Proportional-integral-derivative Tuning Research Articles

Pole placement tuning of proportional integral derivative feedback controller for knee extension model

Functional electrical stimulation (FES) has shown potential in rehabilitative exercises for patients recovering from spinal cord injuries. In recent developments, conventional open-loop FES control techniques have evolved into closed-loop systems that employ feedback controllers for automation. However, closed-loop FES systems often face challenges due to muscle non-linear effects, such as fatigue, time delays, stiffness, and spasticity. Therefore, an accurate non-linear knee model is required during the design stage, and precise tuning of the feedback controller parameters is vital. A proportional– integral–derivative (PID) controller is commonly used as a feedback controller due to its simplicity and ease of implementation. However, most PID tuning methods are complex and time consuming. This paper investigates the viability of employing the pole placement technique for tuning a PID controller that regulates the non-linear knee extension model. The pole placement method aims to improve the control and adaptability of the PID controller in closed-loop FES systems, specifically by facilitating knee extension exercises. MATLAB Simulink was used to assess the effectiveness of this tuning approach. Results showed that the PID controller performed satisfactorily without non-linearities, but performance varied with the inclusion of specific non-linearities. The pole placement tuning method facilitated preliminary assessments of PID controller performance, preceding highly advanced optimization.

Deep reinforcement learning for PID parameter tuning in greenhouse HVAC system energy Optimization: A TRNSYS-Python cosimulation approach

The control of indoor temperature in greenhouses is crucial as it directly impacts the crop's thermal comfort and the performance of heating, ventilation, and air-conditioning (HVAC) systems. Conventional feedback controllers, like on/off, can sometimes make HVAC system work at full capacity when only half that capacity is needed. In contrast, the proportional-integral-derivative (PID) controller, provides precise control based on its P, I, and D parameters. However, it lacks a formal design procedure for optimizing a specified objective function. Previous studies have utilized conventional PID tuning approaches to track room setpoint temperature for residential buildings, data centers, and office buildings, with limited research in greenhouse applications. To address this gap, this study proposes a flexible PID controller that employs a deep reinforcement learning (DRL) algorithm to optimize its parameters, by tracking the setpoints and energy consumption of a greenhouse planted with tomatoes. This approach is different from the typical method of using the trained RL agent directly in HVAC controls. Through a self-made TRNSYS-Python cosimulation framework, the DRL agent interacts directly and in real time with the greenhouse and its plants. Consequently, optimized PID parameters were established and tested in the simulated environment. The resulting performance, in terms of both energy consumption and its ability to maintain the crop’s comfort temperature, was compared with the simulated on/off and manually tuned PID controllers. Compared to the on/off baseline control, the proposed PID optimized parameters reduce energy use by 8.81% to 12.99% and the manually tuned PID parameters with the Ziegler-Nichols tuning method reduce energy use by 7.17 %. Additionally, the proposed method had a deviation of 2.07% to 3.13%, while the manually tuned PID controller and the on/off controller had deviations of 7.27% and 3.27%, respectively, from the minimum comfortable temperature. This study serves as a framework for improving the energy efficiency of greenhouse HVAC system operations.

Synergistic Integration of Advanced Control Strategies for Comprehensive PID Controller Tuning using MATLAB

This study investigates the impact of Proportional-Integral-Derivative (PID) tuning on system performance through comprehensive measurements before and after tuning. The parameters analyzed include rise-time, settling-time, overshoot, undershoot, and peak time. Prior to tuning, the system exhibited significant deficiencies, characterized by prolonged rise and settling times, substantial overshoot and undershoot, and extended peak time. Following PID tuning, notable improvements were observed across all parameters. The rise time and settling time were dramatically reduced, indicating a faster system response and improved stability. Moreover, overshoot and undershoot were effectively minimized, leading to greater accuracy and precision in the system's output. These findings underscore the efficacy of PID tuning in optimizing system performance and achieving desired control objectives.

Pyrolysis process control: temperature control design and application for optimum process operation

Fast pyrolysis in auger reactor gains attention for efficient bio-oil production. Due to the quick nature of the process, precise temperature control using the proportional-integral-derivative (PID) algorithm is paramount. This study harnesses various PID tuning approaches through modelling and experimental validation to optimize continuous and precise pyrolysis temperature. System identification was done to investigate the process dynamic with fit accuracy above 93% and design a suitable PID control. Comparison with the experiment data shows a favorable result with rise time and settling time match above 75%. Ziegler-Nichols (ZN) and Cohen-Coon (CC) tuning methods were implemented in the system with undistinguished results, yielding steady-state error (SSE) below 1% and settling time around 4,300 to 4,800 seconds. The heuristic fine-tuning method improved the rise time and settling time by stabilizing before 3,600 seconds. Furthermore, the robustness of PID controllers was verified with a disturbance rejection test, keeping the SSE deviation inside the boundary of 2%. Finally, the setup could support maximum pyrolytic oil production by 69.6% at 500 °C. The result implies that the PID controller could provide a stable and rugged response to support a productive and sustainable pyrolysis plant operation.

Improving precision and robustness in level control of coupled tank systems: A tree seed optimization and [formula omitted]-analysis approach

Background:Efficient control of liquid levels in interconnected tank systems is a fundamental challenge in various industries, including chemical processes, wastewater treatment, and manufacturing. The traditional approach to achieving precise control has relied on Proportional–Integral–Derivative (PID) controllers, whose performance largely depends on tuned parameters. However, tuning PID controllers for complex and nonlinear systems like coupled tanks remains a challenging task. In recent years, nature-inspired optimization algorithms have gained prominence in control system design. The tree seed optimization (TSO), inspired by the dispersion of tree seeds in search of optimal growth conditions, has shown promise in solving complex optimization problems. This study seeks to explore the application of TSO in tuning PID controllers for coupled tank systems. Methodology:This research employs the TSO to optimize PID controller parameters for enhanced liquid-level control in coupled tank systems. The optimization process involves integrating performance metrics and closed-loop gain constraints to achieve optimal control of coupled tank systems. The imposed constraints aim to ensure robustness and system stability in the face of uncertainties and disturbances. Besides, μ analysis quantifies the robustness of the system by assessing its ability to tolerate uncertainties. Findings:Simulation studies conducted in this research verify the efficacy of the proposed TSO-based approach for tuning PID controllers in coupled tank systems. Compared with various methods, the TSO consistently yields better control performance, reduces settling time, and minimizes overshoot. The robustness of the optimized controllers is also evaluated, showing the ability to handle varying operating conditions effectively.

Developing and Simulating a Model using Proportional Integral Derivative (PID) on Matlab/Simulink for Speed Control of DC Motor

This study uses MATLAB/SIMULINK to perform a thorough inquiry of the use of Proportional Integral Derivative (PID) control for accurate speed control of a DC motor. To increase steady-state error, decrease settling time, and improve transient response, the suggested model uses sophisticated PID tuning techniques. The efficacy of the PID controller in preserving the intended speed setpoints is demonstrated by means of comprehensive simulation experiments that assess its performance in a range of operational scenarios. This work creates a useful foundation for DC motor speed control system optimization in addition to offering insightful information on PID-based control techniques.

Two Feedback PID Controllers Tuned with Teaching–Learning-Based Optimization Algorithm for Ball and Beam System

The proportional integral derivative (PID) controller continues to be the most popular and widely used in the industry despite the development of various artificial intelligence-based controllers due to its simplicity and ease of use. Setting PID parameters, especially in non-linear systems, is still a significant problem. This study presents a cascade PID tuning technique based on the teaching–learning-based optimization (TLBO) algorithm. The proposed method is tested for the position control of a non-linear ball and beam system. A comparative analysis of the proposed method is done with the conventional tuning method and particle swarm optimization-tuned cascade PID controller. The optimization was carried out using integral time absolute error, integral time error, and integral square error as objective functions. It was observed that the evolutionary algorithm-based controller tuned by TLBO gives a much better response regarding rise time, settling time, and overshoot. To test the effectiveness and validity of the proposed controller robust analysis of the proposed controller is carried out with a step disturbance applied at t = 3 s. The comparative study proves that TLBO-tuned response is better than other controllers. Sensitivity analysis of the proposed controller is also performed by varying system parameters.

Control & synchronization of a unified chaotic system using an adaptive controller with an extended Kalman–Bucy-filter based Auto-Tuner

A current challenge with adaptive controllers is to define efficient tuning methods of the controller parameters. Unlike linear systems, nonlinear systems may need parameters that are continuously tuned at different operating points to provide stability and desired behaviours. This study aims to develop a solution for tuning proportional–integral–derivative (PID) controller parameters as opposed to changing the operating points of a nonlinear system. Most tuning methods calculate parameters according to the system’s step or frequency response. However, adaptive controllers have self-tuneable parameters or control rules. The proposed algorithm in this paper contains a controller, an estimator, and a reference model, and uses the system model. Unlike the model reference adaptive control method, the proposed controller has tuneable controller parameters estimated by the extended Kalman–Bucy filter. The filter estimates the controller parameters to make the system perform like the auxiliary ideal reference model to ensure minimum-time consumption. Hence, this study aims to develop an algorithm that will automatically calculate controller parameters for each operating point of the controlled chaotic or nonlinear system to minimize settling time at each operating point. The proposed algorithm is implemented in a unified chaotic system in which the estimator and controller of the system run together. Simulation results confirm the performance of the proposed algorithm. In addition, the simulation results provide strong evidence that the proposed algorithm can be an effective tool for controlling nonlinear or chaotic systems.

Optimal tuning of PID controllers with derivative filter for stable processes using three points from the step response

This paper proposes a novel approach for tuning Proportional Integral Derivative (PID) controllers, utilizing experimental data obtained from an open-loop step input. We propose to use the measurements of the times taken to reach 5%, 35.3% and 85.3% of the final output, as well as the process static gain, to tune the controller. The tuning equations are applicable for a wide range of stable and over damped systems. Starting from an approximate model with three real poles and time delay (obtained from the measurements), the tuning equations approximate the controller that minimizes the Integral of Absolute Error (IAE) of the disturbance response. The designer can freely decide the Sensitivity Margin (Ms) to fix a desired robustness, as a difference with respect to other known methods where the user chooses among few predefined robustness values. The user can also select freely the derivative filter parameter, N, to find the required compromise between speed response and measurement noise amplification. For N=0 a PI controller is chosen. As N increases, a faster PID controller is selected, with higher noise amplification. We have also developed a web-based application and an Android application, downloadable for free, that implement the tuning equations as well as the whole required procedure.

Genetic Algorithm based PID Tuning Software Design and Implementation for a DC Motor Control System

This study presents the software and implementation for proportional-integral-derivative (PID) tuning of a DC motor control system using genetic algorithm (GA). The PID parameters for a specific control structure are optimized using GA in the proposed tuning procedure. Also, integral time absolute error (ITAE) is used as a fitness function to optimize the parameters. The robustness of the control system is compared with conventional mathematical method. Simulations are carried out in MATLAB/Simulink to compare the results of a DC motor control system. Simulation results show that in terms of overshoot, steady-state error, and settling time, GA-based PID tuning approach performed better than conventional method. Additionally, a sensitivity analysis is performed to evaluate how robust the proposed approach is to parameter variations. The analysis shows that compared to the conventional method, the GA-based PID tuning algorithm is more adaptable to variations in system parameters.

Multi-Phase Focused PID Adaptive Tuning with Reinforcement Learning

The Proportional-Integral-Derivative (PID) controller, a fundamental element in industrial control systems, plays a pivotal role in regulating an extensive array of controlled objects. Accurate and rapid adaptive tuning of PID controllers holds significant practical value in fields such as mechatronics, robotics, and automatic control. The three parameters of the PID controller exert a substantial influence on control performance, rendering the tuning of these parameters an area of significant interest within related research fields. Numerous tuning techniques are widely employed to optimize its functionality. Nonetheless, their adaptability and control stability may be constrained in situations where prior knowledge is inadequate. In this paper, a multi-phase focused PID adaptive tuning method is introduced, leveraging the deep deterministic policy gradient (DDPG) algorithm to automatically establish reference values for PID tuning. This method constrains agent actions in multiple phases based on the reward thresholds, allowing the output PID parameters to focus within the stable region, which provides enhanced adaptability and maintains the stability of the PID controller even with limited prior knowledge. To counteract the potential issue of a vanishing gradient following action constraints, a residual structure is incorporated into the actor network. The results of experiments conducted on both first-order and second-order systems demonstrate that the proposed method can reduce the tracking error of a PID controller by 16–30% compared with the baseline methods without a loss in stability.

HYBRID PARTICLE SWARM OPTIMIZATION AND GREY WOLF OPTIMIZER FOR SETTING PID PARAMETERS OF BLDC MOTORS

BLDC (Brushless DC) motors have advantages over asynchronous motors and dc motors in various aspects. Particularly in electric bicycles and flying cars, BLDC motors are utilized widely. Electric bicycles and flying cars are becoming increasingly popular, and as a result, the significance of BLDC motors and their cost-effective and efficient utilization has been growing rapidly. PID (Proportional Integral Derivative) controllers are generally used in motor control because they are cheap and perform well. Many methods have been used to adjust PID parameters. Although methods such as Ziegler-Nichols, Cohen-Coon etc. are widely used, there are also new methods such as optimization algorithms PSO (Particle Swarm Optimization), Whale Optimization Technique, Gray Wolf Optimization technique etc. The hybrid method: HPSOGWO (Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer) is a combination of PSO and GWO (Grey Wolf Optimizer) techniques, and it can be used for tuning PID parameters. As associated with this, the aim of this study is to show the superiority of HPSOGWO algorithm in optimizing the PID parameters. In the content of this study, the essentials regarding the optimization background, and details of the BLDC motor modeling was explained first. After that, the methodology of the hybrid solution was expressed and then the application phase was explained in detail, by including the results generally. In the context of the intelligent optimization approach of this study, the results were obtained in the MATLAB Simulink environment. The application of the used solution method revealed its superiority over the study conducted solely with GWO in various parameters.

Entropy-maximizing TD3-based reinforcement learning for adaptive PID control of dynamical systems

The proper tuning of proportional–integral–derivative (PID) control is critical for satisfactory control performance. However, existing tuning methods are often time-consuming and require system models that are difficult to obtain for complex processes. To this end, automatic PID tuning, particularly that based on deep reinforcement learning, eliminates the necessity of a system model by treating the PID tuning as a black-box optimization. However, these methods suffer from low sample efficiency. In this paper, we present an entropy-maximizing twin-delayed deep deterministic policy gradient (EMTD3) method for automatic PID tuning. In our method, an entropy-maximizing stochastic actor is deployed at the beginning to ensure sufficient explorations, followed by a deterministic actor to focus on local exploitation. Such a hybrid approach can enhance the sample efficiency to facilitate the PID tuning. Extensive simulation studies are provided to show the superior performance of the proposed method relative to other methods on data efficiency, adaptivity, and robustness.

Implementation of Automatic DC Motor Braking PID Control System on (Disc Brakes)

The vital role of an automated braking system in ensuring the safety of motorized vehicles and their passengers cannot be overstated. It simplifies the braking process during driving, enhancing control and reducing the chances of accidents. This study is centered on the design of an automatic braking device for DC motors utilizing disc brakes. The instrument employed in this study was designed to accelerate the vehicle in two primary scenarios - before the collision with an obstacle and upon crossing the safety threshold. It achieves this by implementing the Proportional Integral Derivative (PID) control method. A significant part of this system comprises ultrasonic sensors, used for detecting the distance to obstructions, and rotary encoder sensors, which are utilized to measure the motor's rotational speed. These distance and speed readings serve as essential reference points for the braking process. The system is engineered to initiate braking when the distance value equals or falls below 60cm or when the speed surpasses 8000rpm. During such events, the disc brake is activated to reduce the motor's rotary motion. The suppression of the disc brake lever is executed pneumatically, informed by the sensor readings. Applying the PID method to the automatic braking system improved braking outcomes compared to a system without the PID method. This was proven by more effective braking results when the sensors detected specific distance and speed values. Numerous PID tuning tests achieved optimal results with K_p = 5, K_i = 1, and K_d = 3. These values can be integrated into automatic braking systems for improved performance. The PID method yielded more responsive braking outcomes when applied in distance testing. On the contrary, the braking results were largely unchanged in the absence of PID. Regarding speed testing, the PID method significantly improved the slowing down of the motor speed when it exceeded the maximum speed limit of 8000 rpm. This eliminates the possibility of sudden braking, thus maintaining the system within a safe threshold. The average time taken by the system to apply braking was 01.09 seconds, an indication of its quick responsiveness. This research is a valuable addition to control science, applying the PID control method to automatic DC motor braking. It provides valuable insights and concrete applications of PID control to complex mechatronic systems. It is also noteworthy for its development and optimization of suitable PID parameters to achieve responsive and stable braking. The study, therefore, offers a profound understanding of how PID control can be employed to manage braking systems on automatic DC motors, thereby advancing knowledge and application of control in control science and mechatronics.

Indirect response-based tuning of PID controller with adjustable robustness

The response-based design approach is a straightforward method for proportional–integral–derivative (PID) controller tuning. In this method, an open-loop process response is used to tune the PID controller for fixed robustness. In this article, new response-based PID tuning rules are proposed with adjustable robustness and response. Instead of three PID variables, the proposed PID tuning method allows the user to adjust the process response with a single variable [Formula: see text]. The range of constant [Formula: see text] is provided for ensuring the stability of the process. Three MATLAB/SIMULINK examples and one temperature controller experiment are used to validate the proposed controller performance. The simulation result is also compared with three well-known response-based PID tuning rules Z-N, Cohen-Coon, Astrom et al., and with a model-based PID tuning proposed by Wang et al. Moreover, the proposed tuning provides lesser IAE, ITAE, ISE, and ITSE values than the existing methods.

Robust PID controllers tuning based on the beetle antennae search algorithm

The core components of both traditional and contemporary control systems are the proportional–integral–derivative (PID) control systems, which have established themselves as standards for technical and industrial applications. Therefore, the tuning of the PID controllers is of high importance. Utilizing optimization algorithms to reduce the mean square error of the controller’s output is one approach of tuning PID controllers. In this paper, an appropriately modified metaheuristic optimization algorithm dubbed beetle antennae search (BAS) is employed for robust tuning of PID controllers. The findings of three simulated experiments on stabilizing feedback control systems show that BAS produces comparable or higher performance than three other well-known optimization algorithms while only consuming a tenth of their time.

Pendeteksi Persentase Kadar Alkohol Dengan Kontrol PID Berbasis IOT

In order to find out the alcohol content in drinks circulating in the community, measurements and tests are usually carried out in the laboratory. This takes quite a long time. To solve this problem, we need a tool that can measure alcohol quickly and accurately. This research aims to design and manufacture a detector for the percentage of alcohol content, especially in drinks, using an IoT (Internet of Thing)-based MQ3 sensor as a monitoring system that is connected directly to a smartphone. The method used in this research is PID (Proportional Integral Derivative). By setting the PID tuning with a value of kp=4, ki=3, kd=0.0001 and a setpoint of 210 is used to produce an optimal reading of the alcohol content in the drink. Readings using PID control have an average error value of 1.26%, otherwise without using PID control, the average error value is 8.12%. In order to see the alcohol content of the experimental results of the type of drink detected, it will be displayed on the blynk application on the smartphone.

Otomatisasi Pengontrol Temperatur Suhu Untuk Mesin Pengering Pelet Menggunakan Kontroler Autonics TK4S Dengan Metode PID

A pellet dryer machine is a tool that makes it easy to dry pellets so it doesn't have to require sunlight to dry the pellets. Various methods have also been widely used to control the temperature on the machine. One of the obstacles experienced is controlling the temperature so that it remains stable and has a good response time. In this journal, a pellet drying machine with PID (proportional integral derivative) method will be designed to keep the temperature stable. The temperature that makes the setpoint is 100°C. In the design carried out, it is by comparing sistems that are given Proportional control, Proportional and Integral (PI) control and Proportional Derivative (PD) control. For the controller used, it is an Autonics TK4S type controller. In the control, PID tuning will be carried out which will later be carried out variations of each Kp, Ki, and Kd values.

Low‐gain internal model control PID controller design based on second‐order filter

AbstractA design method is proposed for low‐gain internal model control (IMC) proportional‐integral‐derivative (PID) controllers based on the second‐order filter. The PID parameters are obtained by approximating the feedback form of the IMC controller with a Maclaurin series, in which the second‐order filter is applied using the IMC approach to achieve a low‐gain PID controller that is suitable for model mismatch problems. Analytical PID tuning rules based on the second‐order filter are derived for several common‐use process models. The second‐order filter is designed from the desired time domain performances of maximum overshoot and settling time. Furthermore, the robustness of the IMC PID controller based on the second‐order filter is analyzed, and results show that its robustness performance is better than the first‐order filter under certain conditions. Finally, three categories of models divided by the ration of time constant and time delay are presented in the comparative numerical simulations to validate the effectiveness and generality of the proposed PID controller design method.

Robust feedback compensation and PID Tuning under actuator rate limit effect based on Bode’s integrals

The actuator rate limit is a common phenomenon in control systems while its underlying mechanism remains elusive. Magnitude reduction and phase delay generated by the rate limiter degrade the control performance, but this adverse effect has always been omitted in practical engineering. This study proposes a robust controller design method and controller update rules for general minimum-phase systems to compensate for the rate limit effect and improve control performance. Firstly, a simplified implementation method of “flat phase” constraint is carried out based on the Bode’s integrals for proportional–integral–derivative (PID) and fractional-order proportional–integral (FOPI) controllers. Secondly, the update rules of the controller design specifications are proposed based on Bode’s integrals to mitigate the actuator rate limit effect. The proposed method maintains the original controller structure without extra compensation filters, thereby can be easily applied to engineering practice. The outperformance of FOPI controllers compared to PID controllers is qualitatively analyzed and has been indicated to have lower probability of infinity loops under actuator rate limit. Illustrative examples and experimental results have demonstrated the effectiveness, robustness under gain variation and disturbance rejection capability of the proposed rate limit compensation strategies.