utilize a traditional proportional-integral-derivative (PID) controller because of its design simplicity. However, the traditional PID controller has certain limitations in terms of optimality and robustness; it is difficult to actively adjust the PID gains under some disturbances. In this study, we demonstrated an analog-digital hybrid computing platform based on double-gate SnS2 memtransistors to implement a self-tuning/energy-efficient PID controller in drones. The customized analog circuit with memtransistors executes the PID control algorithm with low power consumption; we experimentally verified that the energy consumption of the proposed hybrid computing-based PID controller is only 63% of that of the traditional PID controller. In addition, the precise tunability of analog conductance states in the memtransistor proved to be capable of reconfiguring the performance of the PID controller, where the developed self-tuning algorithm can automatically find the optimal PID control performance.