Proportional-integral-derivative Research Articles

Trajectory Planning With A* Algorithm and Tracking With Multiple Controllers

ABSTRACTThis article consists of mathematical modelling of differential drive mobile robot (DDMR), path planning and tracking application via multiple controller usage. Robot kinematic and dynamic models were constructed and simulated in virtual 2D map environment. A* algorithm was applied to the 2D map problem to acquire optimal pathfinding, efficiency and flexibility in trajectory planning stage. After the reference trajectory is obtained by algorithm, the robot needs to be guided using control strategy that manages the speed control of electric motors. At the first part of the control problem, kinematic based backstepping control (KBBC) were used to increase the efficiency of the control system. After that Sliding Mode Control (SMC) and Proportional‐Integral‐Derivative (PID) control strategies were applied to reduce tracking errors between reference and actual coordinate values and heading angle value. To test the robustness and efficiency of control combinations, two different simulation systems were designed using Matlab‐Simulink Software. First system was designed for the non‐disturbance condition, while the second system included disturbance torque and an additional mass applied condition. Test results showcasing the performance of the controllers are presented in the concluding section, through trajectory tracking and error comparison graphs.

Revising the Motion Control Parameter Optimization Research of a Two-Wheel Differential Car

This paper proposes a solution based on the particle swarm optimization algorithm to address the issue of Proportional Integral Derivative parameter selection in the motion control of a two-wheel differential car. The mathematical motion model is established based on the driving principle of a two-wheel differential car. The transfer function of the DC motor is derived in detail, based on Kirchhoff’s law and the Laplace transform. The pose renewal equation and error renewal equation of the car are based on the mathematical motion model. Finally, a numerical simulation and experimental analysis were conducted using MATLAB R2022a, Simulink 9.1 (part of R2018a), VOFA 1.3.10 software, an STM32 microcontroller, an L298N driver chip, and other hardware components. The results indicate that the particle swarm optimization algorithm enables the rapid acquisition of optimal Proportional Integral Derivative parameters. The optimized parameter of the motor speed convergence time is set to 10 ms, with an overshoot of 1 r/min and an enhanced anti-interference ability. The optimized parameters effectively regulate the car’s motion, ensuring a maximum error control of approximately 0.003 m.

A Development and Prototyping of a Two-Wheeled Self- Balancing Robot

Mobile robotics is a branch of robotics and within the branch the category of selfbalancingrobots is of particular interest as these robots show the promise of navigating difficultterrain in a manner like humans and can be used to platform for investigating autonomouscontrol systems. This paper aims to summarize the development of a two-wheeled self-balancingrobot as a case study to demonstrate the application of computer control systems in physicalsystems. A complementary filter is used with a triple-axis gyroscope and accelerometer toaccurately gauge the rotation of the two-wheeled robot and provide the data to a Proportional-Integral-Derivative (PID) program which controls the power to the motors accordingly to controlits tilt and achieve self-balancing. In short, the robot manages to achieve self-balancing within asmall tilt angle range, however, design flaws such as the sensor falling off at larger tilt anglescause instability at larger tilt angles. In future work, more complex control algorithms can beemployed, and the effect of different robot builds can be thoroughly explored

A Neural Controller Design for Enhancing Stability of a Single Machine Infinite Bus Power System

This paper examines the formulation and implementation of a neuro-controller for the excitation system of synchronous generators in a Single-Machine Infinite Bus (SMIB) power system. The SMIB model is employed as a fundamental model of a power system, thereby facilitating the assessment and comparison of disparate control strategies with the objective of enhancing system stability. The goal of this study is to enhance the stability of the SMIB power system through the implementation of an Artificial Neural Network (ANN) neuro-controller, providing a comparison of its performance to that of a Power System Stabilizer (PSS) and a Proportional-Integral-Derivative (PID) controller. The proposed neuro-controller will be integrated into the generator's excitation system and will be designed to regulate the excitation voltage in response to fluctuations in the system's operational parameters. To this end, an ANN is calibrated to account for the singularity of the generator's excitation level and terminal voltage. The Levenberg-Marquardt algorithm is employed to ascertain the optimal weight coefficients for the ANN. To assess the performance of the neuro-controller, simulations were conducted using MATLAB/Simulink. The simulations encompass a comprehensive range of operational scenarios, including diverse disturbances and alterations in the reference voltage level. Subsequently, the neuro-controller's outputs are evaluated in comparison to the PSS and PID controllers, as these are the prevailing controllers used to enhance voltage regulation and transient stability in power systems. This paper presents the results of an analysis of the neuro-controller's impact on the system's robustness, voltage variation amplitude, and generator dynamic performance during faults. Simulation results demonstrate that the application of an ANN-based neuro-controller yields superior outcomes in voltage regulation and transient stability compared to the conventional controllers PSS and PID. Furthermore, the neuro-controller is distinguished by accelerated response times and enhanced precision in voltage level regulation. The neuro-controller represents a superior approach to the control of a power system, particularly in the context of SMIB, which would ultimately result in enhanced performance and stability.

Model-Free Adaptive Control of an Active Half-Vehicle-Seat System Coupled with a Nonlinear Energy Sink Inerter (NESI)

In order to reduce vehicle vibration and improve vehicle ride comfort and handling stability, a nonlinear energy sink inerter (NESI) is designed by combing an inerter and nonlinear energy sink (NES) for use in the seat suspension and vehicle suspension for the half-vehicle-seat (HVS) system; furthermore, a model-free adaptive control (MFAC) method based on the genetic algorithm is proposed to enhance the dynamic performance of the passive HVS system. The dynamic model of the active HVS system coupled with NESI using the MFAC method is established; its dynamic responses under pavement random and shock excitations are acquired using the numerical method and the dynamic performance is evaluated by seven evaluation indicators. The efficacy of the MFAC method is demonstrated through comparative analysis with the original passive HVS system, the HVS system coupled with NESI, and the active HVS system coupled with NESI using the proportional integral derivative (PID) control method. In addition, the influence of the installed position of MFAC on the dynamic performance of the active HVS system coupled with NESI is examined. The results show that for the active HVS system coupled with NESI using the MFAC method, compared with the other three HVS systems, the root mean square (RMS) values of the vehicle body vertical acceleration, vehicle body pitch acceleration, seat vertical acceleration, and front and rear suspension dynamic travel under pavement random excitation are smaller, the corresponding peak amplitudes under pavement shock excitation reduce, and the vibration attenuation time shortens; the RMS values of the front and rear dynamic tire loading under pavement random excitation are slightly smaller, the corresponding peak amplitudes under pavement shock excitation increase, and the vibration attenuation time decreases, which reflects the best dynamic performance among the four HVS systems and shows the effectiveness of the MFAC method. Furthermore, the control effect of the MFAC method is the best when it acts both on the seat and vehicle suspensions.

Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm

The increasing complexity of modern plant systems and the limitations of precise mathematical modeling have led to a shift towards data-driven control methods. These methods present an effective alternative that treats plants as black-box systems without requiring explicit models. The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. Nonetheless, control performance can be limited by the fixed probability coefficient used in the original SEDA to disrupt design parameters, especially when balancing exploration and exploitation phases. This study proposes an enhanced version of the SEDA: the modified SEDA (MSEDA), to address this issue. The MSEDA introduces a dynamic probability coefficient that decreases with each iteration. The adjustment improves the balance between exploration and exploitation phases, which enhances control accuracy. The MSEDA was used to tune the brain emotional learning-based intelligent controller (BELBIC) together with a proportional-integral-derivative (PID) controller. The result was the BELBIC-PID controller inspired by the limbic system of the human brain, which has high precision. The effectiveness of the proposed MSEDA-BELBIC-PID was validated using simulations on multi-input multi-output (MIMO) systems, with a focus on tracking performance and computational efficiency. The statistical analysis of 30 independent trials demonstrated that the proposed MSEDA-BELBIC-PID was significantly improved over the original SEDA-BELBIC-PID. Wilcoxon's rank test yielded a fitness function p-value < 0.05, which confirmed the robustness and effect of the proposed enhancement. The comparative results demonstrated that the MSEDA-BELBIC-PID consistently performed better than the original approach and had improved fitness function values, reduced total integral square error, and lower total integral square input. These findings underscored the MSEDA suitability as a data-driven tool for controller design parameter optimization. Furthermore, the low computational burden of MSEDA rendered it a strong alternative to heuristic multi-agent algorithms, which frequently encounter high computational costs with large controller design parameters.

Improvement of NC Program Quality based on Shape Generation Motions and Feed Drives for Five-Axis CNC Machine Tools

Five-axis Computer Numerical Control (CNC) machine tools, integrated with Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems, are used to machine complex parts and reduce trials and errors. However, these machine tools still rely on Numerical Control (NC) programs and often lack accuracy and precision due to poor quality when implemented in the machine. This research aims to enhance the quality of NC programs for five-axis CNC machine tools by focusing on shape generation motions and a closed-loop feed drive system with Proportional-Integral-Derivative (PID) control. The individual motions were mathematically described using 4x4 transformation matrices, incorporating kinematic motion deviations, end mill geometry, machining parameters, and cutting forces derived from virtual machining. Additionally, a closed-loop feed drive system with PID control was integrated with the new position and angular data of each axis from the shape generation motions model. The new NC programs were validated by machining an S-shaped part and measuring dimensional errors at 64 points before and after using a Coordinate Measuring Machine (CMM). The results indicate a substantial reduction in the standard deviations of form and angular errors within the NC program quality, totaling approximately 80.73%. Reductions are demonstrated in the standard deviations for the X, Y, A, and B axes, with decreases of 76.83%, 95%, 82.40%, and 68.72%, respectively indicating a significant improvement in the overall quality of the NC program.

MPPT controller design and comparison of selected site Cox’s Bazar

Wind energy is a popular green source of energy. Like other countries, Bangladesh is also in a process of incorporating wind turbine generator (WTG) into its existing power system. Out of several locations, Cox’s Bazar has the great potential to adopt WTG. The location has every suitability to be selected for offshore WTG farm. From technical perspective, power coefficient plays a significant role in determining the power output. Against variables, maximum power coefficient (Cp) is desired to extract the maximum power from the system. Tip speed ratio (TSR) and pitch angle are the common parameters to be controlled for maximum power point tracking (MPPT). Conventionally, the Proportional - Integral - Derivative (PID) controller is used but they have their limitations against high nonlinearity caused by wind turbine aerodynamics. This paper proposes three types of controllers: Proportional controller (PI), artificial neural network (ANN), and fuzzy logic controllers. The controller is applied to both TSR and pitch angle control. Pitch angle is controlled to avoid the extreme wind gusts. All the simulation works are carried out in MATLAB and Simulink. This paper also analyzes the technical feasibility of the Cox’s Bazar location for offshore WTG.

Optimizing the position of photovoltaic solar tracker panels with artificial intelligence using MATLAB Simulink

This research aims to apply an artificial intelligence (AI) system to control the position of photovoltaic (PV) panels to maximize the use of solar energy using the solar tracker. The implementation of AI algorithms to achieve optimal panel orientation, considering factors such as sunlight intensity and sun position is also discussed. The simulation results using matrix laboratory (MATLAB) Simulink can be observed on the scope, displaying the position control graph of the solar panel from sunrise to sunset. By employing proportional integral derivative (PID) control, the error is likely to be minimal, ensuring that the panel will continue to follow the sun until it sets at the maximum point of 4:00 PM. After that, the panel can be adjusted back or reset to the initial position at 6:00 AM for the following day. In a full-day simulation, the solar panel will follow the sun's movement from sunset to sunrise. At the basic level, sunrise occurs in the first hour at position 1.0, which is 6:00 AM in the minimum point at the bottom left corner of the curve, and sunset occurs in the afternoon at position 5.25, which is 4:00 PM at the maximum point in the top right corner of the curve.

Fuel recycling feedback control via real-time boron powder injection in EAST with full metal wall

Abstract A feedback control of fuel recycling via real-time boron powder injection, addressing the issue of continuously increasing recycling in long-pulse plasma discharges, has been successfully developed and implemented on EAST tokamak. The feedback control system includes four main parts: the impurity powder dropper (IPD), a diagnostic system measuring fuel recycling level represented by Dα emission, a plasma control system (PCS) implementing the Proportional Integral Derivative (PID) algorithm, and a signal converter connecting the IPD and PCS. Based on this control system, both active control and feedback control experiments have recently been performed on EAST with a full metal wall. The experimental results show that the fuel recycling can be gradually reduced to lower level as PCS control voltage increases. In the feedback control experiments, it is also observed that the Dα emission is reduced to the level below the target Dα value by adjusting boron injection flow rate, indicating successful implementation of the fuel recycling feedback control on EAST. This technique provides a new method for fuel recycling control of long pulse and high parameter plasma operations in future fusion devices.

Low cost pulsed electric field generator using DC-DC boost converter and capacitor diode voltage multiplier

Traditional high-voltage pulse generators, like Marx generators often face challenges related to efficiency and complexity. In this paper, a solid-state multi-module high-voltage pulse generator that integrates capacitor-diode voltage multipliers (CDVM) with DC-DC boost converters and closed-loop voltage control is proposed to overcome these challenges. The system achieves high output voltage by coupling the pulsed output voltages of individual low-voltage DC sources in series across each module. The proposed design was modeled using MATLAB, and experimental testing was conducted on a single stage. Comparative analyses between timedomain parameters, proportional-integral (PI), and fractional order proportional integral derivative (FOPID) controllers were performed. Both MATLAB simulations and experimental validations demonstrate the effectiveness of this approach. The rise time, peak time, settling time, and steady-state error are all improved using an FOPID controller, decreasing from 0.32 to 0.31 seconds, 0.42 to 0.35 seconds, and 3.15 to 2.20 seconds, respectively. These findings indicate that a closed-loop FOPID controller enhances time-domain performance parameters more effectively than a PI controller for a two-stage DC-DC voltage multiplier.

Comparative Analysis of Fuzzy Logic, PID, and FOPID Controllers in DC Microgrid Voltage Regulation for Power Plants: Integrating Renewable Energy Sources

The worldwide direction moving towards sustainable energy solutions requires more advanced control mechanisms within power systems, especially in DC microgrids that combine renewable energy sources like solar panels and wind turbines with conventional power setups. This paper addresses the necessary challenge of maintaining steady and efficient DC voltage control—important for the dependability and performance of power plants. Specifically, it looks at the efficiency of four different control strategies: classic PID (Proportional-Integral-Derivative) controllers, Fuzzy Logic Controllers (FLC), Fractional Order PID (FOPID) controllers, and a new mixed system that merges Fuzzy Logic with PID controllers in a Fuzzy-PI layout. Our approach utilizes a strict comparative analysis using MATLAB Simulink simulations, assessing each controller’s capacity to manage dynamic load shifts, adjust to the unpredictability of renewable energy inputs, and preserve strong voltage stability under various operational cases. The simulations are crafted to single out the most effective control strategy to boost efficiency and toughness in microgrid operations. This study adds substantially to the progress of control strategies in hybrid energy systems, offering important insights into the creation of more complex and reliable power management solutions. By evaluating the performance of these varied controllers, we aim to underline potential upgrades in control systems that could support better energy management in intricate grid setups, thereby encouraging the wider inclusion of renewable energy technologies into power grids.

Optimization of Frequency Stability in Hydraulic Load Frequency Control Systems Using Controllers with Filters

This study meticulously investigates the design and analysis of a Load Frequency Control (LFC) system tailored explicitly for hydraulic power systems by employing diverse configurations of PID controllers, namely Proportional (P), Proportional-Integral (PI), Proportional-Derivative (PD), Proportional-Integral-Derivative (PID), Proportional-Derivative-Fractional (PDF), and Proportional-Integral-Derivative-Fractional (PIDF). The primary objective of this exploration is to improve frequency stability in response to various load fluctuations typical in hydraulic systems. A comprehensive evaluation of each controller's performance is conducted through extensive MATLAB simulations, focusing on critical performance metrics such as rise time, peak time, steady-state time, and maximum overshoot. The findings reveal that the PD and PDF controllers exhibit superior response characteristics, offering the fastest and most stable outcomes regardless of whether droop characteristics are utilized or filtering techniques are introduced. Although the implementation of filters significantly mitigates overshoot, it is evident that controllers incorporating droop characteristics tend to compromise optimal steady-state time stability. The overall analysis underscores the PD and PDF controllers as the preeminent solutions for ensuring frequency stability in hydraulic LFC systems, especially under abrupt and substantial load changes, thus positioning them as vital tools in enhancing hydraulic power system reliability and efficiency.

Complex Dynamics and PID Control Strategies for a Fractional Three-Population Model

In recent decades, there have been many studies on Hopf bifurcation and population stability with time delay. However, the stability and Hopf bifurcation of fractional-order population systems with time delay are lower. In this paper, we discuss the dynamic behavior of a fractional-order three-population model with pregnancy delay using Laplace transform of fractional differential equations, stability and bifurcation theory, and MATLAB software. The specific conditions of local asymptotic stability and Hopf bifurcation for fractional-order time-delay systems are determined. A fractional-order proportional–integral–derivative (PID) controller is applied to the three-population food chain system for the first time. The convergent speed and vibration amplitude of the system can be changed by PID control. For example, after fixing the values of the integral control gain ki and the differential control gain kd, the amplitude of the system decreases and the convergence speed changes as the proportional control gain kp decreases. The effectiveness of the PID control strategy in complex ecosystem is proved. The numerical simulation results are in good agreement with the theoretical analysis. The research in this paper has potential application values concerning the management of complex population systems. The bifurcation theory of fractional-order time-delay systems is also enriched.

An Optimal Load Frequency Control in a Two-Area Power System Using a Fractional Order Proportional- Integral-Derivative-Based Zebra Optimization Algorithm

Load frequency control systems are crucial for maintaining the stability and reliability of power grids. They help ensure that the power supply matches the demand, preventing fluctuations in grid frequency. However, Load frequency control systems have inherent limitations, such as the potential for instability and oscillation if not properly controlled. In this work, A fractional order proportional integral derivative controller is proposed to address this issue, which exhibits strong capabilities in managing parameter uncertainties, rejecting disturbances, and handling non-linear systems controllers. The novel approach in this search is the application of the zebra optimization algorithm to fine-tune controller parameters, a technique not previously used in load frequency control systems. In this Study the two-area power system with a reheat turbine is used a test case for fractional order proportional integral derivative controller, and the system is simulated using MATLAB/SIMULINK. The objective function integral time of square error is used that acts as a bridge of communication between the system's behavior and the control strategy. The performance of this controller is evaluated under disturbance 0.04. The results of the analysis demonstrated that the fractional order proportional integral derivative based zebra optimization algorithm controller outperformed the traditional fractional order proportional integral derivative controller in terms of three essential criteria: overshoot, settling time, and steady-state error. This research contributes to the advancement of load frequency control systems, ensuring reliable and stable power system operation.

Adaptive Impedance Control of Multirotor UAV for Accurate and Robust Path Following

Unmanned Aerial Vehicles (UAVs) have become essential tools in various industries for tasks such as inspection, maintenance, and surveillance. An Online Impedance Adaptive Controller (OIAC) is proposed for the online modulating of UAV control gains to obtain better performance and stability of tracking curved trajectories than the traditional methods, Model Reference Adaptive Controller (MRAC) and Proportional–Integral–Derivative (PID). Two UAV path planners with minimal jerk and snap were integrated into OIAC, MRAC, and PID. These six controllers were implemented and compared in a simulated UAV with perceptional noise, which follows curved pipelines and avoids obstacles. Experimental results show that the OIAC controller achieves at least an 80% improvement over the PID controller across all trajectory types in terms of the trajectory tracking error. Additionally, OIAC demonstrates an over 20% improvement in jerk trajectories and a more than 30% improvement in snap trajectories when compared to the MRAC controller. These results indicate that OIAC offers enhanced trajectory tracking accuracy and robustness against perceptual noise. Our work presents an advanced controller of a UAV and its preliminary validation in accurate and robust path tracking.

Oscillation Suppression Method of Digital Proportional Valve Based on Fuzzy Intelligent PID Control

A digital proportional valve is constituted by the main spool and a high-speed on/off valve bridge acting as the pilot stage. However, the main spool will generate oscillation during movement under the control of the pilot stage. This results in poor stability, slow response speed, low control accuracy, and even the potential loss of control of the valve. To tackle this issue, an oscillation suppression method based on fuzzy intelligent proportional-integral-derivative (PID) control is put forward. The movement state of the main spool is determined in accordance with its movement position and velocity. Thereafter, the fuzzy control parameters of the controller are calculated on the basis of the determined movement state of the main spool. Different PID parameters are adopted to eliminate the control effect difference caused by the structural asymmetry of the two pilot control chambers of the main valve. The performance and robustness of the proposed control method are verified by comparison with the PID controller based on full-bridge and half-bridge control. The results demonstrate that the proposed control method can effectively suppress the oscillation of the main spool of the digital proportional valve, improve the control accuracy, and reduce the response time. When the excitation signal takes the form of a step signal, the overshoot of the control method put forward in this paper is diminished by 26.2% in comparison with that of the PID control. Under stable operating conditions, the maximum tracking error is less than 3.1%. Moreover, compared with simply using the PID control method, this error is reduced by 41%.

Research on Omnidirectional Gait Switching and Attitude Control in Hexapod Robots

To tackle the challenges of poor stability during real-time random gait switching and precise trajectory control for hexapod robots under limited stride and steering conditions, a novel real-time replanning gait switching control strategy based on an omnidirectional gait and fuzzy inference is proposed, along with an attitude control method based on the single-neuron adaptive proportional–integral–derivative (PID). To start, a kinematic model of a hexapod robot was developed through the Denavit–Hartenberg (D-H) kinematics analysis, linking joint movement parameters to the end foot’s endpoint pose, which formed the foundation for designing various gaits, including omnidirectional and compound gaits. Incorporating an omnidirectional gait could effectively resolve the challenge of precise trajectory control for the hexapod robot under limited stride and steering conditions. Next, a real-time replanning gait switching strategy based on an omnidirectional gait and fuzzy inference was introduced to tackle the issue of significant impacts and low stability encountered during gait transitions. Finally, in view of further enhancing the stability of the hexapod robot, an attitude adjustment algorithm based on the single-neuron adaptive PID was presented. Extensive experiments confirmed the effectiveness of this approach. The results show that our approach enabled the robot to switch gaits seamlessly in real time, effectively addressing the challenge of precise trajectory control under limited stride and steering conditions; moreover, it significantly improved the hexapod robot’s dynamic stability during its motion, enabling it to adapt to complex and changing environments.

Modeling, Simulation, and Control of a Rotary Inverted Pendulum: A Reinforcement Learning-Based Control Approach

In this paper, we address the modeling, simulation, and control of a rotary inverted pendulum (RIP). The RIP model assembled via the MATLAB (Matlab 2021a)®/Simulink (Simulink 10.3) Simscape (Simscape 7.3)™ environment demonstrates a high degree of fidelity in its capacity to capture the dynamic characteristics of an actual system, including nonlinear friction. The mathematical model of the RIP is obtained via the Euler–Lagrange approach, and a parameter identification procedure is carried out over the Simscape model for the purpose of validating the mathematical model. The usefulness of the proposed Simscape model is demonstrated by the implementation of a variety of control strategies, including linear controllers as the linear quadratic regulator (LQR), proportional–integral–derivative (PID) and model predictive control (MPC), nonlinear controllers such as feedback linearization (FL) and sliding mode control (SMC), and artificial intelligence (AI)-based controllers such as FL with adaptive neural network compensation (FL-ANC) and reinforcement learning (RL). A design methodology that integrates RL with other control techniques is proposed. Following the proposed methodology, a FL-RL and a proportional–derivative control with RL (PD-RL) are implemented as strategies to achieve stabilization of the RIP. The swing-up control is incorporated into all controllers. The visual environment provided by Simscape facilitates a better comprehension and understanding of the RIP behavior. A comprehensive analysis of the performance of each control strategy is conducted, revealing that AI-based controllers demonstrate superior performance compared to linear and nonlinear controllers. In addition, the FL-RL and PD-RL controllers exhibit improved performance with respect to the FL-ANC and RL controllers when subjected to external disturbance.

Application of PID Control Technology in Unmanned Aerial Vehicles

Abstract. The rapid advancement of unmanned aerial vehicle (UAV) technology underscores the essential role of PID (proportional-integral-derivative) control in ensuring flight stability, particularly through precise motor speed adjustments. Thus, this paper systematically analyzes the fundamental principles and limitations of traditional PID control, further highlighting its insufficient adaptability to dynamic external conditions, which includes load fluctuations and wind disturbances. In response to these challenges, it explores adaptive control strategies such as self-tuning PID and fuzzy logic PID, points out how these advanced methods can effectively improve flight stability by dynamically adjusting control parameters, and demonstrates their superiority over traditional PID through comparative analysis. In addition, the paper anticipates future developments in UAV control systems, thereby emphasizing the integration of artificial intelligence and machine learning technologies into PID control. And this integration seeks to optimize control strategies and markedly enhance the autonomous decision-making capabilities and adaptability of UAVs. Meanwhile, the paper also predicts the development trend of hybrid control systems, that is, combining PID with other advanced control methods (such as linear quadratic control) to enhance the overall control capabilities of UAVs. In short, it underscores the necessity for continuous innovation and in-depth research in response to the dynamic flight environment and the diverse mission requirements.