Algorithm Based Proportional-integral-derivative Controller Research Articles

Speed Control of Wheeled Mobile Robot by Nature-Inspired Social Spider Algorithm-Based PID Controller

Mobile robot is an automatic vehicle with wheels that can be moved automatically from one place to another. A motor is built in its wheels for mobility purposes, which is controlled using a controller. DC motor speed is controlled by the proportional integral derivative (PID) controller. Kinematic modeling is used in our work to understand the mechanical behavior of robots for designing the appropriate mobile robots. Right and left wheel velocity and direction are calculated by using the kinematic modeling, and the kinematic modeling is given to the PID controller to gain the output. Motor speed is controlled by the PID low-level controller for the robot mobility; the speed controlling is done using the constant values Kd, Kp, and Ki which depend on the past, future, and present errors. For better control performance, the integral gain, differential gain, and proportional gain are adjusted by the PID controller. Robot speed may vary by changing the direction of the vehicle, so to avoid this the Social Spider Optimization (SSO) algorithm is used in PID controllers. PID controller parameter tuning is hard by using separate algorithms, so the parameters are tuned by the SSO algorithm which is a novel nature-inspired algorithm. The main goal of this paper is to demonstrate the effectiveness of the proposed approach in achieving precise speed control of the robot, particularly in the presence of disturbances and uncertainties.

A Behavioral Study of Different Controllers and Algorithms in Real-Time Applications

Systems, generally being nonlinear behaves unexpectedly due to external disturbances. Therefore, operation controllers are needed in tracking, chemical engineering, electrical engineering, and other areas. For continuing quality and productivity of industrial firms, the deployment of industrial robots is increasing. Different structures of fuzzy-Proportional Integral Derivative based trajectories for robot manipulators are designed, for various regular and irregular trajectories. Industrial robot’s performance is compared by Proportional Integral Derivative and Fuzzy- Proportional Integral Derivative controller. For tracking the peak power in a Photo Voltaic system, a nonlinear proportional–integral–derivative controller is applied for rapidly changing environmental conditions, particle swarm optimization based Nonlinear-PID, Maximum Power Point Tracking method offers a clean response for Maximum Power Point Tracking. To maintain constant frequency and power variations in power systems the freedom of a three-degree Proportional Integral Derivative controller was designed. For excitation control in Automatic Voltage Regulators, Proportional Integral Derivative and Proportional Integral Derivative -Acceleration controllers are used, Proportional Integral Derivative -Acceleration has more response speed and more stability and it has a good response time and response of frequency by implementing the Particle Swarms Optimization and Gravitational Search algorithm-based Proportional Integral Derivative controller Automatic Voltage Regulator’s voltage response is effective. This paper deals with studying the behavior of various controllers and algorithms in practical applications, comparative analysis of magnetic levitation system response with different controllers and algorithms, and finding the performance of magnetic levitation systems by using a Particle Swarm Optimization algorithm-based Proportional Integral Derivative controller.

Optimal Power Flow Through Variable Frequency Transformer Using Different Optimization Techniques

For synchronous and asynchronous connectivity of separate power grids, phase shifting transformers (PST), the high-voltage-direct-current (HVDC) converters and variable-frequency-transformer (VFT) are viable options. Natural damping and high overloading capabilities are inherent in the VFT, which is critical for power grid reliability and stability. This article reports the optimal power flow across a power system involving the implementation of several meta-heuristic optimization techniques based control strategy. Several optimization techniques are applied to optimize the gains of proportional-integral-derivative (PID) controller which aids in regulating the system dynamic response. The firefly algorithm based PID controller performs better in contrast to other optimization techniques in improving the system dynamics. The DC motor accustomed with PID controller provides the required torque necessary for power transfer between two connected grids. Sensitivity analysis is exhibited to inspect the strength of developed power system against deviations in certain system parameters. A separate study is done for the asynchronous operation of VFT based power system using firefly algorithm with different DC motor torque. To validate the effect of VFT over HVDC total harmonic distortion analysis has been carried out to check for the harmonic content in the rotor and stator current of VFT.

Load frequency control based on an improved Chimp optimization algorithm using adaptive weight strategy

PurposeThis study aims to implement an improved version of the Chimp algorithm (IChimp) for load frequency control (LFC) of power system.Design/methodology/approachThis work was adopted by IChimp to optimize proportional integral derivative (PID) controller parameters used for the LFC of a two area interconnected thermal system.FindingsThe supremacy of proposed IChimp tuned PID controller over Chimp optimization, direct synthesis-based PID controller, internal model controller tuned PID controller and recent algorithm based PID controller was demonstrated.Originality/valueIChimp has good convergence and better search ability. The IChimp optimized PID controller is the proposed controlling method, which ensured better performance in terms of converging behaviour, optimizing controller gains and steady-state response.

Water cycle algorithm-based PID controller for AVR

PurposeIn a power system, the purpose of automatic voltage regulator (AVR) is the voltage control of synchronous generator. Power system stability and security depends on the AVR.Design/methodology/approachThe present work is concentrated on the precise terminal voltage control of AVR system and simultaneously maintaining the stability of the system. Therefore, an optimal proportional–integral–derivative (PID) controller is proposed. An optimization technique inspired from Mother Nature, i.e. water cycle algorithm (WCA) is used to evaluate the optimum parameter values of PID controller leading to WCA-tuned PID (WCA-PID). The performance of WCA-PID is compared with other controller reported in the literature.FindingsSimulation results show that WCA-PID regulates the terminal voltage more preciously and accurately in comparison to other controller. Further, it is more robust toward parametric uncertainty, set-point tracking and disturbance rejection in comparison to other controller reported in the literature.Originality/valueThe work is not published anywhere else.

Optimal Control of AVR System With Tree Seed Algorithm-Based PID Controller

In this study, an optimal Tree-Seed Algorithm (TSA) algorithm-based Proportional-Integral-Derivative (PID) controller is proposed for automatic voltage regulator (AVR) system terminal tracking problem. PID controller gains Kp, Ki, and Kd are optimized with the proposed TSA algorithm based on different objective functions. The TSA-based optimal PID controller's performance is compared with numerous PID controllers, which were developed using different meta-hermetic optimization algorithms in the literature. Several analysis methods including root locus, bode analysis, robustness, and disturbance rejection are studied and compared with reported works in the literature. It is shown that there is still a research gap to improve the tracking performance of the AVR system due to its importance in electrical systems. According to the obtained comparison results, it has been revealed that the proposed TSA-based PID controller improves tracking properties under load change thus it can be effectively used for synchronous generator automatic voltage regulator terminal voltage stability.

Automatic generation control of a multi-area power system with renewable energy source under deregulated environment: adaptive fuzzy logic-based differential evolution (DE) algorithm

In this paper, an adaptive fuzzy logic-based differential evolution (DE) algorithm is proposed to optimize the gain of proportional–integral–derivative (PID) controllers. Using the proposed controller, the speed regulation parameters are tuned. Here, fuzzy logic is used to generate the controlled range of population set to DE algorithm. So that most favourable offspring population is created and the drawback of conventional DE algorithm is reduced. Using the proposed controller, the approximation errors and the external disturbance effects are minimized. The proposed method is implemented in MATLAB/Simulink working platform and the effectiveness is verified by multi-source two-area power generation system with renewable energy source. Three test cases are used to evaluate the simulation behaviour of proposed method. Simulation results of the tested power systems prove the effectiveness of the proposed load frequency control and proved its superiority over traditional PID controller, DE-tuned PID controller and fuzzy logic—DE algorithm-based PID controller. Comparison results show that proposed method has less overshoot, undershoot and fast settling time.

Analytic hierarchy process based automatic generation control of multi-area interconnected power system using Jaya algorithm

Controller design aspect for automatic generation control (AGC) of multi-area power system is often regarded as an optimization problem with different conflicting objectives. The controller must fulfill these objectives to produce a feasible set of solution as per the requirements of the designer. This paper explores the design and performance analysis of a proportional-integral-derivative (PID) controller employing Jaya algorithm for AGC of an interconnected power system. A filter with derivative term is used with PID controller to minimize the effect of noise in the input signal. The different performance measures considered are integral-time-multiplied-absolute-error (ITAE), the minimization of peak overshoots and the settling times of the deviations in the frequencies and tie-line power. Furthermore, these performance measures i.e. objectives are combined to form a single objective problem using analytic hierarchy process (AHP). Recently proposed Jaya algorithm, which is free from algorithm-specific parameters, is used for minimizing this single objective. The two-area interconnected linear power system model is considered for the simulation process. The simulation studies are carried out under five different cases with diverse sets of disturbances. The efficacy and superiority of the proposed Jaya algorithm based PID controller is shown by comparing simulation results with other algorithms like particle swarm optimization (PSO), differential evolution (DE), Nelder-Mead simplex (NMS), elephant herding optimization (EHO) and teacher learner based optimization (TLBO). Time-domain simulations and statistical analysis are presented to validate the effectiveness of the proposed controller.

Genetic Algorithm Based PID Controller Tuning Approach for Continuous Stirred Tank Reactor

Genetic algorithm (GA) based PID (proportional integral derivative) controller has been proposed for tuning optimized PID parameters in a continuous stirred tank reactor (CSTR) process using a weighted combination of objective functions, namely, integral square error (ISE), integral absolute error (IAE), and integrated time absolute error (ITAE). Optimization of PID controller parameters is the key goal in chemical and biochemical industries. PID controllers have narrowed down the operating range of processes with dynamic nonlinearity. In our proposed work, globally optimized PID parameters tend to operate the CSTR process in its entire operating range to overcome the limitations of the linear PID controller. The simulation study reveals that the GA based PID controller tuned with fixed PID parameters provides satisfactory performance in terms of set point tracking and disturbance rejection.

Evolutionary algorithm-based PID controller tuning for nonlinear quarter-car electrohydraulic vehicle suspensions

The basic challenge associated with the design of vehicle suspension system is the attainment of an optimal trade-off between the various design objectives. This study presents the design of proportional-integral-derivative (PID) controller for a quarter-car active vehicle suspension system (AVSS) using evolutionary algorithms (EA) such as the particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution (DE). Each of the EA-based PID controllers showed overall improvement in suspension travel, ride comfort, settling time and road holding from the manually tuned controller and the passive vehicle suspension system. These improvements were, however, achieved at the cost of increased actuator force, power consumption and spool-valve displacement. DE-optimized PID control resulted in the best minimized suspension performance, followed by the GA and PSO, respectively. Frequency-domain analysis showed that all the signals were attenuated within the whole body vibration frequency range and the EA-optimized controllers had RMS frequency weighted body acceleration of the vehicle within allowable limits for vibration exposure. Robustness analysis of the DE-optimized PID-controlled AVSS to model uncertainties is carried out in the form of variation in vehicle sprung mass loading, tyre stiffness and speed.

Tabu search algorithm based PID controller tuning for desired system specifications

This paper presents a tuning approach based on a tabu search algorithm (TSA) to obtain the optimal proportional-integral-derivative (PID) controller parameters in order to achieve a desired transient response. TSA is used to determine the main parameters of the PID controller. The performance of the PID controlled system is examined by considering the characteristics of the step response of the plant. Simulation results demonstrate that the tabu algorithm based approach is one of the useful methods for PID controller tuning, and using by the presented method, performance of the controlled system can be significantly improved according to the given control specifications.

New tuning method for PID controller

Abstract In this paper, a tuning method for proportional-integral-derivative (PID) controller and the performance assessment formulas for this method are proposed. This tuning method is based on a genetic algorithm based PID controller design method. For deriving the tuning formula, the genetic algorithm based design method is applied to design PID controllers for a variety of processes. The relationship between the controller parameters and the parameters that characterize the process dynamics are determined and the tuning formula is then derived. Using simulation studies, the rules for assessing the performance of a PID controller tuned by the proposed method are also given. This makes it possible to incorporate the capability to determine if the PID controller is well tuned or not into an autotuner. An autotuner based on this new tuning method and the corresponding performance assessment rules is also established. Simulations and real-time experimental results are given to demonstrate the effectiveness and usefulness of these formulas.