Optimal Proportional-integral-derivative Controller Parameters Research Articles

Real Time Implementation for Tuning PID Controller Based on Advanced Optimization Techniques for Micro Robotics System

The process of tuning the PID controller’s parameters is considered to be a difficult task. Several approaches were developed in the past known as conventional methods. One of these methods is the Ziegler and Nichols that relies on accurate mathematical model of the linear system, but if the system is complex the former method fails to compute the parameters of PID controller. To overcome this problem, recently there exist several techniques based on artificial intelligence such as optimization techniques. The optimization techniques does not require any mathematical model and they are considered to be easy to implement on any system even if it complex, can reach optimal solutions on the parameters. In this study, a new approach to control the position of the micro-robotics system proportional - integral - derivative (PID) controller is designed and a recently developed algorithm based on optimization is known as the sparrow search algorithm (SSA). By using the sparrow search algorithm (SSA), the optimal PID controller parameters were obtained by minimizing a new objective function, which consists of the integral square Time multiplied square Error (ISTES) performance index. The effectiveness of the proposed SSA-based controller was verified by comparisons made with the Sine Cosine algorithm (SCA), and Flower pollination algorithm (FPA) controllers in terms of time and frequency response. Each control technique will be applied to the identified model (simulation results) using MATLAB Simulink and the laboratory setup (experimental results) using LABVIEW software. Finally, the SSA showed the highest performance in time and frequency responses.

Steam Turbine Governor (STG) Controller Design Based on Particle Swarm Optimization (PSO)

A reheat and non-reheat steam turbines governor is a component of the turbine control system that regulates rotational speed in response to changing load conditions. The governor output signal manipulates the position of a steam inlet valve or nozzles which in turn regulates the steam flow to the turbine. The application of PID (proportional integral derivative (controller has been widely used from small industry to high technology industry. Tuning the parameters of a PID controller is very important in PID control, Ziegler-Nichols method also used to tune the coefficients of a PID controller. The compensation has been proposed to be 4%, 6%, 8%, 10% and 15% of the actual load.This paper evaluates the feasibility of using the Particle Swarm Optimization (PSO) method for determining the optimal PID controller parameters for steam turbine speed control. The PSO optimization technique is compared with Ziegler-Nichols method and (PID) controllers. It is validated that PSO based controller is more efficient in reducing the steady-states error, settling time, rising time, and overshooting limit in speed control of the steam turbine control. In which the maximum peak overshoot has been reduced from 44.8% in case of the original system without any controller to 3.45% for the case of PSO based controller and that for 15% load change.

Tuning optimal PID controllers for open loop unstable first order plus time delay systems by minimizing ITAE criterion

The present paper gives an optimal tuning procedure for design of Proportional Integral Derivative (PID) controllers for open loop unstable First Order plus Time Delay systems. Estimation of the optimal PID controller parameters: kc, τI and τD is carried out by minimizing the time integral performance criteria:ITAE(Integral of Time-weighted Absolute Error) for a setpoint tracking problem. Results of the simulation for linear transfer functions and nonlinear isothermal CSTR reveals the proposed tuning approach provides enhanced performance for the set-point tracking and disturbance rejection with improved time domain specifications. To evaluate the superiority of the proposed method over others, robustness analysis with complimentary sensitivity function is carried out.

Application of artificial intelligence techniques for LFC and AVR systems using PID controller

Development of electrical power systems led to search for a new mathematical methods to find the values of PID (Proportional-Integral-Derivative) controller. The goal of the paper is to improve the performance of the overall system, through improved the frequency deviation and the voltage deviation characteristics using PID controller, so in this paper are proposed three methods of artificial intelligence techniques for designing the optimal values of PID controller of Load-Frequency-Control (LFC) and Automatic-Voltage-Regulator (AVR), the first is the Firefly Algorithm (FA), the second is the Genetic Algorithm (GA) and the third is the Particle Swarm Optimization (PSO), in addition to these three methods use the conventional (Ziegler–Nichols, Z-N). The FA, GA and PSO are used to obtain the optimal parameters of PID controller based on minimized different various indices as a fitness function, these fitness functions namely Integral-Time-Absolute-Error (ITAE) and Integral-Time-Square-Error (ITSE). Comparison between the results obtained show that FA has better performance to control of frequency deviation and terminal voltage than GA and PSO, so the results observed the FA is more effectual and reliable to determine the optimal values of PID controller.

Design and implementation of EP-based PID controller for chaos synchronization of Rikitake circuit systems

This article addresses an evolutionary programming (EP) algorithm technique-based and proportional-integral-derivative (PID) control methods are established to guarantee synchronization of the master and slave Rikitake chaotic systems. For PID synchronous control, the evolutionary programming (EP) algorithm is used to find the optimal PID controller parameters kp, ki, kd by integrated absolute error (IAE) method for the convergence conditions. In order to verify the system performance, the basic electronic components containing operational amplifiers (OPAs), resistors, and capacitors are used to implement the proposed chaotic Rikitake systems. Finally, the experimental results validate the proposed Rikitake chaotic synchronization approach.

Reinforcement learning based PID controller design for LFC in a microgrid

PurposeThis paper aims to propose an improved reinforcement learning-based fuzzy-PID controller for load frequency control (LFC) of an island microgrid.Design/methodology/approachTo evaluate the performance of the proposed controller, three different types of controllers including optimal proportional-integral-derivative (PID) controller, optimal fuzzy PID controller and the proposed reinforcement learning-based fuzzy-PID controller are compared. Optimal PID controller and classic fuzzy-PID controller parameters are tuned using Non-dominated Sorting Genetic Algorithm-II algorithm to minimize overshoot, settling time and integral square error over a wide range of load variations. The simulations are carried out using MATLAB/SIMULINK package.FindingsSimulation results indicated the superiority of the proposed reinforcement learning-based controller over fuzzy-PID and optimal-PID controllers in the same operational conditions.Originality/valueIn this paper, an improved reinforcement learning-based fuzzy-PID controller is proposed for LFC of an island microgrid. The main advantage of the reinforcement learning-based controllers is their hardiness behavior along with uncertainties and parameters variations. Also, they do not need any knowledge about the system under control; thus, they can control any large system with high nonlinearities.

An adaptive chaos particle swarm optimization for tuning parameters of PID controller

SummaryAn adaptive chaos particle swarm optimization (ACPSO) is presented in this paper to tune the parameters of proportional‐integral‐derivative (PID) controller. To avoid the local minima, we introduced a constriction factor. Meanwhile, the chaotic searching is combined with the particle swarm optimization to improve the ability of the proposed algorithm. A series of experiment is performed on 6 benchmark functions to confirm its performance. It is found that the ACPSO can get better solution quality in solving the global optimization problems and avoiding the premature convergence. Based on it, the proposed algorithm is applied to tune the PID controller's parameters. The performances of the ACPSO are compared with different inspired algorithms, and these results show that the ACPSO is more robust and efficient when it is used to find the optimal parameters of PID controller.

Integrated PLC-fuzzy PID Simulink implemented AVR system

Abstract Improving the transient response of power generation systems using automation control in a precise manner is the key issue. We design a fuzzy proportional integral derivative (PID) controller using Matlab and programmable logic controllers (PLCs) for a set point voltage control problem in the automatic voltage regulator (AVR) system. The controller objective is to maintain the terminal voltage all the time under any loads and operational conditions by attaining to the desired range via the regulation of the generator exciter voltage. The main voltage control system uses PLCs to implement the AVR action. The proposed fuzzy controller combines the genetic algorithm (GA), radial-basis function network (RBF-NN) identification and fuzzy logic control to determine the optimal PID controller parameters in AVR system. The RBF tuning for various operating conditions is further employed to develop the rule base of the Sugeno fuzzy system. The fuzzy PID controller (GNFPID) is further designed to transfer in PLCs (STEP 75.5) for implementing the AVR system with improved system response. An inherent interaction between two generator terminal voltage control and excitation current is revealed. The GNFPID controller configures the control signal based on interaction and there by reduces the voltage error and the oscillation in the terminal voltage control process. We achieve an excellent voltage control performance by testing the proposed fuzzy PID controller on a practical AVR system in synchronous generator for improve the transient response.

Optimal PID control of DC motor with ABC and PSO algorithms

Proportional-integral-derivative (PID) control is widely used in DC motor system, because of its simple structure, high reliability and so on. However, the parameters of PID controller are commonly tuned by trial and error which also has large overshoot phenomenon for nonlinear and time-varying systems. In order to overcome the deficiency of the PID controller whose parameters are difficult to adjust, two swarm intelligence algorithms - artificial bee colony (ABC) and particle swarm optimisation (PSO) - have been proposed to estimate the optimal parameters of PID controller for speed control. The simulations have been done and results show that ABC and PSO algorithms have stable convergence characteristic and good computational ability, and they are effective and easily implemented methods for optimal tuning of PID parameters.

Sugeno fuzzy PID tuning, by genetic-neutral for AVR in electrical power generation

We report a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an automatic voltage regulator (AVR) system, using a combined genetic algorithm (GA), radial basis function neural network (RBF-NN) and Sugeno fuzzy logic approaches. GA and a RBF-NN with a Sugeno fuzzy logic are proposed to design a PID controller for an AVR system (GNFPID). The problem for obtaining the optimal AVR and PID controller parameters is formulated as an optimization problem and RBF-NN tuned by GA is applied to solve the optimization problem. Whereas, optimal PID gains obtained by the proposed RBF tuning by genetic algorithm for various operating conditions are used to develop the rule base of the Sugeno fuzzy system and design fuzzy PID controller of the AVR system to improve the system's response (∼0.005s). The proposed approach has superior features, including easy implementation, stable convergence characteristic, good computational efficiency and this algorithm effectively searches for a high-quality solution and improve the transient response of the AVR system (7E−06). Numerical simulation results demonstrate that this is faster and has much less computational cost as compared with the real-code genetic algorithm (RGA) and Sugeno fuzzy logic. The proposed method is indeed more efficient and robust in improving the step response of an AVR system.

Design of Intelligent PID Controller for AVR System Using an Adaptive Neuro Fuzzy Inference System

This paper presents a hybrid approach involving signal to noise ratio (SNR) and particle swarm optimization (PSO) for design the optimal and intelligent proportional-integral-derivative (PID) controller of an automatic voltage regulator (AVR) system with uses an adaptive neuro fuzzy inference system (ANFIS). In this paper determined optimal parameters of PID controller with SNR-PSO approach for some events and use these optimal parameters of PID controller for design the intelligent PID controller for AVR system with ANFIS. Trial and error method can be used to find a suitable design of anfis based an intelligent controller. However, there are many options including fuzzy rules, Membership Functions (MFs) and scaling factors to achieve a desired performance. An optimization algorithm facilitates this process and finds an optimal design to provide a desired performance. This paper presents a novel application of the SNRPSO approach to design an intelligent controller for AVR. SNR-PSO is a method that combines the features of PSO and SNR in order to improve the optimize operation. In order to emphasize the advantages of the proposed SNR-PSO PID controller, we also compared with the CRPSO PID controller. The proposed method was indeed more efficient and robust in improving the step response of an AVR system and numerical simulations are provided to verify the effectiveness and feasibility of PID controller of AVR based on SNRPSO algorithm. DOI: http://dx.doi.org/10.11591/ijece.v4i5.6521

Position Control of a Single Arm Manipulator Using GA-PID Controller

This paper demonstrates in detail how to employ the genetic algorithm (GA) optimization technique to search efficiently the optimal proportional-integral-derivative (PID) controller gains to control the position of a fixed arm manipulator system. The system identification technique is used to find an equivalent transfer function for the system under study. GA is applied off-line to find the optimal PID controller parameters based on the identified model. The experimental and simulation results of the actual system and its identified model under the influence of the optimal PID controller are explored. The proposed approach shows superior features, including easy implementation, stable convergence characteristic, and good computational efficiency.

Optimal PID controller parameters for first order and second order systems with time delay using a connectionist approach

This article focuses on developing a neural controller based on a proportional integral derivative (PID) law. The main objective is to show that using neural networks represents a better alternative compared with other conventional models that were used in the past to express the tuning parameters as a function of process parameters such as process gain (K P ), process time constants (τ1, τ2, etc.,) and process time delay (θ). A Levenberg-Marquardt backward propagation algorithm is used to get the required PID parameters corresponding to different types of processes. In the present study, the PID parameters for the first order plus time delay (FOPTD) and the second order plus time delay (SOPTD) are obtained. It was observed that very high R 2 values between the actual PID parameters and the parameters obtained from the NN were achieved. Furthermore, the performance of PID control systems with FOPTD and SOPTD processes are applied on a number of case studies and compared with the conventional Zeigler-Nichols (Z-N) method of tuning PID controllers. Using the proposed NN controller was found to be efficient and the current method could be of potential use in control systems with gain scheduling also where the controller parameters are tuned according to a continuous gain schedule variable that changes based on different levels of process parameters.

Real-coded genetic algorithm and fuzzy logic approach for real-time tuning of proportional–integral–derivative controller in automatic voltage regulator system

Optimal tuning of proportional–integral–derivative (PID) controller parameters is necessary for the satisfactory operation of automatic voltage regulator (AVR) system. This study presents a combined genetic algorithm (GA) and fuzzy logic approach to determine the optimal PID controller parameters in AVR system. The problem of obtaining the optimal PID controller parameters is formulated as an optimisation problem and a real-coded genetic algorithm (RGA) is applied to solve the optimisation problem. In the proposed RGA, the optimisation variables are represented as floating point numbers in the genetic population. Further, for effective genetic operation, the crossover and mutation operators which can deal directly with the floating point numbers are used. The proposed approach has resulted in PID controller with good transient response. The optimal PID gains obtained by the proposed GA for various operating conditions are used to develop the rule base of the Sugeno fuzzy system. The developed fuzzy system can give the PID parameters on-line for different operating conditions. The suitability of the proposed approach for PID controller tuning has been demonstrated through computer simulations in an AVR system.

Abstract: P243 ROLE OF IMMUNOGLOBULIN-LIKE CELL ADHESION MOLECULE NECL-5 IN VEGF-INDUCED ANGIOGENESIS

This article addresses an evolutionary programming (EP) algorithm technique-based and proportional-integral-derivative (PID) control methods are established to guarantee synchronization of the master and slave Rikitake chaotic systems. For PID synchronous control, the evolutionary programming (EP) algorithm is used to find the optimal PID controller parameters kp, ki, kd by integrated absolute error (IAE) method for the convergence conditions. In order to verify the system performance, the basic electronic components containing operational amplifiers (OPAs), resistors, and capacitors are used to implement the proposed chaotic Rikitake systems. Finally, the experimental results validate the proposed Rikitake chaotic synchronization approach.

A Particle Swarm Optimization Approach for Optimum Design of PID Controller in AVR System

In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an AVR system using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO method to search efficiently the optimal PID controller parameters of an AVR system. The proposed approach had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency. Fast tuning of optimum PID controller parameters yields high-quality solution. In order to assist estimating the performance of the proposed PSO-PID controller, a new time-domain performance criterion function was also defined. Compared with the genetic algorithm (GA), the proposed method was indeed more efficient and robust in improving the step response of an AVR system.