Improving Barcode Vision Scanning Process using a Drone-based Tracking PID Controller for Warehouse in Industry 4.0

This paper presents the performance improvement of hard real-time PID (proportional + integral + derivative) controller over the soft real-time one. PID controller is a generic control loop feedback mechanism widely used in industrial control system. For hard real-time PID implementation, we have considered PID controller algorithm in RT-Linux operating system. To achieve an efficient result a sampling rate of 10 msec is considered. The system performance has been analyzed (for both hard real-time and soft real-time) in time domain. In this regard, we performed a laboratory test on servo system for both soft real-time (windows based) and hard real-time (RT-Linux based) PID controller. The test results of our laboratory experiments clearly illustrates that the hard real-time PID controller perform better than soft real-time PID controller.

This paper describes the Adaptive PID (APID) controller design for speed control preference of Brushless Direct Current (BLDC) motor over the Proportional Integrative Derivative (PID) controller. A methodology of the Adaptive PID controller is proposed, which tunes the parameters automatically. Modeling of the BLDC motor was carried out using PID and Adaptive PID controller, respectively. The behavior of the BLDC motor is analyzed without a controller and by using the conventional PID controller and the new APID controller. Hence the result obtained is analyzed and compared by taking two cases. In the first case of constant speed, the PID controller gave large variability in the initial speed and could not track the desired speed. Also, applied torque could not track the desired speed due to a significant deviation in the actual motor speed. Whereas, in the case of APID, the controller gave small variability in the initial speed and could track the desired motor speed. In the second case of variable speed, the PID controller produced a random response at a variable speed. Whereas, in APID, the controller had an accurate response at variable speed, with no deviation. The result obtained shows that the APID controller provides effective, easier, and fast controlling of the BLDC motor. The output response of the BLDC motor is achieved, and the result is analyzed with the help of utilizing MATLAB and SIMULINK. Background: The BLDC motor is considerably used in the home, transportation, and industrial application. Objective: Comparative analysis of modeling and control of BLDC motor drives for the variable required speed. Methods: PID and APID controllers are used in this paper to operate the BLDC motor. Results: A Fixed and variable speed response of both APID and PID controlled BLDC motor is obtained. Conclusion: Response of the speed control of APID controlled BLDC motor is superior to PID controlled BLDC motor at variable speed.

ABSTRACTThis paper is focused on design and application of proportional integral derivative (PID) controller for deregulated multiarea automatic generation control (AGC) scheme utilizing the imperialist competitive algorithm (ICA) under various electricity market transactions. A multiarea hydro thermal 75-bus real power system is considered as a test system for deregulated AGC scheme. Simulation study results demonstrate the effective performance of controller on various load disturbance scenarios. The performance of ICA-tuned PID controller is also compared with genetic algorithm (GA)-tuned PID controller. The comparative results show that ICA–PID controller has higher convergence rate, smaller settling time, reduced oscillations than GA-tuned PID controller, i.e. reaching to better solution. Robustness of ICA–PID controller is also checked to determine its response towards system parameters uncertainties through sensitivity analysis. It is seen that the performance of PID controller tuned by ICA algorithm is robust for the system parameter uncertainties while GA–PID controller performance degrades.

Lighting is a fundamental cornerstone within interior design, possessing the capability to metamorphose spaces and evoke emotional responses profoundly. This principle applies to residential, industrial, and office domains, where lighting nuances are meticulously adjusted to enhance comfort and practicality. However, adequate luminance frequently intersects with energy wastage, often attributed to negligent light management practices. Mitigating this issue necessitates integrating light intensity controls adept at adapting to ambient luminosity and room-specific parameters. A prospective avenue encompasses incorporating a Proportional Integral Derivative (PID) control system synergized with light sensors. This research Implementing a closed-loop architecture, PID control utilizes feedback mechanisms to improve the precision of instrumentation systems. The PID methodology, consisting of Proportional, Integral, and Derivative control modalities, produces stable responses, accelerates system reactions, and diminishes deviations and overshooting by predetermined setpoints. The proposed Light Intensity Control System underpinned by PID methodology manifests as an exhibition of compelling outcomes drawn from empirical trials. The judicious selection of optimal parameters, specifically Kp = 0.2, Ki = 0.1, and Kd = 0.1, yielded noteworthy test outcomes: an ascent time of 0.0848, an overshoot of 6.5900, a culmination period of 0.4800, a settling period of 2.3032, and a steady-state error of 0.0300. Within this system, the PID controller assumes a pivotal role, orchestrating the regulation and meticulous calibration of light intensity to harmonize with designated criteria, thus fostering an environment of augmented energy efficiency and adaptability in illumination.Lighting is a fundamental cornerstone within interior design, possessing the capability to metamorphose spaces and evoke emotional responses profoundly. This principle applies to residential, industrial, and office domains, where lighting nuances are meticulously adjusted to enhance comfort and practicality. However, adequate luminance frequently intersects with energy wastage, often attributed to negligent light management practices. Mitigating this issue necessitates integrating light intensity controls adept at adapting to ambient luminosity and room-specific parameters. A prospective avenue encompasses incorporating a Proportional Integral Derivative (PID) control system synergized with light sensors. This research Implementing a closed-loop architecture, PID control utilizes feedback mechanisms to improve the precision of instrumentation systems. The PID methodology, consisting of Proportional, Integral, and Derivative control modalities, produces stable responses, accelerates system reactions, and diminishes deviations and overshooting by predetermined setpoints. The proposed Light Intensity Control System underpinned by PID methodology manifests as an exhibition of compelling outcomes drawn from empirical trials. The judicious selection of optimal parameters, specifically Kp = 0.2, Ki = 0.1, and Kd = 0.1, yielded noteworthy test outcomes: an ascent time of 0.0848, an overshoot of 6.5900, a culmination period of 0.4800, a settling period of 2.3032, and a steady-state error of 0.0300. Within this system, the PID controller assumes a pivotal role, orchestrating the regulation and meticulous calibration of light intensity to harmonize with designated criteria, thus fostering an environment of augmented energy efficiency and adaptability in illumination.

Special issue on PID control in the information age: Theoretical advances and applications

This paper explores the integration of the Kalman Filter and the Proportional Integral Derivative (PID) controller. In control systems, noise is a major source of error. As a solution to noise, the fusion of the Kalman Filter and the PID controller is discussed. If a PID controller alone were to operate in a system with noise, the derivative component of the PID controller would significantly alter the output of the system, causing undesirable instability. In the proposed controller design, the Kalman filter operates before the PID by in-taking the noisy data and outputting a clean signal. The Kalman Filter allows the PID controller to operate on this clean signal to regulate the output, creating a precisely controlled system. Through the synthesis of these two devices for RPM (rotations per minute) regulation, this application of Control Theory opens the door to PID in industries where it was previously ineffective, and with refinement, will play a part in the innovations of this era.

Conventional PID (Proportional Integral Derivative) control algorithm has been commonly employed in the speed controller design because of easy control and implementation. However, it cannot solve the problems due to the motor parameters and any load disturbance, not even the sensitivity. In order to obtain the dynamic speed response due to these problems, a PID control method based on a radial basis function neural network (RBFNN) is proposed in this paper. The gain parameters of PID controller are tuned by performing the RBFNN according to the variations of system parameters. Finally, a prototype of the RBFNN based motor drive for a brushless DC (BLDC) motor is designed and implemented in this paper. A comparison between conventional PID and RBFNN-based PID control is performed. The experimental results to the range hood show that RBFNN based PID control has better performance than PID control.

In this paper linear quadratic Regulator (LQR) and Proportional integral derivative (PID) controllers are designed for pitch control system of an aircraft. Genetic Algorithm (GA) is used for tuning the parameter LQR and PID controllers. The controller design begins with the appropriate mathematical model to account the longitudinal motion of an aircraft. Simulation is done within the MATLAB and results for the pitch angle response of an aircraft are represented in step's response. LQR and PID controllers are associated with their parameters which can either be tuned manually or by optimized methods like genetic algorithms to get better control and enhancement in the performance. GA is the powerful tool in the field of global optimization to various problems. Here genetic algorithm is successfully applied for Pitch control of an aircraft using MATLAB simulation for tuning of LQR and PID controllers and thus optimized parameters values for the LQR and PID controllers are derived.

This paper focuses on the design method of the optimal multiple inputs and multiple outputs (MIMO) proportional integral derivative (PID) controllers for the MIMO processes via using Lyapunov theorems. A hybrid augmented integral squared error (HAISE) is applied to design the optimal multi-loop PID controller for the MIMO plants. The optimal multi-loop PID control problem is transformed into a nonlinear constraint optimization (NLCO) problem. The optimal PID controller parameters are obtained from solving the NLCO problem. The design method is applied to devise the multi-loop optimal PID controller for different types of MIMO plants and the optimal PID controller under different control weight is shown in this paper. The performances of different PID tuning methods are studied too. The computer simulation results are presented to demonstrate the effectiveness of the design method and good performance and robustness of the optimal multi-loop PID controllers.

This paper presents new circuits for realizing both current-mode and voltage-mode proportional-integral- derivative (PID), proportional-derivative (PD) and proportional-integral (PI) controllers employing second- generation current conveyors (CCIIs) as active elements. All of the proposed PID, PI and PD controllers have grounded passive elements and adjustable parameters. The controllers employ reduced number of active and passive components with respect to the traditional op-amp-based PID, PI and PD controllers. A closed loop control system using the proposed PID controller is designed and simulated with SPICE. I. INTRODUCTION The proportional-integral-derivative (PID), proportional- integral (PI) and proportional-derivative (PD) controllers with adjustable parameters are widely used in many industrial control systems, and usually have been implemented with operational amplifiers (op-amps). PID controller realizations using op-amps employ excessive number of active and passive components. Current conveyors, especially plus-type second-generation current conveyors (CCII+s) have larger bandwidth and dynamic range, and also greater linearity than op-amps (1). Employing only CCII+s simplifies the configuration thus gyrator (2), synthetic floating inductances (3-4), voltage- mode filter (5) and transfer function synthesis (6-7) employing only CCII+s have been reported in the literature. Despite of several circuits such as derivative, integral, proportion and summer constructed with op-amps (8-9) that can be used to implement PID, PI and PD controllers, current conveyors have not been used to realize PID, PD and PI controllers in the literature so far. In this paper, novel current-mode and voltage-mode PID, PI and PD controllers employing CCII+s as active elements are proposed. In the proposed current-mode controllers, output currents are taken from high impedance terminals of the CCII+s. Also input voltages are applied to the Y-terminals of the CCII+s in the presented voltage- mode PID, PI and PD controllers. These properties make the proposed circuit ideal for using in cascaded systems. All the proposed controller realizations employ only grounded passive components, and do not require passive element matching so the circuits are suitable for integration. The PID, PI and PD parameters can be selected independently. Also simulations are performed using SPICE program to verify the theoretical analyzes.

PurposeThe aim of the paper is the fine‐tuning of proportional integral derivative (PID) controllers under model parameter uncertainties (noise).Design/methodology/approachThe fine‐tuning of PID controllers achieved using the Taguchi method following the steps given: selection of the control factors of the PID with their levels; identification of the noise factors that cause undesirable variation on the quality characteristic of PID; design of the matrix experiment and definition of the data analysis procedure; analysis of the data; decision regarding optimum settings of the control parameters and predictions of the performance at optimum levels of control factors; calculation of the expected cost savings under optimum condition; and confirmation of experimental results.FindingsAn example of the proposed method is presented and demonstrates that given certain performance criteria, the Taguchi method can indeed provide sub‐optimal values for fine PID tuning in the presence of model parameter uncertainties (noise). The contribution of each factor to the variation of the mean and the variability of error is also calculated. The expected cost savings for PID under optimum condition are calculated. The confirmation experiments are conducted on a real PID controller.Research limitations/implicationsAs a further research it is proposed the contiguous fine‐tuning of PID controllers under a number of a variant controllable models (noise).Practical implicationsThe enhancement of PID controllers by Taguchi method is proposed with the form of a hardware mechanism. This mechanism will be incorporated in the PID controller and automatically regulate the PID parameters reducing the noise influence.Originality/valueApplication of Taguchi method in the scientific field of automation control.

In this document is presented the simulation of the adaptive control and obtaining of the nonlinear model of a MIPR (mobile inverted pendulum robot), this model consists of four variables (velocity and acceleration in wheels, velocity and acceleration in pendulum). The objective is to implement an adaptive PID (proportional, integral derivative) controller using the GDM (gradient descendent method), to keep the pendulum in an unstable equilibrium point, the GDM is used for to optimize the gains of the PID controller. The parameters weight and length of the nonlinear model are varying in the time. The response of adaptive control is compared with a classic PID controller of constant gains. The system with two laws of control was implemented in the Matlab software, the results are showed that classic PID controller with constant gains can not to control the MIPR, on the other hand the adaptive PID controller has good response.

This paper presents a way that fu zzy logic can be used in high level control functions. Specifically, we examine the use of fuzzy logic in supervisory control, for selecting discrete control actions, identifying the operating environment and evaluating controller performance. Proportional integral derivative (PID) controllers are widely used in excitation control of power systems, they exh ibit poor performance when the controlled systems contain unknown linearities. The main objective of this paper is to simu late the use of fuzzy logic to provide new control functions that are outside the domain of the PID control-where fuzzy control is likely to provide the greatest payoff. Simply, a supervisory correction term is added to the input of the PID controller. The supervisory correction term is the output of fuzzy supervisory controller. A performance demonstration of the proposed scheme via the excitation control of a single-machine infinite-bus system subjected to a wide variety of transient disturbances is presented in this paper. Ou r results show that the fuzzy supervisory PID controllers have high performance compared to PID controllers with significant reduction in overshoot and undershoot. The scheme can be easily imp lemented in pract ice by adding a fuzzy supervisory controller to the existing PID controller

The operation and control of the modern power system has become complex and difficult due to the incessant penetration of nonconventional energy sources integrated to the power grid and the structural variation of power system with continuing escalation of power demand in recent years. This entails the implementation of intelligent control strategy for satisfactory operation of the power system. Hence, a fractional order fuzzy proportional integral derivative (FOFPID) controller is suggested in this article for automatic generation control of two unequal area interconnected power system with diverse generating units such as thermal, hydro, diesel and wind power plants. The dynamic performance of the system is investigated by using proportional integral derivative (PID), fractional order PID (FOPID), fuzzy PID (FPID) and fractional order fuzzy PID (FOFPID) controllers separately. The parameters of these controllers are optimised by using ant lion optimiser algorithm with integral time absolute error as the objective function. The supremacy of the proposed controller is established by contrasting the results with FPID, FOPID and PID controllers. It is also observed that FPID controller gives superior result than PID and FOPID controllers. Further it is found that the result of FOPID controller is better than the integer order PID controller. Finally robustness analysis is performed to confirm the robustness of the proposed controller against parametric variations and random loading of the system.

This paper describes the development of activesuspension system of light passenger vehicle to improveride comfort of the passengers using PID (Proportional –Integral - Derivative) controller. The system is subjected tobumpy road and its performance is assessed and comparedwith a passive suspension system. Tuning of the controllerparameters is also illustrated. Experimental verification ofanalytical results is carried out. It is found that ride comfortis improved by 78.03%, suspension travel has been reducedby 71.05% and road holding ability is improved by60% with active suspension system when compared withpassive suspension system. Therefore it is concluded thatactive suspension system with PID controller is superior topassive suspension system.