Proportional Integral Derivative Control Algorithm Research Articles

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.

Improved Adaptive PSO-based Gain Tuning for PID Controllers in Utility Boilers

In this paper, the stabilization and set point tracking of the utility boiler is achieved via the Proportional-Integral-Derivative (PID) controller, tuned using the versions of metaheuristic Particle Swarm Optimization (PSO) algorithms. The utility boiler model dynamics are based on the Bell and Astrom boiler turbine system. Initially, the non-linear mathematical model is obtained using the first principles data from the plant model. Further, Taylor series approximation is applied to linearize the non-linear equations around the equilibrium point to implement a stable controller for the plant. Finally, the PID controller gains for the plant model are determined using the PSO algorithms to achieve the desired response. For tuning the PID controller, three PSO algorithms namely standard PSO, Adaptive PSO (APSO) and Improved Adaptive PSO (IAPSO) are proposed. The performance of the proposed PSO based PID control algorithms are evaluated using error metrics such as Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE) and Integral Square Error (ISE). The simulation results show that the proposed APSO and IAPSO algorithms have improved performance when compared to regular PSO algorithms and are capable of optimizing the control parameters.

Control algorithm of weight on bit and rate of penetration based on drilling robot

At present, the traction force and traction speed of the drilling robot cannot be controlled. However, if the speed of the drilling robot is too high, the bit will be damaged because of the shock load. If the speed of the drilling robot is too low, the drilling efficiency will be very low. The existing control system and control algorithm of the drilling robot cannot meet the requirements of the drilling conditions. A control system of the weight on bit and the rate of penetration of the drilling robot was invented. On the basis, a dual-loop fuzzy proportional integral derivative (PID) control algorithm of the pressure difference and flow rate was proposed to control the weight on bit and rate of penetration. Furthermore, the simulation model of the dual-loop fuzzy PID control algorithm was established. It is found that the proposed dual-loop fuzzy PID control algorithm has the characteristics of small overshoot and good tracking. Furthermore, the correctness of the theory was verified by the experiments. The experiments show that the average amplitude of rate of penetration vibration by using adaptive PID controller is less than 50% of that of PID controller. The maximum weight on bit with the adaptive PID controller is only 45.94% of that of the conventional PID controller. The overshoot of the adaptive PID controller is only 24.07% of that of the conventional PID controller. At the same time, the vibration amplitude of the conventional PID controller is obviously larger than that of the adaptive PID controller. The proposed adaptive PID controller in this paper can effectively reduce the vibration of the bit. By reducing the vibration of the bit, the life of the bit can be extended. Finally, it is of great significance to realize the efficient and intelligent drilling process.

Fuzzy PID control method for damping of electronically controlled air suspension shock absorbers for vehicles

A damping fuzzy proportional-integral-derivative (PID) control method for electronically controlled air suspension shock absorbers was proposed to reduce the RMS values of the body vibration acceleration and improve vehicle ride comfort. Depending on the operating mode of the electronically controlled air suspension shock absorber, a mechanical model of the air suspension shock absorber was established. A fuzzy PID control technology for damping control of the shock absorber is designed by combining a PID control algorithm with the fuzzy control concept. The adaptive expansion factor of the cybernetics domain for the fuzzy PID controller is calculated by considering nonlinear characteristics and time delay to improve the controller’s adaptability. Then, the control parameters are adjusted based on the control deviation and deviation rate parameters. The nonlinear detection platform produced by an enterprise is selected to build an experimental environment for analysis. Simulation results reveal that when the vehicle speed is 30 km h−1, the RMS values of the body vibration acceleration upon applying the proposed method is approximately 0.02 and 0.05 km h−2, respectively, on Class B and C road surfaces. Furthermore, the RMS values of the body vibration acceleration using the proposed method has little fluctuation; when the vehicle speed is 60 km h−1, the RMS values of the body vibration acceleration is 0.022 and 0.028 km h−2, respectively, on Class B and C road surfaces, indicating that the proposed method can effectively reduce the RMS values of the body vibration acceleration and improve rider comfort.

A Novel Fuzzy Controller for Visible-Light Camera Using RBF-ANN: Enhanced Positioning and Autofocusing.

To obtain high-precision for focal length fitting and improve the visible-light camera autofocusing speed, simultaneously, the backlash caused by gear gaps is eliminated. We propose an improved RBF (Radical Basis Function) adaptive neural network (ANN) FUZZY PID (Proportional Integral Derivative) position closed-loop control algorithm to achieve the precise positioning of zoom and focus lens groups. Thus, the Levenberg-Marquardt iterative algorithm is used to fit the focal length, and the improved area search algorithm is applied to achieve autofocusing and eliminate backlash. In this paper, we initially adopt an improved RBF ANN fuzzy PID control algorithm in the position closed-loop in the visible-light camera position and velocity double closed-loop control system. Second, a similar triangle method is used to calibrate the focal length of the visible-light camera system, and the Levenberg-Marquardt iterative algorithm is used to fit the relation of the zoom potentiometer code values and the focal length to achieve the zoom position closed-loop control. Finally, the improved area search algorithm is used to achieve fast autofocusing and acquire clear images. The experimental results show that the ITAE (integrated time and absolute error) performance index of the improved RBF ANN fuzzy PID control algorithm is improved by more than two orders of magnitude as compared with the traditional fuzzy PID control algorithm, and the settling time is 6.4 s faster than that of the traditional fuzzy PID control. Then, the Levenberg-Marquardt iterative algorithm has a fast convergence speed, and the fitting precision is high. The quintic polynomial fitting results are basically consistent with the sixth-degree polynomial. The fitting accuracy is much better than that of the quadratic polynomial and exponential. Autofocusing requires less than 2 s and is improved by more than double that of the traditional method. The improved area search algorithm can quickly obtain clear images and solve the backlash problem.