PID Controller Research Articles

Performance analysis of position sensorless control of antagonistic shape memory alloy actuators

Abstract This paper presents a comprehensive performance analysis of position sensorless control of antagonistic shape memory alloy actuators (ASMAA), focusing on enhancing the sensorless position sensing capabilities and servo performance. The mechanisms and influencing factors of ASMAA self-sensing characteristics are comprehensively analyzed. A self-sensing model framework based on the constitutive model of ASMAA is proposed, and a self-sensing model based on differential resistance feedback is developed. An experimental platform based on the ASMAA motion mechanism is established to test and analyze SMA wires of two different materials under various pretension force conditions. The experimental results show that the antagonistic configuration mitigates hysteresis and improves linearity. A simple polynomial fitting is employed to establish the resistance-displacement relationship, achieving good accuracy with minimal residuals. The system identification is employed to avoid the parameter complexity and time-varying in the constitutive model. Based on the identified transfer function, a traditional PID controller is designed for position sensorless servo control. However, the inherent nonlinear hysteresis of ASMAA is difficult to suppress using a linear PID controller. Furthermore, a compound control strategy based on the Duhem model and PID controller is proposed, which utilizes the particle swarm optimization (PSO) algorithm to identify the Duhem inverse model controller. Experimental results demonstrate that the compound controller significantly enhances position tracking accuracy and response speed compared to a standalone PID controller.

Evaluation of Optimization Algorithms for Use in a Mobile App Aimed at Learning Autotuning of PID Controllers for Engineering Students

Evaluation of Optimization Algorithms for Use in a Mobile App Aimed at Learning Autotuning of PID Controllers for Engineering Students

Nonlinear model predictive controller of hydrogenation of dimethyl oxalate for ethylene glycol production

Abstract Ethylene glycol (EG) is a valuable commodity organic intermediate that is produced using the catalyzed gas-phase hydrogenation process of dimethyl oxalate (DMO) from syngas. The reactor process is challenging to control because of its nonlinearity and multivariable condition. Thus, this study proposes the application of Neural Wiener model predictive control (NWMPC) for DMO hydrogenation reactor control. The application of empirical-based MPC, such as NWMPC, is still new in DMO hydrogenation reactor control. In order to simulate the process, the DMO hydrogenation reactor is modeled using Aspen Plus and Aspen Dynamic software. The Neural Wiener (NW) model is developed based on state space and neural network modeling using a Linear-Nonlinear (L-N) identification approach. A validation test is also performed to verify the accuracy of the NW model. Based on the test, the model accuracy is acceptable with the coefficient of determination (R2) of 0.965 for EG output mole fraction (first output) and R2 of 0.936 for product temperature (second output). The NWMPC capability is evaluated with a PID controller to handle a setpoint change in EG output mole fraction and reject disturbance in the feed stream flow rate. The control performance results have demonstrated the superior ability of the NWMPC to handle such scenarios better than PID in terms of controller action speed and profile.

Advanced hybrid control for induction motor combining ANN-PID speed controller with neural predictive current regulator based on particle swarm optimization

This paper presents a hybrid control strategy for an induction motor (IM) organized into two nested loops. In the outer loop, an adaptive artificial neural network-based proportional-integral-derivative (ANN-PID) controller is used for speed control. The ANN-PID is divided into two stages: the first stage includes an adaptive ANN speed estimator, and the second stage includes an adaptation algorithm that fine-tunes both the weights and biases of the ANN estimator and the parameters of the PID controller. In the internal loop, a predictive current controller is used to control IM currents using neural networks. Specifically, the neural predictive controller (NPC) approaches the control problem by presenting it as an optimization problem using a neural predictor for IM currents. The considered objective function includes two elements: the current tracking error and the electromagnetic torque ripple. This function is computed over a given time horizon informed by predictions of IM currents. The particle swarm optimization (PSO) algorithm is applied to determine the optimal solution for this optimization task. The effectiveness of the hybrid control scheme is demonstrated by simulation results obtained using Matlab/Simulink, highlighting its superiority over conventional control methods. The proposed hybrid control is characterized by a reduction in torque ripple and overshoot, while also showing robustness to variations in load, rotor resistance, and rotor inductance. Comparisons with an ANN controller and a fuzzy PI controller further demonstrate the superior performance of the hybrid controller in handling nonlinear dynamics and maintaining control accuracy under various operating scenarios.

Application of PID algorithm in temperature control of pig houses farmers' mental health from the perspective of social ecological system and its implications for agricultural production efficiency

This study investigates the application of PID algorithm-based temperature control systems in pig house environmental management and its impact on agricultural production efficiency. Through field experiments conducted at Dongshun Aquaculture Ecological Development Co., Ltd. in Nanping City, Fujian Province, we evaluated the system's effectiveness in energy efficiency, production performance, and animal health. Results show that the PID system achieved 18.5% energy savings, a 9% increase in daily weight gain, and a 7.1% improvement in feed conversion ratio. Additionally, meat quality scores improved by 8.3%, and disease incidence rates decreased by 21.9%. Economic analysis indicates that the system begins to generate a positive return on investment in the second year, with cumulative benefits reaching 192% within five years. These findings highlight the potential of PID temperature control systems in improving pork production efficiency and sustainability, offering a valuable solution for modern pig farming.

Result-Adaptive PID Control Based Ant Colony Optimization Tuning for Battery Operation Control in Standalone PV System With Consumption Side Power Management

Result-Adaptive PID Control Based Ant Colony Optimization Tuning for Battery Operation Control in Standalone PV System With Consumption Side Power Management

Load frequency and virtual inertia control for power system using fuzzy self-tuned PID controller with high penetration of renewable energy

AbstractReliance on renewable energy is increasing, and generating units are being added to the network. Since renewable power fluctuates greatly, the frequency deviation of the grid becomes a crucial problem with access to renewable power generation. The fluctuation of the renewable power output of the system puts forward a higher demand for load frequency control of the power grid to increase the penetration of renewable power in the system. PID controller has proven its effectiveness for the LFC due to its simple structure and clear concept. In this article, the virtual synchronous generator is introduced and a fuzzy self-tuned PID controller is proposed for inertia control. The proposed controller is implemented in light of the significant integration of renewable energy and virtual inertia. The efficacy of the suggested controller is evaluated against the traditional PID controller for the Egyptian Power System as a case study under various load disturbance scenarios. The control technique is employed for variable loads with photovoltaic and wind turbine generation systems. Three instances of load changes are studied and the controller design is performed based on grey wolf optimizer in each case. The overshot and integral time absolute error are considered as comparison measures. The new contribution is applied to the proposed controller for the grid and virtual inertia. In the case of many load variations imposed, the disturbances of residential and industrial loads varied from 0.05, 0.01, 0.15, and 0.02 pu. The maximum overshoot is 0.005 for the proposed controller and 0.0078 for the classic PID controller. The integral time absolute error is 0.06429 for the proposed controller and 0.11481 for the classic PID controller. The results demonstrate the efficacy of the proposed controller for inertia control with high penetration of renewable energy. The results show that the proposed fuzzy self-tuned PID controller has an overshot less than the classical PID controller by 25% and integral time absolute error by 45%. These results show that the use of the proposed fuzzy self-tune controller for the grid and inertia gave a better performance in terms of the overshot value and the integral time absolute error.

Direct adaptive neural control for precision piezoelectric actuator without hysteresis compensation

This paper introduces a novel Direct Adaptive Neural Control (DANC) approach to enhance the precision of piezoelectric actuator motion control significantly. By effectively addressing the complexities of hysteresis, DANC simplifies system design while delivering superior performance. A Radial Basis Function Neural Network (RBFNN) is employed to handle system uncertainties robustly, and a switching control mechanism guarantees stability, rigorously verified through Lyapunov analysis. Experimental results using a Thorlabs PZS001 actuator convincingly demonstrate substantial performance improvements over conventional PID control, the feedforward-based neural network and feedback control (FF-FB control).

TDOF PID Controller for Enhanced Disturbance Rejection with MS-Constraints for Speed Control of Marine Diesel Engine

This study proposes a two-degree-of-freedom PID controller design and tuning method based on a simple pole placement approach to enhance servo and regulatory performance while ensuring the stability of diesel engines. In the modeling of the control target, the actuator is analytically modeled. In contrast, the type of model for the diesel engine is derived analytically, and its parameter estimation uses operational data from naval ships. The proposed controller consists of a PID controller to improve regulatory performance and a set-point filter to enhance servo response. PID controller parameters consist of the parameters of the controlled plant model and a single tuning variable. At the same time, the set-point filter comprises the controller parameters and a single weighting factor. To ensure the robust stability of the controller, the controller parameters are tuned based on the maximum sensitivity. To verify the effectiveness of this study, simulations for the speed control of a diesel engine with inherent nonlinearity were conducted under three scenarios. Performance was quantitatively analyzed using the integral of time-weighted absolute error, 2% settling time, 2% recovery time, specified maximum sensitivity, and maximum peak response value, and was compared with Skogestad’s IMC and Lee’s IMC. Based on evaluation indices, the proposed controller demonstrated superior performance in both servo and regulatory responses compared to the two existing control techniques while ensuring stability.

Implementasi Yolo V8 pada Prototipe Autonomous Underwater Robot Berbasis Raspberry Pi 5 Guna Menanggulangi Pencemaran Sampah Plastik di Daerah Perairan

Plastic waste pollution in aquatic environments threatens marine ecosystems and human health. This study developed a prototype Autonomous Underwater Robot (AUR) based on Raspberry Pi 5, equipped with the YOLO V8 algorithm for real-time detection and classification of plastic waste. Additionally, the AUR uses a PID control system that enables movement stabilization and accurate navigation towards the target. Testing was conducted to evaluate object detection accuracy using mAP and IoU metrics, as well as the PID control performance in maintaining orientation stability. Results indicate that YOLO V8 on the AUR can detect and classify objects with an mAP of 0.6772 at IoU 0.5 and a detection accuracy of 89.6%. The PID control system, with an optimal parameter setting (Kp:Ki:Kd = 125:12:8), achieved an object center accuracy of 96.4%, orientation stability of 90.8%, and a completion time of 2 seconds, demonstrating the efficiency of the AUR in addressing plastic waste in aquatic environments effectively and sustainably.

A Novel Nonlinear Adaptive Control Method for Longitudinal Speed Control for Four-Independent-Wheel Autonomous Vehicles

As autonomous driving technology and four-independent-wheel chassis systems advance, four-independent-wheel autonomous vehicles have increasingly become a focal area of modern research. The longitudinal control problem for four-independent-wheel autonomous vehicles presents challenges such as complex models, high nonlinearity, and strong system uncertainties. This paper proposes a novel hierarchical control algorithm to address these challenges, innovatively combining the advantages of adaptive backstepping and dynamic sliding mode control algorithms in the upper controller, allowing it to effectively overcome the impact of uncertain system parameters and suppress the common chattering phenomenon in the output of typical sliding mode control methods. Based on the design of the upper controller, an innovative optimized longitudinal force distribution strategy and the construction of a tire reverse longitudinal slip model are proposed, followed by the design of a fuzzy PID controller as the lower slip ratio controller to achieve precise whole-vehicle longitudinal speed tracking and improve overall control performance. This method not only improves the accuracy of speed tracking but also enhances the robustness and adaptability of the control system. Finally, the effectiveness and superiority of the proposed hierarchical control method are verified through CarSim simulations.

Trajectory Planning and Adaptive Fuzzy PID Control for Precision in Robotic Vertebral Plate Cutting: Addressing Force Dynamics and Deformation Challenges

This study explores trajectory planning and cutting force control for spinal surgical robots, focusing on managing cutting deformation and vibration during vertebral plate cutting—a critical challenge in robotic spinal surgery. Through theoretical analysis, simulation, and experimental validation, the research addresses the complexities of cutting force dynamics and the associated deformations. A dynamic equation for vertebral plate cutting is established, providing a theoretical foundation for trajectory planning. A new trajectory planning method using B-spline curves and an interpolation point constraint formula is introduced, enabling precise robot trajectory control. Additionally, an adaptive fuzzy PID control strategy with force feedback is proposed to dynamically adjust the feed rate, effectively suppressing cutting deformation and vibration in real time. Simulations and experiments validate the effectiveness of these methods in reducing force fluctuations and enhancing control stability. The findings offer valuable guidance for improving the performance and safety of spinal surgery robots, optimizing their trajectories, incorporating dynamic sensing, and enhancing safety controls. Future research should focus on refining control strategies for more complex surgical scenarios and validating their applicability under conditions closer to actual surgical environments. This work provides theoretical and practical insights into advancing robotic spinal surgery, contributing to better surgical outcomes and patient safety.

Advances and Prospects of PID Controller in Mechanical Field: From Traditional Tuning to Intelligent Optimization

Abstract. The extensive use of PID controllers in the mechanical fieldparticularly in robots and other mechanical systemsis covered in this study. In these sectors, PID controllers have become the most popular control technology due to their straightforward design and simplicity of use. PID controller performance in nonlinear complex systems is explored, along with stability and adaptive adjustment, by comparing the conventional and intelligent PID tuning methods. According to the research, control performance can be greatly enhanced by merging PID controller with intelligent technologies like fuzzy logic and genetic algorithms. These results point to the possible use of PID-based control in numerical control systems in the future. PID controllers' automatic tuning capabilities have garnered significant attention in the industrial sector. Future research will focus on the automatic tuning of PID controller to further optimize its performance and broaden its application range.

Design and comparative analysis of motor position control based on traditional PID controller and fuzzy PID controller

Abstract. Using the PID algorithm to control electrical elements is a practical industrial application that have been studied for decades. This paper constructs a study on the difference between BLDC motor control and applying traditional PID and fuzzy PID. The simulation is done through Falstad and Matlab/Simulink. The math model of the transfer function of a BLDC motor is constructed by analyzing voltage and torque equilibrium. The parameters are determined approximately by looking up the datasheet of 2338 0006s. Then, Falstad establishes the operational amplifier model of the BLDC motor and traditional PID controller. The parameters are determined through both root locus and step response presented by Falstads scope. Next, Simulink is used to realize the fuzzy PID controller. The comparison study of traditional PID controller and fuzzy PID controller is constructed by looking at the scope and export data. After that, it is concluded that fuzzy PID controller performs better than traditional PID controller, it is robust and the influence of the input parameter is small. Finally, a lead lag compensator is applied to the whole system to improve the performance in the frequency domain.

Review of PID Control in Industrial Areas for Tobacco Production, Self-driving Car Technology and Robotic Arm Robots

Abstract. PID control is widely employed across diverse industries such as tobacco production, autonomous car technology, and mechanical arm robotics. This shows that PID control has a substantial developmental potential, so I choose the PID control as the topic. Since few people summarize its different applications in different industries, this study has the motivation to comprehensively summarize the applications of PID. This research provides an in-depth overview and analysis of PID control's varied applications in tobacco production, autonomous car technology, and mechanical arm robotics. It focuses on addressing practical challenges and exploring optimization strategies encountered in these fields. This article is summarized using the methods of the review. PID plays a crucial role in enhancing stability, precision, and response speed in these applications, thereby improving overall control system dynamic response performance. This study consolidated existing literature to offer insights into PID's effectiveness across these industrial sectors, shedding light on its impact and potential for further advancements in control technology.

Design and optimization of PD control system based on saturation

Abstract. Designing PI, PD, and PID controllers is challenging, but it is an inevitable step if engineers want their systems to perform well. Consequently, control theory was purposed and the system became more complicated. Unfortunately, saturation is hard to prevent. The degree of saturation is an important aspect of the electrical signal process. This paper investigates the concepts of saturation for PD,PI, and PID control. It involves the discussion of properties of passive components, excessive input/output concerning the design maximum or minimum, and the saturation function. The main purpose of the paper is to give an insight into how to prevent control saturation. The investigation will be mainly based on current control theory, research processes from other papers, and the online simulations. To avoid saturation, applying analysis on the gain of all controllers and saturation function is crucial.

Research on Quadrotor Control Based on Genetic Algorithm and Particle Swarm Optimization for PID Tuning and Fuzzy Control-Based Linear Active Disturbance Rejection Control

The control system of a quadrotor aircraft is characterized by nonlinearity, strong coupling, and underactuation, making it susceptible to external disturbances that can affect flight performance. To address this issue, this paper proposes a novel control system based on inner–outer loop architecture. In this system, the outer loop position control adopts a PID controller optimized by Genetic Algorithm-based Particle Swarm Optimization (GA-PSO), while the inner loop attitude control employs a Linear Active Disturbance Rejection Controller (LADRC) with fuzzy algorithm-based adaptive tuning, forming a dual-loop control structure. Comparisons with traditional dual-loop cascaded PID controllers, conventional PID in the outer loop with LADRC in the inner loop, and conventional PID in the outer loop with fuzzy algorithm-based adaptive tuning in the inner loop demonstrate that the proposed control system can stably track the desired position and attitude angles under certain external disturbances, exhibiting excellent anti-disturbance capability and stability.

Autonomous Cargo Grabbing System for Drones Using Cameras and ROS

Abstract. With the rapid development of low-altitude economy, the functional requirements for logistics UAVs are constantly improving. In this paper, an unmanned aerial vehicle system (UAV) is proposed to realize autonomous grasping of goods. First of all, a new type of grasping device is proposed, which can be easily mounted on the UAV. The camera image stream mounted on the UAV was obtained through camera calibration and corrected. Subsequently, the object recognition algorithm was used to process the image frames, successfully identify the goods to be captured, and calculate the position and direction relationship between the UAV and the target cargo. Next, the speed of the drone is adjusted by the PID controller to achieve a stable landing above the target cargo. Then, the drone is equipped with a robotic claw innovatively designed by our team to complete the gripping of the goods. Subsequently, the drone lifted off and began to deliver cargo. Throughout the grabbing process, a laptop equipped with Ubuntu and ROS systems communicates with the drone via Wi-Fi. The feasibility of the whole system was proved by experiments.

BLE Bluetooth remote-controlled car based on ESP32

Abstract. With the rapid advancement of information technology, Bluetooth technology has been widely used in the field of short-range wireless communication, playing an important role in information exchange. As one of the main technologies for short-range wireless communication, Bluetooth technology has broad application prospects, especially in robotic applications such as remote-controlled cars. This paper provides a detailed introduction to the design and implementation process of a Bluetooth remote-controlled car system based on the ESP32. It covers the classic Bluetooth and Bluetooth Low Energy (BLE) functionalities of the ESP32, the hardware design and assembly of the car (including the DC motor drive module, Bluetooth module, and speed measurement module), and methods for establishing a connection between the ESP32 and Bluetooth module, data transmission, and remote control of the car's movement. The paper also evaluates the system's performance through experiments, discusses the application and parameter adjustment of the PID control algorithm in enhancing speed control, and finally summarizes the research findings and suggests directions for future work.

Improved PID control of DC motor

Abstract. In this paper, the circuit optimization problem of precise control of DC motor through PID interface is studied. The main purpose of this research is to strategically coordinate resistance and capacitance elements in circuit design to explore and improve the speed of signal transmission and the accuracy of response. Additionally, by optimizing the interaction between these components, this paper attempts to achieve improved performance and efficiency in the motor control system, that is, changing different K_p and K_d to achieve the best fit parameters for the most system. The experimental approach employed in this study involves a collaboration between Tinkercad and Falstad software platforms to simulate and analyze the effects of various circuit configurations as well as coefficients. The effects of K_p and K_d on the system are obtained through systematic experiments and analysis. Meanwhile, it aim to verify the proposed optimization technique and evaluate its effectiveness in practical applications. The simulation results of Tinkercad and Falstad agree well with the expected theoretical results.