Proportional Integral Derivative Controller Tuning Research Articles

Application of fractional order PID controller for AGC under deregulated environment

In this paper, a fractional order proportional integral derivative (FOPID) controller for multiarea automatic generation control (AGC) scheme has been designed. FOPID controller has five parameters and provides two additional degrees of flexibility in comparison to a proportional integral derivative (PID) controller. The optimal values of parameters of FOPID controller have been determined using Big Bang Big Crunch (BBBC) search algorithm. The designed controller regulates real power output of generators to achieve the best dynamic response of frequency and tie-line power on a load perturbation. The complete scheme for designing of the controllers has been developed and demonstrated on multiarea deregulated power system. The performance of the designed FOPID controllers has been compared with the optimally tuned PID controllers. It is observed from the results that the FOPID controller shows a considerable improvement in the performance as compared to the conventional PID controller.

Incremental Q-learning strategy for adaptive PID control of mobile robots

Expert and intelligent systems are being developed to control many technological systems including mobile robots. However, the PID (Proportional-Integral-Derivative) controller is a fast low-level control strategy widely used in many control engineering tasks. Classic control theory has contributed with different tuning methods to obtain the gains of PID controllers for specific operation conditions. Nevertheless, when the system is not fully known and the operative conditions are variable and not previously known, classical techniques are not entirely suitable for the PID tuning. To overcome these drawbacks many adaptive approaches have been arisen, mainly from the field of artificial intelligent. In this work, we propose an incremental Q-learning strategy for adaptive PID control. In order to improve the learning efficiency we define a temporal memory into the learning process. While the memory remains invariant, a non-uniform specialization process is carried out generating new limited subspaces of learning. An implementation on a real mobile robot demonstrates the applicability of the proposed approach for a real-time simultaneous tuning of multiples adaptive PID controllers for a real system operating under variable conditions in a real environment.

Automatic tuning of PID controllers using frequency sampling filters

This study proposes new proportional-integral-derivative (PID) controller auto-tuners using frequency sampling filters (FSFs) for the estimation of plant frequency response information under relay feedback control. It differs from the existing auto-tuners in the estimation of plant frequency response information and its utilisation in the frequency domain-based PID controller design. A desired closed-loop time constant is selected by the user as the scaling factor of the estimated open-loop time constant. Simulation examples are used to demonstrate the efficacy of the auto-tuner and its characteristics in disturbance rejection and reference following, indicating that the auto-tuner produces satisfactory closed-loop control performance for the commonly encountered systems in process control. In addition, Monte Carlo simulation studies are used to demonstrate that the FSF model-based algorithm requires not only shorter experimental time to achieve higher accuracy in estimation but also provides much improvement over the traditional Fourier analysis-based approaches when disturbances are encountered during the relay experiments.

Tuning of PID Controllers for Unstable Systems Using Direct Synthesis Method

Tuning of proportional integral derivative (PID) controllers for unstable first-order plus time delay systems and unstable second-order plus time delay systems is proposed using the direct synthesis (DS) method. In this method, the mode of PID controller is selected and the characteristic equation of the unstable process with the PID controller is compared with the desired characteristic equation to derive PID controller parameters. The performance of the proposed controller is compared with the recently reported DS method, internal model control method and setpoint overshoot method (SOM) to demonstrate the efficiency of the controller. The performance comparison is made in terms of integral absolute error (IAE). The smooth functioning of controller is determined in terms of total variation (TV) (input usage). The controllers designed by the present method are applied to many transfer function models and the nonlinear model of isothermal continuous stirred tank reactor (CSTR) carrying out an enzymatic reaction with nonlinear kinetics and jacketed CSTR carrying out a first-order reaction. The robustness of the controller is given in terms of Ms (maximum magnitude of the sensitivity function), phase margin and gain margin.

Implementation and test of a new autotuning method for PID controllers of TITO processes

In this paper we present a new closed-loop automatic tuning methodology for decentralised proportional–integral–derivative (PID) controllers applied to two-inputs–two-outputs (TITO) non-singular processes. The main feature of the technique is the estimation of the process parameters by evaluating two closed-loop set-point step responses with two roughly tuned PID controllers already in place. Then, once a process model has been obtained, the PID controllers can be retuned by using any rule available in the literature and suitable for the application. Simulation examples, comparative results with other works and a real application on a neonatal incubator prototype are given to illustrate the methodology and to show its effectiveness.

Genetically tuned fuzzy PID controller in two area reheat thermal power system

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.

A novel HTD-CS based PID controller tuning method for time delay continuous systems with multi-objective and multi-constraint optimization

Currently, there exist many traditional PID (Proportional-Integral-Derivative) controller tuning methods for the unconstrained single-objective optimization problems. However, when dealing with multi-objective optimization problems, the traditional methods face many theoretical challenges. Furthermore, the situations may be even worse in the presence of multi-constraint requirements. In the view of above situations, by applying the heavy-tailed-distribution (HTD) sequences into the basic cuckoo search (CS) algorithm, we firstly propose a novel HTD-CS-based PID controller tuning method for the multi-objective and multi-constraint optimization problems. In this proposed swarm intelligent optimization algorithm, the introduction of HTD sequences represents a novel heavy-tailed search strategy in order to improve the search performance. Thus, in terms of achieving global optimization, the modified HTD-CS algorithm can provide more accurate PID parameter estimates. The simulation experiments verify that the proposed algorithm is quite efficient.

Multivariable Online Adaptive PID Controller for Plasma Current, Shape, and Position in Tokamaks

Nuclear fusion is one of the newest and most promising clean and safe energies hence, it imposes a new research area of control. In this paper, the design of a multivariable adaptive proportional-integral-derivative (PID) controller for the control of the plasma current, shape and position to ensure the safe operation of the fusion reactor is successfully developed. The recursive least square algorithm is used in an alternative way as an adaptation mechanism for tuning PID controller gains. Since stability is a vital issue in the evaluation of control systems, therefore stability analysis of the proposed controller is developed using the Lyapunov stability theory. The main objective of plasma current, shape and position controller in fusion reactors is to improve the stability and the performance of tokamak magnetic systems without contravening the limits imposed by the actuating coils voltages physical limitations. The proposed APID (adaptive PID) controller tunes online its parameters to cope with the presence of the disturbance or any parameters changes occur during the operation. The results of the proposed APID on a simulation code of a tokamak show a noteworthy improvement with respect to those obtained with other control techniques in the cases of changing the initial controller gains, adding disturbance signal and variation in the reactor model parameters.

Optimal tuning of PID controller with time delay system using CS and SRMR technique

This paper proposed a cuckoo search (CS) algorithm and self reunion multiple regression (SRMR) method based optimal tuning of proportional integral derivative (PID) controller with high order time delay system. The main part of this projected technique is in hybridizing SRMR with CS algorithm and so enhanced searching ability, random reduction and mitigated difficulty can be achieved. The main goal of this paper is to acquire the optimal control parameter of the PID controller depend on the outcome deviation of the higher order time delay system. In this projected technique is used to derive the SRMR function from the higher order system output parameters. Here, the CS algorithm depends on the least square error (LSE) minimization function is used to optimize the SRMR coefficients. The projected technique is helps to achieve the best fit parameter of the higher order system and improving the consistency of the system. The projected technique is applied in the MATLAB/Simulink platform and examined under various types of higher order time delay systems. The efficiency of the projected technique is proved by the comparative analysis with the existing techniques. The differentiation results consistently prove the efficiency of the projected technique and verified its potential to resolve the related problems.

VELOCITY CONTROL OF A UNICYCLE TYPE OF MOBILE ROBOT USING OPTIMAL PID CONTROLLER

A unicycle model of control a mobile robot is a simplified modeling approach modified from the differential drive mobile robots. Instead of controlling the right speed, and the left speed, of the drive systems, the unicycle model is using and as the controller parameters. Tracking is much easier in this model. In this paper, the dynamic of the robot parameter is controlled using two blocks of Proportional-Integral-Derivative (PID) controllers. The gains of the PID are firstly determined using particle swarm optimization (PSO) in offline mode. After the optimal gain is determined, the tracking of the robot’s trajectory is performed online with optimal PID controller. The achieved results of the proposed scheme are compared with those of dynamic model optimized with genetic algorithm (GA) and manually tuned PID controller gains. In the algorithm, the control parameters are computed by minimizing the fitness function defined by using the integral absolute error (IAE) performance index. The simulation results obtained reveal advantages of the proposed PSO-PID dynamic controller for trajectory tracking of a unicycle type of mobile robot. A MATLAB-Simulink program is used to simulate the designed system and the results are graphically plotted. In addition, numerical simulations using 8-shape as a reference trajectory with several numbers of iterations are reported to show the validity of the proposed scheme.

PERFORMANCE COMPARISON OF PID TUNING BY USING ZIEGLER-NICHOLS AND PARTICLE SWARM OPTIMIZATION APPROACHES IN A WATER CONTROL SYSTEM

Tuning the gain of Proportional-Integral Derivative (PID) controller in a process control system is exceptionally paramount since correct tuning would help a control process response without steady state error and overshoot. As the conventional gain tuning of PID controller, the Ziegler-Nichols (ZN) approach generally delivers an enormous overshoot; therefore, current heuristics approach, namely Particle Swarm Optimization (PSO) is utilized.A tuning problem of a single tank water level dynamic control system is presented.The best PID controller parameters are determined by using the ZN and PSO approaches. Comparisons of process time performance and the performance measurement of the system are made in order to evaluate both approaches in terms of their step response through the MATLAB/Simulink platform.The results demonstrate that the PSO approach produces promising results with lower overshoot compared to the ZN approach.It is found that the PSO approach would be advantageous for the industries related to single tank water control system for a better PID gain tuning.

Paper Machine Industrial Analysis on Moisture Control Using BF-PSO Algorithm and Real Time Implementation Setup through Embedded Controller

Proportional Integral Derivative (PID) controller tuning is an area of interest for researchers in many areas of science and engineering. This paper presents a new algorithm for PID controller tuning based on a combination of bacteria foraging and particle swarm optimization. BFO algorithm has recently emerged as a very powerful technique for real parameter optimization. To overcome delay in an optimization, combine the features of BFOA and PSO for tuning the PID controller. This new algorithm is proposed to combine both the algorithms to get better optimization values. The real time prototype model of paper machine is designed and controlled by using PIC microcontroller embedded with the programming in C language.

Improved Continuous Cycling Method of Tuning PID Controllers for Unstable Systems

In the present work, an improved continuous cycling method is proposed for tuning proportional-integral-derivative (PID) controllers for unstable systems. The method involves the determination of the controller settings by solving the magnitude criterion and the phase angle criterion for the system. Subsequently, incorporating the transfer function of the PID controller, with unity proportional gain, with the system model and again solving the amplitude and the phase angle criteria to get the updated gain of the controller. The proposed method is applied by simulation on (i) a second-order system with one unstable pole and (ii) a nonlinear model of a bioreactor. The method is also extended for the relay tuning method to automate the improved continuous cycling method. The controller settings significantly enhance the performances (integral time absolute error) of both the servo and the regulatory problems and also give a robust performance. The present method is compared with the reported methods based on the ultimate cycle and with the method proposed by Jeng and Fu [“Closed Loop Tuning of Set Point Weighted Proportional-Integral-Derivative Controllers for Stable, Integrating and Unstable Processes: A Unified Data Based Method”, Ind. Eng. Chem. Res., 54, pp. 1041–1058 (2015)].

PID controller tuning for a multivariable glass furnace process by genetic algorithm

Standard genetic algorithms (SGAs) are investigated to optimise discrete-time proportional-integral-derivative (PID) controller parameters, by three tuning approaches, for a multivariable glass furnace process with loop interaction. Initially, standard genetic algorithms (SGAs) are used to identify control oriented models of the plant which are subsequently used for controller optimisation. An individual tuning approach without loop interaction is considered first to categorise the genetic operators, cost functions and improve searching boundaries to attain the desired performance criteria. The second tuning approach considers controller parameters optimisation with loop interaction and individual cost functions. While, the third tuning approach utilises a modified cost function which includes the total effect of both controlled variables, glass temperature and excess oxygen. This modified cost function is shown to exhibit improved control robustness and disturbance rejection under loop interaction.

Particle Swarm Optimization Quadrotor Control for Cooperative Aerial Transportation of Deformable Linear Objects

ABSTRACTWe present a cooperative aerial robot system for the transportation of hoses. The hose–robot attachment makes the whole system physically interconnected but not rigid, so that control design becomes a difficult nonlinear optimization problem. The hose in quasistationary state can be modeled by sections of catenary curves. We use proportional integral derivative (PID) controllers for both quadrotor attitude and trajectory control, tuned by particle swarm optimization (PSO). In this work we test PSO minimizing an energy function to achieve the PID controller tuning for horizontal motion of quadrotor teams transporting hoses under different stress conditions.

BBO algorithm-based tuning of PID controller for speed control of synchronous machine

A biogeography-based optimization (BBO) algorithm was used for tuning the parameters of a proportional integral derivative (PID) controller-based power system stabilizer (PSS). The proposed method minimizes the low frequency electromechanical oscillations (0.1–2.5 Hz) and enhances the stability of the power system by optimally tuning the PID parameters. This was achieved by minimizing the objective function of the integral square error for various disturbances. The performance of the BBO algorithm was tested on a single machine infinite bus system for a different range of operating conditions and the results were compared with particle swam optimization, adaptation law, and conventional PSS. The result analysis concluded that the BBO algorithm damps out the low frequency oscillations in the rotor of the synchronous machine effectively when compared to other methods. The algorithms were simulated with MATLAB/Simulink. The results from the simulation showed that the proposed controller yields a fast convergence rate and better dynamic performance.

PID Controller Design for Electro-hydraulic Servo Valve System with Genetic Algorithm

The proportional-integral-derivative (PID) controller are widely applied with the electro-hydraulic servo system since it is easily to implement and highly efficient. The optimal tuning PID parameter that can increase the performance of the control system. Thus, this paper is considered the optimization technique of Genetic Algorithm (GA) for tuning PID controller parameter for electro-hydraulic servo system. The other techniques are compared, that is the obtained results are shown the genetic algorithm optimized PID controller provide an improved closed-loop are performed than the Ziegler- Nichols, tuning method automatic tuning method and particle swarm optimization (PSO).

Removal of Moisture Content in Paper Machine Using Soft Computing Techniques

The choice of this investigation is to tune the proportional-integral-derivative (PID) parameters separately for controlling the moisture content in paper industry by using Particle Swarm Optimization (PSO). This paper boon a new algorithm for PID controller tuning based particle swarm optimization. PSO algorithm has recently developed as a very powerful method for real parameter optimization. This new process is proposed to combine both the algorithms to get better optimization values. The proposed algorithm tuned the PID parameters and its performance has been compared with PID algorithm. Compared to PID algorithm technique, dynamic performance requirements such as rise time settling time and peak overshoot optimal values produced by PSO. The plant model represented by the transfer function is obtained by the system identification toolbox.

Design of RCGA-based PID controller for two-input two-output system

Proportional-integral-derivative (PID) controllers are widely used in industrial sites. Most tuning methods for PID controllers use an empirical and experimental approach; thus, the experience and intuition of a designer greatly affect the tuning of the controller. The representative methods include the closed-loop tuning method of Ziegler-Nichols (Z-N), the C-C tuning method, and the Internal Model Control tuning method. There has been considerable research on the tuning of PID controllers for single-input single-output systems but very little for multi-input multi-output systems. It is more difficult to design PID controllers for multi-input multi-output systems than for single-input single-output systems because there are interactive control loops that affect each other. This paper presents a tuning method for the PID controller for a two-input two-output system. The proposed method uses a real-coded genetic algorithm (RCGA) as an optimization tool, which optimizes the PID controller parameters for minimizing the given objective function. Three types of objective functions are selected for the RCGA, and each PID controller parameter is determined accordingly. The performance of the proposed method is compared with that of the Z-N method, and the validity of the proposed method is examined.

Optimal PID controller design through swarm intelligence algorithms for sun tracking system

Due to depletion of fossil fuel many developing countries like Pakistan are facing energy crisis. Over the last decade Pakistan has suffered greatly economically due to energy crisis and at government level and in private sector a great effort is being made to overcome this energy crisis. In abundantly available energy sources solar energy is one of the cleanest and omnipresent energy source. Solar tracker systems are gaining popularity due to wide spread of solar energy use as an alternative energy source compared to fossil fuel. To obtain optimum energy from solar system accurate tracking of sun is required with respect to azimuth and tilt angle. Usually there are two types of solar trackers one is single axis and other is dual axis. For both types of solar trackers DC motor based servo systems are used for precise movement control. We consider in the present work designing of an optimum proportional-integral-derivative (PID) controller for DC motors of dual axis solar tracker system. The problem is formulated as optimization problem and three swarm intelligence based metaheuristic algorithms namely particle swarm optimization (PSO), firefly algorithm (FFA) and Cuckoo Search Algorithm (CSA) are employed for optimum tuning of PID controller. The study shows that CSA is superior as compared to FFA and PSO due to its faster convergence rate, small variance and standard deviation of design parameters obtained. Also pertaining to performance domain FFA and CSA show better performance as compared to PSO for the problem domain under study.