Using PSO and GA for Optimization of PID Parameters

The steam turbine control system is strongly non-linear in all operating conditions. Proportional-Integral-Derivative (PID) controller that is currently used in control systems of many types of equipment is not considered highly precision for turbine speed control system. A fine tuning of the PID controller by some optimization technique is a desired objective to maintain the precise speed of the turbine in a wide range of operating conditions. This Paper evaluates the feasibility of the use of Particle Swarm Optimization (PSO) method for determining the optimal Proportional-Integral-Derivative (PID) controller parameters for steam turbine control. The turbine speed control is modelled in SimulinkTM with PID controller and the PSO algorithm is implemented in MATLAB to optimize the PID function. The PSO optimization technique is also compared with Genetic Algorithm (GA) and it is validated that PSO based controller is more efficient in reducing the steady-states error; settling time, rise time, and overshoot limit in speed control of the steam turbine control.

Study the optimization of aero-engine PID (Proportional-Integral-Derivative) controller parameters. In order to improve the tuning accuracy on aero-engine PID control parameters for the optimal solution, this paper presents a genetic algorithm-based PID parameter tuning method. Because the choice of crossover and mutation probabilities in genetic algorithm have a significant impact on the convergence speed and stability of the control system, genetic algorithm is adopted of which the crossover and mutation probability can automatically change with the fitness. Simulation results show that after variable crossover and mutation probability adaptive genetic algorithm to optimize PID control parameters, the average convergence algebra is significantly reduced and the overall control performance of the system is better. Aero engine work process is complex systems that big lag, nonlinear and multi-coupling throughout the flight envelope, Aero engine aerodynamic thermodynamic processes will occur a very big change, with the change of environmental conditions and flight status (airspeed, Mach number, etc.), so be quick, effective control is particularly important. PID control is one of the earliest developed control strategy, due to its simple structure, robustness, high reliability, easy to implement and other characteristics, widely used in process control and motion control, especially in the determination system that precise mathematical model can be established. Three parameters of proportional coefficient ( Kp ), integral time ( Ki ) and derivative time ( Kd ) tuning are direct influenced the control effect of the PID controller, the optimizing of PID controller parameters directly affect the control effect of control system. At present, there are many ways to optimize the PID parameters, such as Ziegler-Nichols method, indirect optimization method, the gradient method, climbing method, etc. The designer of these methods, mainly through the Mathematical model of controlled object, combined with practical experience, through repeated on-site equipment debugging obtained. Because these tuning method are relatively complicated, and affected by human factors, the final parameters of being got are not usually the optimal solution. The traditional tuning method based on a specific model is not applicable, for such aero engine as complex nonlinear systems. Therefore you can take advantage of intelligent control technology for optimizing PID parameters in order to achieve optimal control. Genetic Algorithm (GA) is an optimization method for parallel simulation of natural genetic mechanisms and biological evolution and the formation of a random search, survival of the fittest principle of biological evolution exist in nature, was introduced to optimize the parameters of a coded series groups.

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.

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.

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

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.

Direct Current (DC) motors are extensively used in various applications due to their versatile and precise control capabilities. However, they face operational challenges such as speed instability and sensitivity to load variations and external disturbances. This study compares the performance of several advanced control methods—Proportional Integral Derivative (PID), Fractional Order PID (FOPID), Integral State Feedback (ISF), Sliding Mode Control (SMC), and Fuzzy Logic Controller (FLC) for DC motor control. Particle Swarm Optimization (PSO) is employed to optimize the tuning parameters of PID, FOPID, ISF, and SMC controllers, while FLC is implemented without optimization. The simulation results indicate that the PSO-FOPID controller exhibits the best overall performance, characterized by the fastest rise and settling times and the lowest ITSE, despite a minor overshoot. The PSO-PID controller also performs well, with fast response times, although it is less efficient in terms of settling time and ITSE compared to PSO-FOPID. The OBL/HGSO-PID controller, while stable and overshoot-free, has a slower response. The PSO-ISF controller shows the highest stability with the lowest SSE values, making it suitable for applications requiring high stability. The PSO-SMC controller demonstrates good stability but is slightly slower than PSO-ISF. The FLC controller, however, performs the worst, with significant overshoot and long recovery times, making it unsuitable for fast and precise control applications. The robustness analysis under varying motor parameters further confirms the superiority of the PSO-FOPID controller, which outperforms OBL/HGSO and OBL-MRFO-SA optimizations across both PID and FOPID controllers, making it the most effective solution for applications requiring high precision and rapid response.

It is well known that the power system controller designed by H∞ control is complicated, high order and impractical. In power system applications, practical structures such as proportional integral derivative (PID) etc., are widely used, because of their simple structure, less number of tuning parameters and low-order. However, tuning of controller parameters to achieve a good performance and robustness is based on designer's experiences. To overcome this problem, this paper proposes a fixed structure robust H∞ loop shaping control to design Static Var Compensator (SVC) and Automatic Voltage Regulator (AVR) for robust stabilization of voltage fluctuation in an isolated wind-diesel hybrid power system. The structure of the robust controller of SVC and AVR is specified by a PID controller. In the system modeling, a normalized coprime factorization is applied to represent possible unstructured uncertainties in the power system such as variation of system parameters, generating and loading conditions etc. Based on the H∞ loop shaping, the performance and robust stability conditions are formulated as the optimization problem. The particle swarm optimization is applied to solve for PID control parameters of SVC and AVR simultaneously. Simulation studies confirm the control effect and robustness of the proposed control.

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.

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

As an indispensable constituent of the premises of highly precious control of vertical takeoff and landing (VTOL) aircrafts, parameter identification has received an increasingly considerable attention from academic community and practitioners. In an effort to tackle the matter better, we herewith put forward a PID controlling particle swarm optimizer (PSO) which we call the proportional integral derivative (PID) controller inspired particle swarm optimizer (P idSO). It uses a novel evolutionary strategy whereby a specified PID controller is used to improve particles' local and global best positions information. Empirical experiments were conducted on both analytically unimodal and multimodal test functions. The experimental results demonstrate that PidSO features better search effectiveness and efficiency in solving most of the multimodal optimization problems when compared with other recent variants of PSOs, and its performance can be upgraded by adopting proper control law based controllers. Moreover, PidSO, together with least squares (LS) method and genetic algorithm (GA), is applied to the parameter estimation of the VTOL aircraft. In comparison with LS method and GA, PidSO is a more effective tool in estimating the parameters of the VTOL aircraft.

This paper demonstrates the design and analysis of automatic generation control using intelligent genetic algorithm tuned fuzzy based controller. A two area thermal power system simulated for four different scenarios considers a reheat steam turbine in each area with Generator rate constraints. The Integral Time Squared Error (ITSE) employed to get an objective function for the optimization of controller gains. The simulation results compared with the conventional Proportional Integral Derivative (PID) controller, Genetic Algorithm (GA) tuned PID controller and GA tuned Fuzzy PID controller. The proposed GA tuned Fuzzy based PID Controller can generate the best performance for peak overshoot, undershoot and settling time with step load disturbances. Robustness of the performance of the proposed controller provided with system parametric uncertainties.

In this study, adaptive artificial intelligence control algorithms were designed using the particle swarm optimisation (PSO) method for safe take-off and landing of aircraft. The performance of the designed control algorithms, which were applied to the control of the aircraft’s pitch angle, was compared with each other and with studies in the literature. Integrated squared error (ISE) was used as a fitness function in the design of PSO-based proportional–integral–derivative (PSO-PID), PSO-based fuzzy logic controller (PSO-FLC) and PSO-based self-adaptive fuzzy-PID (PSO-SAF-PID) controllers. Performance comparisons between the designed controllers were made based on the transient behaviours such as overshoot, rise time and settling time, and statistical error analyses including mean squared error (MSE), mean absolute error (MAE), ISE and integrated absolute error (IAE). It was observed that the performance of the PID controller, FLC and SAF-PID controllers improved significantly with the application of the PSO method. First, the mathematical model of the system was derived. Second, traditional control methods (PID control algorithm) and artificial intelligence control methods (FLC and SAF-PID type control algorithms) were designed to control the modelled system. Then, to improve the performance of these designed control algorithms, the optimal control parameters of the PID algorithm were determined using the PSO method. At the same time, membership function weight coefficients of the FLC algorithm and SAF-PID type control algorithm were determined using the PSO algorithm. Determining the control parameters and membership function coefficients at optimum values significantly affects the performance of control algorithms. Optimal control parameters and membership coefficients have significantly improved control performance. Furthermore, tests performed on transfer functions with different system parameters have shown that the PSO-SAF-PID controller is robust.