Optimal tuning of robust proportional integral derivative based on sliding mode controller for an AVR system

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.

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

In this paper, the particle swarm optimization (PSO) technique is used in optimising theproportional integral derivative (PID) controller parameters for the exhaust temperature control of a gas turbine system. The performance of the PID controller whose parameters are tuned based on the PSO method (PSO-PID) is compared with the conventional PID (CPID) controller that employs the Ziegler-Nichols method. A new performance criterion, known as multipurpose performance criterion (MPPC) is proposed and used in the PSO algorithm. Time domain performance of the PSO-PID controller, such as the maximum overshoot QUOTE , rise time QUOTE , settling time QUOTE and absolute error (AE) are being optimized based on the MPPC and compared with other performance criteria such as the integral of time multiplied by absolute error (ITAE), integral of time multiplied by square error (ITSE), integral square error (ISE) and integral of absolute error (IAE). Result shows that the PSO technique, combined with the MPPC performance criterion is very effective to yield optimal transient response of the gas turbine exhaust temperature. An adjustable weighting factor in the MPPC technique makes it more reliable, consistent and flexible as compared to the commonly used performance criteria. Key words: Proportional integral derivative (PID) controller, particle swarm optimization, multipurpose performance criterion, gas turbine exhaust temperature.

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.

Magnetic levitation system is a second-order nonlinear system. The objective of the system is to levitate a metallic ball at a determined height under the influence of magnetic force by the principle of non-contact. In this paper, particle swarm optimization (PSO) technique is used to develop a controller to regulate the nonlinear dynamics in the magnetic levitation system. The proposed technique acts in parallel with a proportional integral derivative (PID) controller to balance the position of levitating ball in the air. Here, the optimization technique tunes the gain values of the PID controller for achieving better performance measures when applied to a nonlinear system. This tuning process adheres to search the optimal solution with the help of agents called as particles whose trajectories are adjusted by a deterministic component. Each particle is optimized by its best-achieved position through the iterative approach. Further, the performance of the proposed controller is demonstrated through simulation as well as on hardware. Both PID and PSO tuned PID controllers were designed and the dynamics of magnetic levitation system were tested. The results validate the performance of both conventional PID controller as well as PSO tuned PID controller enhancing the operation of the magnetic levitation system. It is observed that the PSO tuned PID overcomes the drawback of conventional PID by optimizing the performance parameters such as steady-state error and settling time.

Mobile robots experience a lot of challenges with path tracking because of the significant problem with DC motor speed control. The particle swarm optimization (PSO) tuned proportional integral derivative (PID) controller is commonly used to control DC motor speed. However, it fails to reach the optimal solution quickly. Therefore, this paper demonstrates the application of the artificial bee colony (ABC) algorithm to tune a PID controller for speed regulation of a DC motor. The proposed approach was used to obtain the optimal PID controller gains (proportional, integral, and derivative) by minimizing the integral of time absolute error (ITAE) as the objective function. The analysis of statistical tests, transients, and frequency responses was conducted to test the rapid convergence of the proposed approach. Also, the performance of the proposed ABC-tuned PID controller has been compared with three variants of the PSO-tuned PID controller. The conducted simulation results and comparison with the proposed ABC controller and variant PSO controllers have shown that the proposed ABC-PID controller is more effective and robust than the variant PSO-PID controllers for the speed control of a DC motor.

A major concern is frequency change with load. So, Load Frequency Control (LFC) of an interconnected power system is proposed in this research using a unique integral plus proportional integral derivative controller with filter (IPIDF). The Differential Evolution (DE) algorithm is used to optimize the integral plus proportional integral derivative controller with filter controller parameters for a two-area power system. By contrasting the results of the proposed method with those of recently published optimization techniques for the same power system, such as the Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Firefly Algorithm (FA), and Differential Evolution (DE) based Proportional integral derivative (PID) and PIDF controllers, the superiority of the integral plus proportional integral derivative controller with filter approach is made clear. It is possible to determine the system performance index like integral time multiplied the absolute error (ITAE) and the settling time (Ts). The power system with superconducting magnetic energy storage and an HVDC link is also included in the proposed work, and the values of the suggested integral plus proportional integral derivative controller with filter controllers are evaluated using the Differential Evolution method. By comparing the outcomes with the Differential Evolution tuned PIDF controller for the identical power systems, the suggested controller’s superiority is demonstrated. To show the stability of the recommended Differential Evolution algorithm tuned integral plus proportional integral derivative controller with filter controller, the speed governor, turbine, synchronizing coefficient, and frequency bias parameters’ time constants and operating load conditions are varied in the range of +25 to −25% from their nominal values, along with the magnitude and location of step load perturbation and pulse load perturbation, to perform sensitivity analysis. According to research, proposed integral plus proportional integral derivative controller with filter controllers offer greater dynamic response by minimizing time required to settle and undershoots than Proportional integral derivative controllers and PIDF controllers. MATLAB/Simulink is used to run the simulations.

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.

This paper presents a method to get the optimal tuning of Proportional Integral Derivative (PID) controller parameters for an AVR system of a synchronous generator using Particle Swarm Optimization (PSO) algorithm. The AVR is not initially robust to variations of the power system parameters. Therefore, it was necessary to use PID controller to increase the stability margin and to improve performance of the system. Fast tuning of optimum (PID) controller parameter yield high quality solution. New criteria for time domain performance evaluation was defined. Simulation for comparison between the proposed method and Ziegler-Nichols method is done. The proposed method was indeed more efficient also. The terminal voltage step response for AVR model will be discussed in different cases and the effect of adding rate feed back stabilizer to the model on the terminal voltage response. Then the rate feedback will be compared with the proposed PID controller based on use of (PSO) method to find its coefficients. Different simulation results are presented and discussed.

The primary focus of this paper is to assess an interconnected power system using different optimization techniques. The main purpose is to employ different optimization techniques, including genetic algorithms (GA) and particle swarm optimization (PSO), to systematically enhance the performance of a multi-area or two-area automatic generation control (AGC) system, aiming to optimize the three PID controllers gain values and improve system performance under diverse loading conditions. Two case studies are conducted exploring different loading conditions in the megawatt (MW) range, including increasing load demand and decreasing load demand. The analysis involves four scenarios, covering without any kind of controller, another with solely a proportional integral derivative (PID) controller, a PID controller enhanced through a genetic algorithm (GA), and lastly, a PID controller improved through particle swarm optimization (PSO). The optimization process utilizes the integral time absolute error (ITAE) as the objective function to evaluate the system's performance. The simulation outcomes for ITAE, settling time, overshoot, and undershoot for frequency deviation of area one, area two, and power deviation in the tie-line are compared with previous similar studies to assess the novelty of this work. The article highlights the importance of the multi-area AGC system and the significance of different optimization techniques in improving its performance.

In this paper, a new hybrid optimization algorithm known as particle swarm optimization and grey wolf optimizer (PSO-GWO) based proportional integral derivative (PID) controller is suggested for automatic voltage regulator (AVR) system terminal tracking problem. The main objective of the suggested approach is to reduce crucial performance factors such as rise time, settling time, peak overshoot and peak time of the voltage of the power system in order to improve the AVR system's transient response. This analysis was compared to results obtained from existing heuristic algorithm-based approaches found in the literature, proving the improved PID controller's enhanced performance obtained through the suggested approach. Furthermore, the performance of the tuned controller with respect to disturbance rejection and its robustness to parametric uncertainties were evaluated separately and compared with existing control approaches. According to the obtained comparison results and from all simulations, using MATLAB-Simulink tool, it has been noted that the PID controller optimized using PSO-GWO algorithm has superior control performance compared to PID controllers tuned by ABC, DE, BBO and PSO algorithms. The main conclusion of the presented study highlights that the recommended strategy can be effectively implemented to improve the performance of the AVR system.

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.

Optimizing PID controller parameters for robust automatic voltage regulator system through indirect design approach-2