Auto-tuning PID Controller Research Articles

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

Robustness Analysis for a Nonlinear Gantry Crane System Using Fuzzy Logic Controller

Gantry cranes are equipment used for loading and unloading heavy items. They are majorly used in the industries and shipyards to transport heavy loads. Generally, operational requirements for the gantry crane is to move the load with minimum swing angle, high positioning accuracy and with minimum time possible. External and environmental disturbances affects the performance of the gantry crane when in operation. Control of the gantry crane system has over many years attracted many researchers with interest in designing a robust gantry crane system free of swing motions. Several researchers have proposed the use of PID controllers. However, their control methods have limitations among them, being sensitive to disturbances. Thus, for the crane to operate effectively, trolley positioning and swing motion of the load need to be controlled. The main goal of this research is to design a robust control mechanism for the gantry crane system against swing motion due to uncertainties. The gantry crane system has been designed using mathematical modeling with assumption that the load is attached to a trolley using one rope and moves in 2D plane. The robustness analysis of the Fuzzy Logic Controller (FLC) has been simulated in MATLAB/Simulink environment and comparison done with auto-tuned PID controller in environment where external disturbances are present. Various transient response performance indices specifications have been used to determine the effectiveness of the controller model. The final results have demonstrated that FLC is capable of producing better results of minimizing swing motion as compared to PID controllers.

Applying Cognitive Methodology in Designing On-Line Auto-Tuning Robust PID Controller for the Real Heating System

A novel design and implementation of a cognitive methodology for the on-line auto-tuning robust PID controller in a real heating system is presented in this paper. The aim of the proposed work is to construct a cognitive control methodology that gives optimal control signal to the heating system, which achieve the following objectives: fast and precise search efficiency in finding the on- line optimal PID controller parameters in order to find the optimal output temperature response for the heating system. The cognitive methodology (CM) consists of three engines: breeding engine based Routh-Hurwitz criterion stability, search engine based particle swarm optimization (PSO) and aggregation knowledge engine based cultural algorithm (CA). Matlab simulation package is used to carry out the proposed methodology that finds and tunes the optimal values of the robust PID parameters on-line. In real-time, the LabVIEW package is guided to design the on-line robust PID controller for the heating system. Numerical simulations and experimental results are compared with each other and showed the effectiveness of the proposed control methodology in terms of fast and smooth dynamic response for the heating system, especially when the control methodology considers the external disturbance attenuation problem.

Design of Auto-Tuning Nonlinear PID Tracking Speed Control for Electric Vehicle with Uncertainty Consideration

This study presents a new auto-tuning nonlinear PID controller for a nonlinear electric vehicle (EV) model. The purpose of the proposed control was to achieve two aims. The first aim was to enhance the dynamic performance of the EV regarding internal and external disturbances. The second aim was to minimize the power consumption of the EV. To ensure that these aims were achieved, two famous controllers were implemented. The first was the PID controller based on the COVID-19 optimization. The second was the nonlinear PID (NPID) optimized controller, also using the COVID-19 optimization. Several driving cycles were executed to compare their dynamic performance and the power consumption. The results showed that the auto-tuning NPID had a smooth dynamic response, with a minimum rise and settling time compared to other control techniques (PID and NPID controllers). Moreover, it achieved low continuous power consumption throughout the driving cycles.

Auto tuned PID and neural network predictive controller for a flow loop pilot plant

This paper presents an advanced control strategy that uses the neural network (NN) predictive controller to govern the dynamics of a Flow loop pilot plant. The set point tracking using NN Predictive controller and conventional PID strategies are investigated. The control objective is to keep the outlet flow of the Single Input Single Output Flow loop at a reference value. An orifice meter as a flow measuring device and Variable Frequency Drive as a final control element is selected to collect Input, Output data. System Identification toolbox in MATLAB is used to construct the model of a flow loop from measured input–output data. Various models are estimated, validated and realized that the transfer function model gives the best fit. This model is used to build the PID and NN Predictive controller. NN predictive controller parameters are set, by the designer's expertise. The performance of a NN predictive controller is then compared with the conventional auto-tuned PID controller. Experimental results are presented, which emphasizes the superiority and effectiveness of the NN predictive controller.

Comparison between real-time auto-tuning PID and conventional PID controller for a dairy industrial evaporation process

Comparison between real-time auto-tuning PID and conventional PID controller for a dairy industrial evaporation process

Comparison between real-time auto-tuning PID and conventional PID controller for a dairy industrial evaporation process

Comparison between real-time auto-tuning PID and conventional PID controller for a dairy industrial evaporation process

딥러닝 기반의 오토 튜닝 PID 제어를 이용한 차량 경로 제어 모델 개발

딥러닝 기반의 오토 튜닝 PID 제어를 이용한 차량 경로 제어 모델 개발

Output Force Control of a Pneumatic Soft Gripper with a Jointed Endoskeleton Structure

Soft grippers are suitable for gripping various objects without damage owing to their high flexibility. But current soft grippers have a general problem of insufficient gripping force. To grip objects with different mass and sizes on assembly line safely and reliably, a novel pneumatic soft gripper with a jointed endoskeleton structure was developed and fabricated successfully. The actuation and force bearing functions of the gripper were separated to increase the gripping force. Moreover, to control the output force of the finger tip of the soft gripper, a fuzzy auto-tuning PID controller was designed and the relevant parameters of the controller were determined. Furthermore, a test platform with the designed fuzzy auto-tuning PID controller was developed to control the output force of the finger tip. The test results showed that the controller was able to maintain the desired target output force with a maximum deviation of 0.6 N. At last, the soft gripper was used to grip material objects for testing. From the experiment, it indicated that the output force of the finger tip can reach up to the desired values accurately and various objects can be gripped safely and reliably.

Performances Comparison between Real-Time Auto-Tuning PID and Conventional PID Controller for a Dairy Industrial Evaporation Process Control

In this study, an industrial milk evaporation process is introduced and a mathematical model of a multi-effect falling film evaporator is developed using MATLAB/Simulink to evaluate the performance of the controller. A real-time closed-loop auto-tuning PID controller is presented as a candidate control strategy for the evaporation process. The simulation results controlled by autotuning and conventional PID are compared and discussed and the performance improvement by the auto-tuning PID controller is illustrated.

Separately Excited DC Motor Speed Tracking Control Using Adaptive Neuro-Fuzzy Inference System Based on Genetic Algorithm Particle Swarm Optimization and Fuzzy Auto-Tuning PID

This paper presents detailed comparative studies to control the separately excited DC motor speed. The configurations of proposed methods are fuzzy auto-tuning PID and Adaptive Neuro-fuzzy inference system. The study intends to optimize the parameters of the transient speed response, for instance, the overshoot, settling time and rise time. Also, a comparison has been made between the two suggested controllers. PID tuned by Ziegler Nicholas’s method utilized as a basis for the proposed controllers. Furthermore, PID model is used to extract the data set for ANFIS configuration, which has been manipulated by genetic algorithm and particle swarm optimization to ensure that it will be able to cover all operation condition possibilities. In order to investigate the eligibility of the suggested controllers, the model of the DC Motor is tested under several conditions. Finally, the results which implemented using Matlab-Simulink toolbox showed that fuzzy auto-tuning PID controller is too complicated, and has slow dynamic response comparing with ANFIS controller. Moreover, ANFIS has rapid robustness efficiency, also in the domain of the motor response characteristics. ANFIS expressed superior proficiency and better Keywords: SEDC motor, fuzzy auto-tuning PID, Adaptive Neuro-fuzzy inference system (ANFIS), genetic algorithm, particle swarm optimization, data set, and FIS. Performance under various experimental conditions.

A control strategy for electro-magneto-mechanical system based on virtual system model

A new approach to the control of electro-magneto-mechanical system is proposed in this paper. Conventionally, these systems are controlled based on the Maxwell system model via an on-off or PID control technique, which displays acceptable performance in the low frequency region, but not in the high frequency region where position control performance is greatly degraded. In order to improve the performance, a newly developed virtual 2nd order system modeling technique, SSID, is adopted for a complex electro-magnetomechanical system in the study. This technique states that any unknown system exposed to a random disturbance with unknown intensity can be identified in terms of a virtual 2nd order system model via the inverse process of a certain stochastic analysis. As a typical hybrid system, a solenoid valve is used as the target electro-magneto-mechanical system to study the modeling of the virtual 2nd order system. In order to confirm the performance of the proposed control strategy, autotuning PID controller in PWM mode is utilized. Simulations based on the conventional Maxwell system model with control via the bang-bang, autotuning PID, and the proposed virtual 2nd order system model approaches are conducted using MATLAB Simulink. Performance of these three systems in the low and high frequency bands is also compared. The simulation results reveal that the control performance of the virtual 2nd order system model is much improved compared with that of the Maxwell system model under autotuning PID and bang-bang controls in both low and high frequency regions, where the error is drastically reduced to approximately 1/5 of the original value.

FPGA-based optimal robust minimal-order controller structure of a DC–DC converter with Pareto front solution

This paper presents multi-objective optimization-based robust controller design of a DC–DC boost converter, controlled with FPGA (Field Programmable Gate Array). The main aim of the proposed design technique is to obtain a fixed and low-order robust controller which is reliable and easy to implement on a low-cost real-time digital system. The improved proposed control design method with direct closed-loop pole position assessment using metric L2, is based on robust optimal regional closed-loop pole assignment technique. The optimal solution has been obtained using multi-objective Pareto front search genetic algorithm. This paper also presents simulated and practical experimental results with implemented optimized robust controller on FPGA, controlling the DC–DC boost converter. For the sake of comparison with the proposed controller design method, an additional auto-tuned PID controller has been designed along with robust controllers based on mixed sensitivity loop-shaping method.

Adaptive PID controller design for wing rock suppression using self-recurrent wavelet neural network identifier

This paper presents a novel control scheme based on auto-tuning PID controller to suppress wing rock phenomena. Due to having a complex dynamic, wing rock motion identification is not a simple task, and this complexity can adversely affect the performance of PID controller. Employing a wavelet neural network based identifier, this paper develops an auto tuning adaptive PID controller to tackle the problem. Since having an acceptable control performance inevitably involves having a meticulously trained identifier, the training performance is of utmost importance. Aiming at boosting the training efficacy, a two-phase algorithm encompassing Bees algorithm and Back-Propagation (BP) is proposed by this paper to train the proposed identifier respectively in off-line and on-line modes. Due to its inherent capability in sifting the global minima, Bees algorithm is employed to find initial values of weights around which it is then possible to conduct a local search by means of BP based online training. Therefore, the identifier can precisely furnish the proposed PID controller with the system sensitivity in on-line mode. The adaption of PID controller can thus be performed in each time step. The performance of this method has been presented in simulation results and the comparison section confirms the effectiveness of proposed scheme.

APPLICATION OF AUTO-TUNING ZIEGLER-NICHOLS PID CONTROL IN CHAOTIC VIBRATION SUPPRESSION OF A BEAM WITH NONLINEAR BOUNDARY CONDITIONS

This paper investigates the vibration control of an elastic beam with nonlinear boundary conditions using Macro Fiber Composite (MFC) actuators and an adaptive control strategy. This system is known as a chaotic system under external sinusoidal applied force. The beam is clamped at one end, and the other end is pinned for the tip displacements less than a fixed value and is free for displacements greater than this value. For certain values of forcing frequency and amplitude the periodic motion becomes unstable and non-periodic, bounded vibration results. Meanwhile, for control purposes an autotuning Ziegler-Nichols PID controller is implemented in order to investigate the robustness and vibration suppression performance of the closed-loop system. Finally, numerical simulations evaluate the effectiveness of this control strategy.

A novel auto-tuning PID control mechanism for nonlinear systems

In this paper, a novel Runge–Kutta (RK) discretization-based model-predictive auto-tuning proportional-integral-derivative controller (RK-PID) is introduced for the control of continuous-time nonlinear systems. The parameters of the PID controller are tuned using RK model of the system through prediction error-square minimization where the predicted information of tracking error provides an enhanced tuning of the parameters. Based on the model-predictive control (MPC) approach, the proposed mechanism provides necessary PID parameter adaptations while generating additive correction terms to assist the initially inadequate PID controller. Efficiency of the proposed mechanism has been tested on two experimental real-time systems: an unstable single-input single-output (SISO) nonlinear magnetic-levitation system and a nonlinear multi-input multi-output (MIMO) liquid-level system. RK-PID has been compared to standard PID, standard nonlinear MPC (NMPC), RK-MPC and conventional sliding-mode control (SMC) methods in terms of control performance, robustness, computational complexity and design issue. The proposed mechanism exhibits acceptable tuning and control performance with very small steady-state tracking errors, and provides very short settling time for parameter convergence.

An Auto-Tuning Method for the Scaling Factors of Fuzzy Logic Controllers with Application to SISO Mechanical System

In this paper, a PD-like self-tuning fuzzy controller based on tuning of scaling factors (STFC) by gradient descent method is presented. The tuning scheme allows the tuning of the scaling factors to be on-line. Tuning scaling factors is more effective and simpler than tuning all the parameters of standard fuzzy logic controller (FLC). The aim is to obtain good performance parameters, such as the rise time, the overshoot, the steady-state error. Experimental results of an inverted pendulum system with STFC controller show a better performance in the transient and steady state phases than other classical controllers like PD, PID, auto-tuned PID controller (PID-AT), and linear quadratic regulator (LQR). Index Terms—Fuzzy logic controller (FLC), scaling factors, gradient decent method, performance indices.

Study on Auto-Tuning PID Controller

PID control is the most common control method used in process control. The PID control parameters tuning methods are develop constantly. At present, in numerous tuning methods, there are mainly two methods applied better in the practical industrial process. One is based on pattern identification (based on rules); the other is based on relay feedback (based on model). They are collectively referred to as intelligent PID parameter tuning method. This paper studies on the PID parameter auto-tuning methods and introduces the results of the latest research on this subject. Finally, the development direction of auto-tuning PID controller was also prospected.

Intelligent Control of Rehabilitation Robot: Auto Tuning PID Controller with Interval Type 2 Fuzzy for DC Servomotor

DC servomotors have been in use extensively for many applications vary from industrial to robotics to consumers. DC servomotors provide error feedback signal to its PID controller to minimize the error. However, its conventional PID controller still causes several problems such as poor accuracy in non-linear systems and poor response when there is frequent disturbance. In this research, atuning method for PID controller,i.e., Interval Type-2 Fuzzy Logic for DC servomotor is proposed. The paper proposes error and error derivative are oftype 2 fuzzy inputs to the rules and output is the corrective signal to supplement PID output. Detailed methodology and work results show that both type-1 and type-2 fuzzy logic tuning offers better response than conventional PID controller and Interval Type-2 Fuzzy Logic handles uncertainties better that Type-1.Thiscould be used as intelligent control in Rehabilitation Robots. In addition, italso provides promising potentials in robotics and medical applications that require high precision and quick response such as medical diaphragm pump, infusion pump, pharmaceutical dispenser, minimally invasive surgery, etc.

PID auto-tuning controller design via fuzzy logic

PID auto-tuning controller was designed via fuzzy logic. Typical values such as error and error derivative feedbackwere changed as heuristic expressions, and they determine PID gain through fuzzy logic and defuzzification process. Fuzzy procedure and PID controller design were considered separately, and they are combined and analyzed. Obtained auto-tuning PID controller by Fuzzy Logic showed the ability for less than 3rd order plant control.