Particle Swarm Optimization (PSO) Based Turbine Control

Proportional-Integral-Derivate (PID) controllers are widely used in industry because of their remarkable efficiency, simple structure and robust performance for a wide range of applications. Parameters tuning (Kp,Ki,Kd) of PID controller is necessary to satisfy the operation of the system. But many tuning methods such as Ziegler-Nichols methods do not work so perfectly as it is expected. Such methods have many disadvantages such as lack of precision, long run time and lack of stability. Therefore, Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) are applied to system in order to optimize the PID controller parameters and improve the performance of PID controller system. The simulation results show that the PSO method is more effective in improving the step response characteristics such as: overshoot, rise time and settling time.

A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system

Introduction. In an electrical power system, the output of the synchronous generators varies due to disturbances or sudden load changes. These variations in output severely affect power system stability and power quality. The synchronous generator is equipped with an automatic voltage regulator to maintain its terminal voltage at rated voltage. Several control techniques utilized to improve the response of the automatic voltage regulator system, however, proportional integral derivative (PID) controller is the most frequently used controller but its parameters require optimization. Novelty. In this paper, the chaotic sequence based on the logistic map is hybridized with particle swarm optimization to find the optimal parameters of the PID for the automatic voltage regulator system. The logistic map chaotic sequence-based initialization and global best selection enable the algorithm to escape from local minima stagnation and improve its convergence rate resulting in best optimal parameters. Purpose. The main objective of the proposed approach is to improve the transient response of the automatic voltage regulator system by minimizing the maximum overshoot, settling time, rise time, and peak time values of the terminal voltage, and eliminating the steady-state error. Methods. In the process of parameter tuning, the Chaotic particle swarm optimization technique was run several times through the proposed hybrid objective function, which accommodates the advantages of the two most commonly used objective functions with a minimum number of iterations, and an optimal PID gain value was found. The proposed algorithm is compared with current metaheuristic algorithms including conventional particle swarm optimization, improved kidney algorithm, and others. Results. For performance evaluation, the characteristics of the integral of time multiplied squared error and Zwe-Lee Gaing objective functions are combined. Furthermore, the time-domain analysis, frequency-domain analysis, and robustness analysis are carried out to show the better performance of the proposed algorithm. The result shows that automatic voltage regulator tuned with the chaotic particle swarm optimization based PID yield improvement in overshoot, settling time, and function value of 14.41 %, 37.91 %, 1.73 % over recently proposed IKA, and 43.55 %, 44.5 %, 16.67 % over conventional particle swarm optimization algorithms. The improvement in transient response further improves the automatic voltage regulator system stability for electrical power systems.

Improved Adaptive PSO-based Gain Tuning for PID Controllers in Utility Boilers

ABSTRACTFuzzy logic control is the most common method utilized to tune proportional integral derivative (PID) controller parameters online. However, proportional integral derivative controllers often perform poorly in the control of nonlinear and/or complicated systems, such as direct current motors, where the model parameters are not exactly known if the scaling factors are not properly selected besides the membership function and rule sets in a fuzzy logic controller design. Finding the most suitable scaling factors for complex systems where the model parameters are not exactly known or nonlinear systems is a challenging task. Furthermore, traditional trial and error techniques of determining appropriate scaling factors are experience based, time consuming, and may not always provide optimal response. In this paper, a particle swarm optimization algorithm is suggested for optimizing the input and output gains of the fuzzy PID controller. The robustness and effectiveness of the suggested controller was validated using MATLAB/Simulink. The performance of the suggested controller is compared with the Ziegler Nichols and Particle Swarm Optimization Algorithm tuned PIDs, and fuzzy PID controllers. The simulation result show that the fuzzy PID controller whose scaling factor was tuned using particle swarm optimization outperforms the other controllers in avoiding disturbance and has a better trajectory tracking capability.

Motion control applications are widely applied in the industrial field with the assistance of electro-hydraulic actuator (EHA) system. EHA system is well known to be disclosed to the parameter variations, disturbances and uncertainties which are affected by the changes in the operating conditions such as friction, internal and external leakage. The complexity and nonlinear characteristic of the EHA system leads to a great challenge for controller development and system modelling. The performance of the utilized controller can be improved in order to achieve its best capability. In this paper, the basic knowledge in optimization of the proportional-integral-derivative (PID) controller through Gradient Descent (GD) and Particle Swarm Optimization (PSO) techniques are discussed. The PID parameters obtained through Ziegler-Nichols (ZN) tuning method has been optimized by using the GD method and is compared with PSO optimization technique via MATLAB/Simulink software. The findings show significant improvement in positioning tracking performance when the developed optimization technique is applied. Therefore, the issues degrading the EHA system performance have been reduced.

The aim of this research is to develop PID (proportional integral derivative) controllers for a variable load two-area intelligent power system (multi-area intelligent power system) in the context of healthcare data analysis. PID controllers are used in the present healthcare business to improve the healthcare industry. It is still debatable how the settings should be changed to create a PID controller that is dependable, adaptable, and effective. In this research, a better PID gain is demonstrated using the particle swarm optimization (PSO) algorithm. The automatic generation control (AGC) for a two-area intelligent power system in healthcare industry has been built using a PID controller and a PSO-based method. To improve frequency response and settling time of healthcare industry equipment in accident and emergency cases (A and E), a PSO-based PID controller generates and transmits the two control signals to the two regions for the algorithm convergence in emergency cases as per MATLAB/Simulink result.

This paper presents a comparison of optimization for vehicle steering controls system simulation using several Artificial Intelligence (AI) for optimizing Proportional Integral Derivative (PID) control parameters to suppress errors on lateral motion and the yaw motion of vehicles. This paper compares five kinds of tuning methods of parameter for PID controller, among other are Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bat Algorithm (BA) and Imperialist Competitive Algorithm (ICA). The vehicles are represented in the model vehicle with 10 degrees of Freedom of vehicle dynamics system. The simulation results show that the PID control tuned by AI in the vehicle steering control system can adjust the plant output to the desired trajectory so that the stability of the vehicle is maintained. Vehicle yaw error and lateral error can be reduced by using ICA to determine PID parameter. The main advantage of proposed optimization is faster and more accurate compared with PID controller. So the error of the controller is reduced too. The results obtained are of vehicle motion can be maintained in accordance with the desired trajectory with smaller error and was able to achieve higher speeds than with the control system using optimized without parameters. This paper only deals with software simulation to proof the effect of AI optimization. The hardware implementation will be investigated in the next future.

Trajectory control of electro-hydraulic position servo system using improved PSO-PID controller

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.

In this study, a new design approach using HFPSO algorithm, which is obtained by hybridization of firefly algorithm (FA) with particle swarm optimization (PSO) algorithm is presented for the adjustment of proportional-integral-derivative (PID) controller parameters in a buck converter. Using the proposed approach, it is aimed to improve the stability profile of the relevant system. In order to demonstrate the superiority of the proposed HFPSO-based PID (HFPSO/PID) controller, stability analyzes in time domain and frequency domain, and disturbance suppression analysis were performed comparatively. The results obtained from analyzes showed that the proposed HFPSO/PID controller is more efficient than the whale optimization algorithm (WOA) based PID (WOA/PID) controller and genetic algorithm (GA) based PID (GA/PID) controller to improve the control performance of the buck converter.

Desalination plants have an important concern regarding controlling the permeate flow rate and pH during operability. This paper proposes the proportional integral derivative (PID) control design using modified Particle Swarm Optimization (PSO) techniques called autonomous groups PSO (AGPSO) in the two-input two-output (TITO) RO desalination plant to control the permeate flow rate and pH. Here, three different versions (AGPSO1, AGPSO2, and AGPSO3) of the AGPSO algorithm are utilized to design PID control for the same TITO plant. In addition, an integral time absolute error (<inline-formula> <tex-math notation="LaTeX">$J_{\mathrm {ITAE}}$ </tex-math></inline-formula>) based objective function is utilized to design a PID controller. The simulation results suggest that the proposed controller designs are flexible, self-tuning, and have stable characteristics, while the AGPSO3-PID control design attained a robust design for optimum tuning compared to existing improved grey wolf optimization PID (IGWO-PID) and other versions of AGPSO based PIDs (AGPSO1-PID, AGPSO2-PID). The design of AGPSO-PID achieved a minimum objective function than existing IGWO-PID and other versions of AGPSO based PIDs &#x007B;AGPSO1-PID, AGPSO2-PID&#x007D;. Finally, the proposed controller designs outperform the existing IGWO-PID design in literature in terms of control performance, demonstrating a precise control for and the improvement of plant performance.

This paper presents the use of particle swarm optimization (PSO) algorithm applied to the optimal design of the 4/2 switched reluctance motor (SRM). The main advantage of designing 4/2 SRM is the robust rotor structure for high speed unidirectional rotating applications such as the air conditioner's blower. In the designing process, the nite element method magnetics (FEMM) is employed to analyze the designing SRM with its optimized parameters given by PSO. This PSO algorithm is ecient and exible. The objective function is based on the ripple torque minimization with respect to rotor node position. The PSO algorithm is described and the FEMM simulation results with detailed analysis will be given for verifying optimal rotor design by PSO algorithm.

This paper presents the use of particle swarm optimization (PSO) algorithm applied to the optimal design of the 4/2 switched reluctance motor (SRM). The main advantage of designing 4/2 SRM is the robust rotor structure for high speed unidirectional rotating applications such as the air conditioner's blower. In the designing process, the finite element method magnetics (FEMM) is also employed to analyze the designing SRM with its optimized parameters given by PSO. This PSO algorithm is efficient and flexible. The objective function is based on the ripple torque minimization with respect to rotor node position. The PSO algorithm is described and the preliminary FEMM simulation results will be given for verifying optimal rotor design by PSO algorithm. In the final paper, the complete FEMM simulation results with detailed analysis will be provided.

Selecting appropriate weighting matrices for desired linear quadratic regulator (LQR) controller design using evolutionary algorithms is presented in this paper. Obviously, it is not easy to determine the appropriate weighting matrices for an optimal control system and a suitable systematic method is not presented for this goal. In other words, there is no direct relationship between weighting matrices and control system characteristics, and selecting these matrices is done by using trial and error based on designer’s experience. In this paper, we use the particle swarm optimization (PSO) method which is inspired by the social behavior of fish and birds in finding food sources to determine these matrices. Stable convergence characteristics and high calculation speed are the advantages of the proposed method. Simulation results demonstrate that in comparison with genetic algorithms (GAs), the PSO method is very efficient and robust in designing of optimal LQR controller. Key words: Linear quadratic regulator (LQR), weighting matrices, particle swarm optimization, genetic algorithm.