Adaptive PID Controller Research Articles

Simulation and analysis of the electro-hydraulic coordinated system for the disc brake system in mining belt conveyor

The present study aims to investigate the dynamic performance of the disk hydraulic brake system in a mining belt conveyor. To achieve this, a simulation model of the hydraulic system for the belt conveyor is designed and constructed using AMESim. It is recognized that brake disk deformation can lead to brake shock, thus an ADAMS rigid-flexible coupling model for hydraulic disk brakes is established. Furthermore, the concept of rigid-flexible coupling mechanical systems is introduced into the hydraulic system to enable interaction between physical quantities. Use fuzzy logic rules to adjust parameters in Simulink, and in response to the suboptimal control of braking and acceleration, a compact non-model-based adaptive algorithm is incorporated into the conventional PID control algorithm. Employing mechanical modeling and multidisciplinary dynamic collaborative simulation technology, a simulation system integrating electromechanical and hydraulic aspects is developed for the disk hydraulic brake of belt conveyors. This system is employed to conduct simulation studies on the closed-loop control of braking disk speed and deceleration/acceleration for belt conveyors. The study findings reveal that the non-model-based adaptive PID control algorithm, in comparison to fuzzy PID, not only demonstrates quicker and more stable responses across various dynamic performance curves but also markedly reduces the axial oscillation amplitude of the braking disk under braking conditions.

Automatic Generation Control in Renewables-Integrated Multi-Area Power Systems: A Comparative Control Analysis

Electrical load dynamics result in system instability if not met with adequate power generation. Therefore, monitoring and control plans are necessary to avoid potential consequences. Tie-line-bias control has facilitated power exchange between interconnected areas to cope with load dynamics. However, this approach presents a challenge, as load variation in either area leads to frequency deviations and power irregularities in each of the interconnected areas, which is undesirable. The load frequency control loop method is used to address this issue, which utilizes area control errors. This study focuses on the control of inter-area oscillations in a six-area power system under the effect of renewable energy sources. It evaluates the area control errors in response to changes in load and the penetration of renewable energy into the system. To mitigate these errors efficiently, an adaptive-PID controller is proposed, and its results are compared with PI and PID controllers optimized with heuristic and meta-heuristic algorithms. The findings demonstrate the superiority of the proposed controller over traditional controllers in mitigating tie-line power errors and frequency deviations in each area of the interconnected power system, thus helping to mitigate inter-area oscillations and restore system stability.

Adaptive PID Control for Hydraulic Turbine Regulation Systems Based on INGWO and BPNN

Adaptive PID Control for Hydraulic Turbine Regulation Systems Based on INGWO and BPNN

Enhancement of the Blood Glucose Level for Diabetic Patients Based on an Adaptive Auto-Tuned PID Controller via Meta-Heuristic Methods

Enhancement of the Blood Glucose Level for Diabetic Patients Based on an Adaptive Auto-Tuned PID Controller via Meta-Heuristic Methods

A novel adaptive PID controller with new seesaw algorithms using alternative derivatives

ABSTRACT The PID controller is present in at least 90% of industrial applications due to its simplicity and robustness to control linear and non-linear systems. Its main drawbacks include difficulty in achieving proper gains tuning for complex systems and lack of adaptability attributed to its fixed gains when system dynamics change. Despite the emergence of promising controllers like Neural Networks, Fuzzy Logic, Sliding Modes, or Genetic Algorithms, diverse research efforts focus on using these to improve the PID rather than replacing it, aiming for either ideal fixed gains or adaptive gains. Nevertheless, while some require system models, training data, rules establishment, extensive iterations, and demanding computational resources, the PID and the novel APID controller do not. The new seesaw algorithms propose alternative behaviours based on the current error and its derivative, which are dynamic parameters that change over time. These behaviours can be inserted into the PID to imbue it with adaptive characteristics, achieving adaptive PID ( SeesawAPID ) controllers with faster signal rise and stabilization times, reduction of the maximum peak, and less error accumulation compared to conventional PID .

High-performance Smart Home System based on Optimization Algorithm

Background: With the recent COVID-19 pandemic, people have become increasingly concerned about their physical health. Therefore, the ability to monitor changes in the surrounding environment in real-time and automatically improve the environment has become a current hot topic to improve the overall health level. Objective: This article describes the design of a high-performance intelligent home system that can simultaneously perform monitoring and automatic adjustment functions. Methods: The ESP8266 was used as the core controller, and the DHT11 and G12-04 sensors were used to collect data, such as temperature, humidity, and ambient light intensity. The sampling frequency was increased and the sampled data were processed to improve data accuracy. The sampled data were wirelessly transmitted to a PC or mobile terminal for real-time display. When the sampled data underwent sudden changes, an alert message was sent via the mobile terminal. Based on the real-time changes in ambient light, an improved lighting brightness adjustment algorithm combining bang-bang and single neuron adaptive PID control was used to adjust the lighting brightness. Results: After testing the system designed in this paper and analyzing the errors compared to standard values, the temperature measurement error ranged from 0% to 0.01107%, and the humidity measurement error ranged from 0% to 0.03797%. The improved algorithm was simulated and tested using MATLAB software and compared with traditional PID algorithms and single-neuron adaptive PID algorithms. The improved algorithm did not overshoot during adjustment, and the system reached a steady state much faster than traditional algorithms. Conclusion: The system showed good performance in real-time, stability, and accuracy, fully demonstrating the effectiveness of the devices and algorithms used in the system. This provides ideas for the design and improvement of future smart homes.

An adaptive PID controller for path following of autonomous underwater vehicle based on Soft Actor–Critic

In recent years, autonomous underwater vehicles (AUVs) have witnessed rapid development, and its motion control has garnered increasing attention. Meanwhile, in industries, PID controllers are still wildly employed by most AUVs due to their simplicity, ease of deployment, and a certain level of robustness. However, they are facing significant challenges in parameter tuning, especially when dealing with various control missions and changing external environments. Deep reinforcement learning, as a data-driven approach, has gradually made its impact in AUV control. However, its lack of interpretability has hindered its deployment in relevant experiments. To address these issues, this paper proposed an adaptive PID controller for path following of AUVs based on the Soft Actor–Critic (SAC). This controller combines the interpretability of PID with the intelligence of reinforcement learning. A simulation platform was established and compared with other typical control methods, demonstrating the superiority of the proposed controller. Finally, the feasibility of the proposed SAC-PID controller was validated by lake trials. The results showed that the SAC-PID controller significantly outperformed the PID and Proximal Policy Optimization (PPO) PID controllers in multiple dimensions, such as control precision and convergence speed.

A design an optimized fuzzy adaptive proportional-integral-derivative controller for anti-lock braking systems

This paper introduces a new control theory for optimizing anti-lock braking systems (ABS) in automotive applications. Anti-lock systems play a critical role in ensuring vehicle safety during braking by preventing wheel lock-up. However, conventional ABS control algorithms often struggle to adapt to changing road conditions and vehicle dynamics. The error tuning mechanism was not perfect because the occurrence of error is unique based on each application. In response to this challenge, we propose a new approach that combines fuzzy logic and adaptive proportional-integral-derivative (PID) control. The controller more robust to noise and uncertainties in the system. The present research work has concentrated on designing a novel Wavelet Fuzzy adaptive-hybrid Lion-strawberry Proportional-Integral-Derivative (WFA-HLSPID) for the ABS to maximize the control performance while it applied in the vehicle. Simulation results and comparative analysis demonstrate the superior performance of the proposed control theory when compared to traditional ABS systems. The optimized fuzzy adaptive PID control not only enhances vehicle stability during braking but also improves stopping distance. In all cases, the stopping distance, slip rate, and friction have been validated using MATLAB/Simulink.

An Implementation of Fuzzy to Minimize Electrical Losses in Induction Machine Vector Control

An induction motor is a motor that is difficult to control so it requires synchronous speed due to dynamic load changes which are controlled by the torque current while the field current is kept constant. Thus, in this paper, we implement an adaptive PID controller method with an induction motor using MATLAB which coordinates the speed of two decoupled loads with a three-phase induction motor, where an adaptive Self Tuning Regulator (STR) PID controller is designed and realized to regulate the speed of a three-phase induction motor. using MATLAB software and AVR Atmega16 microcontroller as input and output from the plant with communication using serial RS232. The results show that after coordinating the system with each motor which is given a neural controller and FOC, the motor speed results are obtained which gradually provide synchronous speed so it can be concluded that this system can work well to slow down the plant response. Keywords: Induction Motor, Motor Speed, neural method, PID controller

Model-Based Predictive Control of a Solar Reactor

The present paper deals with the modelling and control of a solar reactor designed to produce syngas, by exploiting concentrated solar power. A model of the reactor based on the thermodynamic equilibrium is developed. Two model-based predictive control strategies are proposed: the first strategy (MPC strategy 1) aims to maintain the reactor's temperature at its nominal value whereas the second strategy (MPC strategy 2) aims to maintain the reactor's temperature at its nominal value, while maximizing the use of solar energy. Finally, these strategies are compared to a reference strategy, which is based on a combination of a rule-based controller and an adaptive PID controller with optimized gains. The robustness of the MPC controller to forecast errors is also studied by testing different DNI forecasting models. Parts of this paper were published as journal articleKarout, Y.; Curcio, A.; Eynard, J.; Thil, S.; Rodat, S.; Abanades, S.; Vuillerme, V.; Grieu, S. Model-Based Predictive Control of a Solar Hybrid Thermochemical Reactor for High-Temperature Steam Gasification of Biomass. Clean Technol. 2023, 5, 329-351. https://doi.org/10.3390/cleantechnol5010018

Fractional-Order PID Control of Permanent Magnet Synchronous Motors Based on Based on Improved Squirrel Algorithm

An adaptive differential squirrel algorithm fractional order PID controller is proposed to address the problems of instability and poor accuracy of Permanent Magnet Synchronous Motor PID control systems. In order to improve the global optimisation of the squirrel search algorithm, the crossover operation of the difference algorithm was combined, adaptive predation probability factor, dynamic crossover probability and Gaussian distribution perturbation operation are added to the squirrel search algorithm to improve its exploration and exploitation capability. In the comparative experiment of PMSM speed control, ADESA fractional-order PID was contrasted with conventional PID and other intelligent optimization algorithms. The results indicate that the PMSM control utilizing fractional-order PID through ADESA demonstrated minimal overshoot, the fastest response time, and superior anti-disturbance capabilities compared to PID and other intelligent optimization algorithms. This significantly enhanced the control performance of the permanent magnet synchronous motor.

Adaptive PID control of overhead cranes based on ISOA–NN

Adaptive PID control of overhead cranes based on ISOA–NN

Design and Simulation of a Multi-Channel Biomass Hot Air Furnace with an Intelligent Temperature Control System

Timely and effective drying of agricultural products is crucial for ensuring the quality and yield of grains. Biomass drying enhances energy utilization and reduces energy pressure. To this end, a novel multi-channel circulating biomass hot air furnace was designed to provide precise control of the heat source for grain drying, thereby improving the efficiency and quality of the drying process. The combustion process utilizes a multi-channel combined air supply to ensure complete combustion of biomass pellet fuel. During the heat exchange process, heat exchange plates isolate hot and cold areas, discharging combustion exhaust, while ensuring a pure air output. Using rapeseed as the drying subject, a temperature controller based on adaptive fuzzy PID was designed, targeting the biomass hot air furnace’s heat exchange system for modeling and verifying the model with the step response method. Model simulations were conducted in Matlab’s Simulink module using both adaptive fuzzy PID and traditional PID controllers, for a given signal. The settling times for the conventional PID and fuzzy PID were 445 s and 364 s, respectively, with overshoots of 20.1% and 6.3%, showing that the fuzzy PID controller performed better in terms of control performance. The validation tests showed that both control methods could maintain the temperature within ±5 °C. Compared to traditional PID control, the adaptive fuzzy PID control achieved a precision of ±3 °C. At the target temperature of 90 °C, the error was reduced to 3.7%, with a stabilization time of 1014 s. The use of fuzzy PID control exhibited better dynamic response characteristics, meeting the drying needs of rapeseed. This study provides a theoretical basis for the structural design and control system design of biomass hot air furnaces.

Achieving stable trajectory tracking in complex environments using an adaptive PID control strategy-based quadcopter drone

Stable trajectory tracking of unmanned aerial vehicles (UAVs) in complex environments is of paramount importance for achieving high precision and robustness in flight missions. In this paper, we address the attitude control problem of quadrotor UAVs and propose an optimization method based on adaptive PID control strategy. This text first presents an overview of the current status of UAVs both domestically and internationally, followed by the establishment of a mathematical model for quadrotor UAVs. Next, analysing the application of the traditional PID algorithm in UAV attitude control and provide a detailed description of the principles behind genetic algorithms and simulated annealing algorithms, along with their application in optimizing PID parameters. Through simulation experiments conducted in strong wind conditions, we compare the performance of the traditional PID algorithm with the optimization algorithm in stable trajectory tracking tasks. The experimental results demonstrate that the optimization algorithm significantly enhances the flight stability and accuracy of UAVs. Finally, this text summarizes the research findings and provide an outlook on future development directions.

Design of a Taguchi-GRA optimized PID and adaptive PID controllers for speed control of DC motor

Design of a Taguchi-GRA optimized PID and adaptive PID controllers for speed control of DC motor

Research on intelligent active disturbance rejection control algorithm for shock train leading edge of dual-mode scramjet

To prevent scramjet inlet unstart, stringent control over the shock train's leading edge in the isolator is necessary. Therefore, this study proposes a fuel flow active control scheme combining an adaptive particle swarm optimization algorithm (APSO) and active disturbance rejection control (ADRC) to enhance the traditional control (such as proportional-integral-derivative, PID) system's stability and anti-interference capabilities in complex and uncertain environments. Within this context, a simple, semi-empirical mathematical model of a dual-mode scramjet is constructed. An ADRC controller is designed to counteract the nonlinearity and disturbance of the dual-mode scramjet. ADRC, not requiring an accurate mathematical model, utilizes its extended state observer to estimate all uncertain factors affecting the controlled object based on the relationship between the shock train's leading edge and the controller's output, treating them as unknown disturbances for compensation, thereby exhibiting strong anti-interference capabilities. In order to optimize the performance, the APSO is introduced to fine-tune the ADRC controller parameters. The APSO reduces the rise time of ADRC from 22.28 to 11.39 ms, and the adjustment time from 40.05 to 13.64 ms. The APSO effectively tunes the parameters of the ADRC controller, which ensures rapidity comparable to the PID and all-coefficient adaptive control (ACAC) in managing the shock train's leading edge. At the same time, ADRC has stronger anti-interference ability and adaptive ability than ACAC and PID. This research preliminarily verifies the feasibility and advancement of the ADRC for shock train leading-edge control in scramjet isolators.

Output Recurrent Fuzzy Broad Learning Systems for Adaptive MIMO PID Control: Theory, Simulations, and Application

Output Recurrent Fuzzy Broad Learning Systems for Adaptive MIMO PID Control: Theory, Simulations, and Application

Autonomous navigation and control of magnetic microcarriers using potential field algorithm and adaptive non-linear PID.

Microparticles are increasingly employed as drug carriers inside the human body. To avoid collision with environment, they reach their destination following a predefined trajectory. However, due to the various disturbances, tracking control of microparticles is still a challenge. In this work, we propose to use an Adaptive Nonlinear PID (A-NPID) controller for trajectory tracking of microparticles. A-NPID allows the gains to be continuously adjusted to satisfy the performance requirements at different operating conditions. An in-vitro study is conducted to verify the proposed controller where a microparticle of 100 m diameter is put to navigate through an open fluidic reservoir with virtual obstacles. Firstly, a collision-free trajectory is generated using a path-planning algorithm. Secondly, the microparticle dynamic model, when moving under the influence of external forces, is derived, and employed to design the A-NPID control law. The proposed controller successfully allowed the particle to navigate autonomously following the reference collision-free trajectory in presence of varying environmental conditions. Moreover, the particle could reach its targeted position with a minimal steady-state error of 4 m. A degradation in the performance was observed when only a PID controller was used in the absence of adaptive terms. The results have been verified by simulation and experimentally.

Deep learning-based intelligent control of moisture at the exit of blade charging process in cigarette production

Abstract Currently, in the production of cigarettes in the blade, charging export moisture control means is relatively single and can not effectively guarantee the excellent quality of cigarette filament. In this paper, first of all, the working principle of the tobacco blade charging machine is introduced, and the moisture of the tobacco leaf for the charging machine is dynamically analyzed, and the influence of the return air temperature control of the charging machine on the export moisture of the blade charging process is explored. Secondly, based on the traditional PID controller, an adaptive fuzzy PID controller is established by combining adaptive fuzzy rules, and then the stacked noise-reducing self-encoder in deep learning is combined with the adaptive fuzzy PID control to design the intelligent control structure of export moisture of leaf charging process. Finally, the effectiveness of export moisture intelligence control, process capability index, and the effect before and after application were analyzed in controlled experiments, respectively. The results show that the difference between the predicted value and the real value of blade export moisture in this paper’s method is only 0.5%, and the process capability index of this paper’s method is improved by 1.48 compared with the PID controller, and it can control the temperature of the return air of the charging machine in the range of 56.86℃~57.21℃. The intelligent control method of export moisture introduced by deep learning can accurately control the export moisture of the leaf dosing process, which effectively ensures the quality of tobacco filament making.

A Robust Hybrid MRAPID Controller Design for an Underactuated Nonlinear System With Parametric Uncertainty

This paper presents an optimal design and simulation of a novel enhanced model reference adaptive proportional–integral–derivative controller (MRAPIDC) for underactuated nonlinear systems with parametric uncertainty. The controller is applied to a cart‐inverted pendulum mechanical system, a fourth‐order, nonminimum phase system with pronounced nonlinearity and unstable dynamics. This system includes a single input and multiple outputs, specifically the position and velocity of the cart, as well as the angle and angular velocity of the inverted pendulum arm. The main objective of the proposed controller is to maintain the pendulum in an upright position against gravity while moving the cart to a desired displacement regardless of parametric uncertainty, input variation, external disturbances, and random noise. The hybrid controller design is developed by combining the MRAPIDC design with controller gains applied to each output and incorporating the system adaptation error in a specific configuration. Optimal performance is achieved through parameter tuning via the social spider optimization (SSO) algorithm, and MIT (Massachusetts Institute of Technology) rule criteria are employed to ensure system stability. Performance improvements are demonstrated through several error indices, showing enhanced transient and steady‐state responses compared to both the reference model adaptive controller and the conventional proportional–integral–derivative (PID) controller. The proposed controller can effectively achieve system stability with a short settling time of 2.55 s and establishes a steady swinging balance within 2.9 s.