Traditional Proportional Integral Derivative Controller Research Articles

A Method of Anti-Windup PID Controller for a BLDC-Drive System

This research aims to enhance control systems for Brushless DC (BLDC) motors by introducing Proportional-Integral-Derivative (PID) control as a straightforward yet reliable solution, known for its precision, quick responsiveness, and stability. Emphasizing its suitability for BLDC motor speed control, the study addresses PID controller windup challenges, highlighting anti-windup techniques crucial for maintaining system stability. The primary focus is on improving the performance of an anti-windup PID controller for BLDC motor speed control in electric vehicles. Through simulations and analyses, the research aims to minimize steady-state error and overshooting, contributing to overall operational efficiency. Practical implementation involves optimizing the PID anti-windup controller's gain using the MATLAB PID Tuner and implementing it in the Arduino IDE. Experimental tests, which cover constant and varying step function scenarios, are conducted on the designed hardware. Results show the PID anti-windup controller's superiority, exhibiting significantly lower overshoot values of 5.42% and 3.35% compared to 13.26% and 23.76%, respectively. Despite its higher control action, the traditional PID controller displays a more pronounced overshoot. This research is a significant step toward advancing control systems for electric vehicles and optimizing BLDC motor performance in practical applications.

Research on Temperature Control of Plastic Laser Welding Based on Improved Snake Optimization Algorithm

Aiming at the nonlinear and time-varying characteristics of plastic laser welding temperature control system, and the problems of low accuracy and large overshoot when using the traditional proportional-integral-derivative (PID) controller, an improved snake optimization (ISO) is proposed to optimize the PID parameters. Firstly, the elite reverse learning strategy is introduced into the snake optimization (SO) to generate the reverse solution, and new individuals are generated through comparison to improve the convergence speed of the algorithm; Simultaneously introducing the water wave dynamic adaptive factor to enhance the algorithm’s iterative optimization ability. By iterating the algorithm, a set of suitable PID parameters are obtained to control the temperature control system. The simulation results show that compared to traditional PID controllers, ISO-PID reduces the adjustment time by approximately 0.42 seconds and does not cause overshoot.

Analysis and Controlling of Uncertainty in BLDC Motor Using Optimal Hybrid Algorithm in State Space

In this article, we present a novel approach for enhancing the performance of Brushless DC Motors (BLDC) by utilizing a Hybrid Controller that combines Proportional – Integral – Derivative (PID) and Artificial Neural Network (ANN) elements. We employ this innovative controller to assess the motor’s behavior under steady-state conditions and internal faults within a state space model. To evaluate the controller’s effectiveness, we compare it with traditional controllers in scenarios involving rapid load changes, and variations in speed, as well as open circuit faults and short circuit faults, which are known to induce disruptive states in the motor. Our study involves a comprehensive examination of the proposed controller’s steady-state stability using state-space representation and the Lyapunov Technique. We construct a prototype model to capture the Steady State and dynamic characteristic variables of a 300 W BLDC motor system. The suggested Hybrid PID-ANN Controller clearly outperforms the traditional PID controller, providing greater control capabilities for BLDC motor applications depending on the results from both simulations and data from experiments.

A flight control system for quadrotor plant protection UAV based on RBF-PID algorithm

As a powerful tool for modern agricultural protection, unmanned aerial vehicle (UAV) has the advantages of good adaptability and high efficiency in spraying and other operations compared with traditional manual operations The attitude and control of plant protection UAV are greatly affected by external factors during operation. However, the traditional proportional-integral-derivative (PID) controller commonly used at present cannot adapt to the complex environment because of the fixed parametersTherefore, a more sensitive method is needed to achieve adaptive balance of the aircraft attitude. In this paper, we establish the dynamic mathematical model of quadrotor UAV, and the influence of air resistance and rotational torque is considered in the process. Adopting the combined control method of Radial Basis Function (RBF) neural network and PID, the dynamic tuning of system control parameters is achieved through the self-learning of neural network and the feature of nonlinear mapping. The control system not only has the advantages of simple PID control structure and clear physical meaning, but also has the ability of neural network self-learning and adaptation. Matlab/Simulink was used as the experimental platform to respectively simulate the RBF-PID control system and the traditional PID control system. The simulation results show that the control system based on RBF-PID algorithm has a shorter response time and stronger robustness, which enhances the systems self-adaptability

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.

Optimal intelligent fuzzy TID controller for an uncertain level process with actuator and system faults: Population-based metaheuristic approach

For the Conical Tow-Tank Non-Interacting Level (CTTNL) prototype system, this article discusses the design of an unique optimal fractional order TID controller employing a type-1 fuzzy set. To generate a linearized model transfer function for the nonlinear CTTNL process, local linearization around the equilibrium state was performed. This work compares the performance of a fuzzy tilted-integral-derivative (FTID) controller for the CTTNL system to a traditional proportional–integral–derivative (PID) controller. The parameters of the FTID controller were optimized using the Chicken Search Optimization (CSO) algorithm in this study, and those values were used to create a PID controller. Thereafter, the FTID and PID controllers’ performance is compared. The investigation shows that the FTID controller beats the PID controller in terms of IAE, ISE, ITAE, and ITSE integral errors. The suggested controller’s fault-tolerant performance was also assessed using the fault recovery time (Frt) parameter. The FTID controller outperforms traditional PID controllers in terms of transient and steady-state performance. The fault tolerance capabilities of the FTID controller will be tested on the CTTNL system in the future, using additive and multiplicative failures. Further research will include a comprehensive examination of robustness in the context of model parameter uncertainties.

Advanced optimal GA-PID controller for BLDC motor

The brushless direct current (BLDC) motor is characterized by high torque, which made it widely used in many industrial applications. To improve the working performance of the BLDC motor, the addition of controllers such as proportional integral derivative (PID) is adopted. To obtain high-performance controllers, the design process is adopted to develop a suitable algorithm. The genetic algorithm (GA) was chosen to tune the PID controller and get suitable parameters for each of kp, ki and kd with self-tuning. The design process is based on BLDC motor control using the GA to tune the traditional PID controller. Simulations were carried out for three cases including the absence of controllers secondly, by using the traditional control unit and finally with the GA. The integral time absolute error (ITAE) type error control standard for BLDC motor control system was selected. After conducting the simulation, the results demonstrated the superiority of the GA over the traditional ones in terms of response speed, (stability, rise, and settling) time and percentage overshootas details will be mentioned the model in the subsequent paragraphs of the research, finally the simulation results indicate the development and improvement of BLDC motor operation and performance during real time.

Advanced optimal GA-PID controller for BLDC motor

The brushless direct current (BLDC) motor is characterized by high torque, which made it widely used in many industrial applications. To improve the working performance of the BLDC motor, the addition of controllers such as proportional integral derivative (PID) is adopted. To obtain high-performance controllers, the design process is adopted to develop a suitable algorithm. The genetic algorithm (GA) was chosen to tune the PID controller and get suitable parameters for each of kp, ki and kd with self-tuning. The design process is based on BLDC motor control using the GA to tune the traditional PID controller. Simulations were carried out for three cases including the absence of controllers secondly, by using the traditional control unit and finally with the GA. The integral time absolute error (ITAE) type error control standard for BLDC motor control system was selected. After conducting the simulation, the results demonstrated the superiority of the GA over the traditional ones in terms of response speed, (stability, rise, and settling) time and percentage overshootas details will be mentioned the model in the subsequent paragraphs of the research, finally the simulation results indicate the development and improvement of BLDC motor operation and performance during real time.

Ters Sarkaç Sisteminde Referans Model Kullanarak Kesir Dereceli PID Denetleyici Tasarımı

The proportional Integral Derivative (PID) controller has three basic parameters: Proportional gain (Kp), Integral gain (Ki) and Derivative gain (Kd). In a conventional PID controller, integral and derivative operators are integer order. The researchers proposed a fractional order PID (PIλDµ) controller by using the fractional integral and derivative operators instead of the integer order integral and derivative operators in the traditional PID controller because it improves the control performance. The PIλDµ controller has an additional fractional integrator degree (λ) and fractional derivative degree (µ). In this study, the focus is on the design of a fractional-order PID controller according to a reference model in the time domain. Bode's ideal transfer function was used as the reference model. It is aimed to obtain PIλDµ parameters by minimizing the error between the time domain response of Bode's ideal transfer function model and the output of the system to be controlled by using the optimization method. Genetic Algorithm (GA) optimization was used as the optimization method. The study was carried out as a simulation study on an inverted pendulum system with a single-input multiple-output (SIMO) structure.

An intelligent control method based on artificial neural network for numerical flight simulation of the basic finner projectile with pitching maneuver

In this paper, an intelligent control method applying on numerical virtual flight is proposed. The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a good application prospect. Firstly, a numerical virtual flight simulation model based on overlapping dynamic mesh technology is constructed. In order to verify the accuracy of the dynamic grid technology and the calculation of unsteady flow, a numerical simulation of the basic finner projectile without control is carried out. The simulation results are in good agreement with the experiment data which shows that the algorithm used in this paper can also be used in the design and evaluation of the intelligent controller in the numerical virtual flight simulation. Secondly, combined with the real-time control requirements of aerodynamic, attitude and displacement parameters of the projectile during the flight process, the numerical simulations of the basic finner projectile’s pitch channel are carried out under the traditional PID(Proportional-Integral-Derivative) control strategy and the intelligent PID control strategy respectively. The intelligent PID controller based on BP(Back Propagation) neural network can realize online learning and self-optimization of control parameters according to the acquired real-time flight parameters. Compared with the traditional PID controller, the concerned control variable overshoot, rise time, transition time and steady state error and other performance indicators have been greatly improved, and the higher the learning efficiency or the inertia coefficient, the faster the system, the larger the overshoot, and the smaller the stability error. The intelligent control method applying on numerical virtual flight is capable of solving the complicated unsteady motion and flow with the intelligent PID control strategy and has a strong promotion to engineering application.

Development of an Adaptive Fuzzy Sliding Mode Controller of an Electrohydraulic Actuator Based on a Virtual Prototyping

The EHA (electro hydraulic actuator) has a notable advantage over conventional hydraulic actuators as it uses a closed-loop circuit, reducing the size and volume of oil, and eliminates pressure losses caused by valve orifices. However, accurate control performance of EHA is difficult to achieve using a traditional PID (proportional integral derivative) controller due to the strongly nonlinear, time-varying, and unknown dynamics of the system. Hence this paper seeks to address this problem by proposing a design of an intelligent controller for the EHA. The proposed adaptive fuzzy sliding mode controller (AFSMC) is developed as a hybrid of the adaptive, fuzzy logic, and sliding mode algorithms. To reduce costs and time, a virtual prototype approach is also proposed instead of experimentations to evaluate the performance of the proposed controller. The virtual model of the EHA is built in Amesime software, and then embedded into Matlab/Simulink where the AFSMC is developed and tested to obtain the position responses of the EHA. The results show that the AFSMC is highly successful and more efficient than the traditional PID at controlling the position of the piston accurately.

Speed control of brushless DCmotors using (conventional, heuristic, and intelligent) methods-based PID controllers

One of the most often utilized types of direct current (DC) motors in both the industrial and automotive sectors are brushless DC motors (BLDC). This research presents a comparative analysis on brushless DC motor speed management. A mathematical model of the BLDC motor is developed using MATLAB/Simulink, and its speed is tested using three alternative controller types. The first controller is a traditional proportional integral derivative (PID) controller for BLDC motor speed control. The second controller used the particle swarm optimization (PSO) approach with PID which give the best response for BLDC motor speed. The PID controller in the third method based on neural network also give best reaction on motor speed. Finally, comparison made in speed and torque profiles by using sudden changes in speed and load torque under the three proposed methods. The results show when using first controller the speed rise to 1,526 r.p.m and drop to 1,400 r.p.m at the test conditions. These oscillations will disappear when using the second and third controller.

A millikelvin precision temperature control system designed for a low cost, portable and variable temperature blackbody from 298.15 to 693.15 K

Millikelvin precision portable variable temperature blackbody from 298.15 to 693.15K is very important in the on-site calibration of infrared measuring instruments. The stability of the blackbody temperature directly affects the calibration quality. However, the temperature measurement and control system is the key component to guarantee the stability of the blackbody temperature. In this article, a measurement and control system of the low-cost and portable blackbody was designed and verified. A Pt100 platinum resistance thermometer was employed to measure the temperature of the blackbody radiation source. Based on the round-robin structure and current reversing technology, the precision of the temperature measurement achieved a sub-millikelvin level. To overcome the drawbacks that traditional proportional integral derivative (PID) controller would lead to large overshoot and long adjustment time during the temperature control of the large thermal inertia blackbody, a feedforward and segmented PID controller was introduced to improve the dynamic performance of the blackbody radiation source. The experimental results showed that the precision of the temperature measurement at 0.5Hz was better than 0.5 mK and the temperature stability within 10minwas better than 3 mK in the temperature range from 298.15 to 693.15K. Hence, the millikelvin precision measurement and control system has a strong prospect for practical application in high-performance blackbody development.

High-Speed Temperature Control Method for MEMS Thermal Gravimetric Analyzer Based on Dual Fuzzy PID Control.

The traditional thermal gravimetric analyzer (TGA) has a noticeable thermal lag effect, which restricts the heating rate, while the micro-electro-mechanical system thermal gravimetric analyzer (MEMS TGA) utilizes a resonant cantilever beam structure with high mass sensitivity, on-chip heating, and a small heating area, resulting in no thermal lag effect and a fast heating rate. To achieve high-speed temperature control for MEMS TGA, this study proposes a dual fuzzy proportional-integral-derivative (PID) control method. The fuzzy control adjusts the PID parameters in real-time to minimize overshoot while effectively addressing system nonlinearities. Simulation and actual testing results indicate that this temperature control method has a faster response speed and less overshoot compared to traditional PID control, significantly improving the heating performance of MEMS TGA.

Performance Analysis and Implementation of Brushless DC Motor by PID, Fuzzy and ANIFS Controllers

Now a day’s Brushless DC (BLDC) motors are more popular because of its good electrical and mechanical property. These motors are now changing to brushed DC motors and induction motors in various applications. The main problem that occurs in the traditional PID controller is that the parameters obtained from the drive control system of the BLDC servo motor cannot produce a steady state under various operating conditions such as adjusted gain plus transient response and parameter variation, load disturbance. So a fuzzy controller provides good speed response for start-up. In this paper how the design and simulation of ANFIS controller is better for the performance of brushless DC motor (BLDC) servomotor as well as the design and implementation of ANFIS controller and its performance are compared is shown. The capability of BLDC servomotor based on Fuzzy, ANFIS and PID controller is investigated under different operating conditions. Paper represents the PID controller and fuzzy controller to demonstrate error tracking capability and usefulness of ANFIS controller in control applications. By using the PID, Fuzzy and ANFIS controller we can implement, analyze the overall performance of brushless DC motor. KEYWORDS: Proportional-Integral-Derivative (PID) controller, Brushless DC (BLDC) motor, Adaptive-Network- Based Fuzzy Inference System (ANFIS), (FLC) Fuzzy logic controller (IGBT) Insulated Gate Bipolar Transistor.

Simulated Scramjet Shock Train Control Using an All-Coefficient Adaptive Control Approach

This paper explores the use of dual-mode scramjet shock train control taking an adaptive control approach known as characteristic model-based all-coefficient adaptive control. In this study a simple, semiempirical model of a dual-mode scramjet is developed. This model is suited to computationally efficient control simulation for the assessment of different control approaches. All-coefficient adaptive control is predicted to be as capable of controlling the shock train location, and rejecting disturbances, as a traditional proportional–integral–derivative controller. However, prior knowledge of the scramjet plant is not required for the all-coefficient adaptive control, and it is predicted that this controller is able to better perform when changes in the plant take place. When characteristics of the plant deviate from their nominal values, the adaptive controller can adjust in real time and maintain a stable system response. This is the first time the performance of all-coefficient adaptive control has been examined in the open literature for a dual-mode scramjet. It is also the first time all-coefficient adaptive control of a dual-mode scramjet has been compared to proportional–integral–derivative control. The simple, empirically based plant model of the dual-mode scramjet is also new, and it is well suited to real-time and hardware-in-the-loop control simulations.

Adaptive backstepping control for permanent magnet linear motors against uncertainties and disturbances

In practical operation, the system parameters of a permanent magnet linear motor are affected by unknown factors, including nonlinear friction, sudden load changes, thrust fluctuations, and so on. Therefore, a controller designed for fixed parameters often cannot produce satisfactory results. To solve this problem, a novel design method for permanent magnet linear motor systems based on backstepping is proposed in this article. The proposed control scheme does not require the values or range of changes of the system parameters in advance but constructs an update law to perform online parameter estimation. A difficulty encountered in controller design and stability analysis is the estimation of the unknown coefficient of a single-variable state, which can be considered as a virtual input. In this article, the estimated virtual control coefficient is introduced into the coordinate transformation to construct a novel coordinate transformation. However, introducing the estimated coefficient into the virtual control makes the derivative of the Lyapunov function used in the stability analysis more complex. This novel nonlinear dynamic term can be precisely cancelled by changing the update law of the coefficient. Finally, simulations are performed to assess the performance of the proposed control scheme against that of a traditional proportional–integral–derivative controller, and the simulation results show that the proposed control law can effectively improve the system performance.

High-bandwidth image-based predictive laser stabilization via optimized Fourier filters.

Controlling the delivery of kHz-class pulsed lasers is of interest in a variety of industrial and scientific applications, from next-generation laser-plasma acceleration to laser-based x-ray emission and high-precision manufacturing. The transverse position of the laser pulse train on the application target is often subject to fluctuations by external drivers (e.g.,room cooling and heating systems, motorized optics stages and mounts, vacuum systems, chillers, and/or ground vibrations). For typical situations where the disturbance spectrum exhibits discrete peaks on top of a broad-bandwidth lower-frequency background, traditional PID (proportional-integral-derivative) controllers may struggle, since as a general rule PID controllers can be used to suppress vibrations up to only about 5%-10% of the sampling frequency. Here, a predictive feed-forward algorithm is presented that significantly enhances the stabilization bandwidth in such laser systems (up to the Nyquist limit at half the sampling frequency) by online identification and filtering of one or a few discrete frequencies using optimized Fourier filters. Furthermore, the system architecture demonstrated here uses off-the-shelf CMOS cameras and piezo-electric actuated mirrors connected to a standard PC to process the alignment images and implement the algorithm. To avoid high-end, high-cost components, a machine-learning-based model of the piezo mirror's dynamics was integrated into the system, which enables high-precision positioning by compensating for hysteresis and other hardware-induced effects. A successful demonstration of the method was performed on a 1 kHz laser pulse train, where externally-induced vibrations of up to 400 Hz were attenuated by a factor of five, far exceeding what could be done with a standard PID scheme.

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.

Research on robotic end positioning based on fuzzy PID control

The existing traditional proportional-integral-derivative (PID) control robot has low-end positioning accuracy, which cannot meet the needs of accurately placing the molding die in the current automatic coal pen production process. To address this problem, this paper proposes a fuzzy strategy PID control method based on fuzzy strategy, aiming to improve the accuracy and robustness of the system when the manipulator grasps the mounding mold. Firstly, a manipulator motor control model is established to derive the parameters of the manipulator control system; secondly, the fuzzy control strategy is applied to adjust the PID control parameters in real time to improve the dynamic stability of the system. Finally, the control system is simulated, and field experiments are conducted to verify the accuracy of robotic end positioning. The experimental results show that the fuzzy PID control system has a shorter response time, better anti-interference ability, and better stability than the traditional PID control and achieves the accuracy requirements of coal pen automation production.