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Journal ArticleDOI

Particle swarm optimization based PID controller tuning for level control of two tank system

01 Nov 2017-Vol. 263, Iss: 5, pp 052001
TL;DR: Particle Swarm Optimization (PSO) based algorithm is proposed for the optimization of a PID controller for level control process and results show that the proposed method provides better controller performance.
Abstract: Automatic control plays a vital role in industrial operation. In process industries, in order to have an improved and stable control system, we need a robust tuning method. In this paper Particle Swarm Optimization (PSO) based algorithm is proposed for the optimization of a PID controller for level control process. A two tank system is considered. Initially a PID controller is designed using an Internal Model Control (IMC). The results are compared with the PSO based controller setting. The performance of the controller is compared and analyzed by time domain specification. In order to validate the robustness of PID controller, disturbance is imposed. The system is simulated using MATLAB. The results show that the proposed method provides better controller performance.
Citations
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Journal ArticleDOI
22 Jan 2019
TL;DR: Design of the Proportional-Integral-Derivative (PID) controller using conventional and metaheuristic methods for the temperature control of the Continuous Stirred Tank Reactor is presented.
Abstract: An integral part of industrial processes is the Continuous Stirred Tank Reactor (CSTR) whose dynamic characteristics are highly nonlinear causing the reactor to deviate from its set temperature point. For its efficient operation, specific parameters of the CSTR are required to be controlled. Hence, this paper presents designing of the Proportional-Integral-Derivative (PID) controller using conventional and metaheuristic methods for the temperature control of the CSTR. The conventional controller is tuned with Ziegler Nichols (Z-N) method. Global search population-based metaheuristic methods like Artificial Bee Colony (ABC), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been implemented to optimize the adaptive controller parameters, and a comparative analysis is done taking the step response into consideration. The CSTR system is simulated by the proposed controller which improves the robustness, behaviour and tracking of the system. The simulation results present substantial enhancement of the time response parameters, i.e. settling time, rise time, peak overshoot, mean square error and integral time absolute error. Studies showed that the proposed adaptive methods with metaheuristic algorithms are fast and efficient in error reduction. Here, ABC irrespective of other optimization methods suitably optimized the controller parameters.

19 citations

Journal ArticleDOI
TL;DR: A new optimum interval type-2 fuzzy fractional-order controller for a class of nonlinear systems with incipient actuator and system component faults is introduced and its effectiveness is demonstrated when compared to IT2FPID and existing passive fault tolerant controllers in recent literature.
Abstract: A new optimum interval type-2 fuzzy fractional-order controller for a class of nonlinear systems with incipient actuator and system component faults is introduced in this study. The faults of the actuator and system component (leak) are taken into account using an additive model. The Interval Type-2 Fuzzy Sets (IT2FS) is used to design an optimal fuzzy fractional order controller, and two different nature inspired metaheuristic algorithms, Follower Pollination Algorithm (FPA) and Genetic Algorithm (GA), are used to optimize the parameters of the fuzzy PID controller and Interval Type-2 Fuzzy Tilt-Integral-Derivative Controller (IT2FTID) for nonlinear system. The suggested control approach consists of two parts: an Interval Type-2 Fuzzy Logic Controller (IT2FLC) controller and a fractional order TID controller. Additionally, the two inputs of the IT2FLC are also calibrated using two fine tuning parameters and , respectively. The stability of the proposed controller is presented with some conditions. In addition to unknown dynamics, some unknown process disturbances, such as rapid changes in the control variable, are taken into account to check the efficacy of the proposed control scheme. Two nonlinear conical two-tank level systems are used in the simulation as a case study. The performance of the suggested approach is also compared to that of a widely recognized Interval Type-2 Fuzzy Proportional-Integral-Derivative (IT2FPID) Controller. Finally, the proposed control scheme's fault-tolerant behaviour is demonstrated using fault-recovery time results and statistical Z-tests for both controllers, and the proposed IT2FTID controller's effectiveness is demonstrated when compared to IT2FPID and existing passive fault tolerant controllers in recent literature.

10 citations

Journal ArticleDOI
TL;DR: In this article , a real-time liquid level monitoring and control in a single tank system using a modified Grey Wolf Optimization (mGWO) algorithm is used to manage the liquid level.

8 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a hybrid control of active and reactive power for a doubly-fed induction generator for variable velocity wind energy injection into the electrical grid using a combination of adaptive particle swarm optimization and integral backstepping control.
Abstract: We proposed an analysis of a hybrid control of active and reactive power for a doubly-fed induction generator for variable velocity wind energy injection into the electrical grid using a combination of adaptive particle swarm optimization and integral backstepping control in this paper. The stability of the Lyapunov function is utilized to establish the latter. Six controllers are developed as part of the proposed control process: The first is concerned with the maximum PowerPoint. The stator powers are managed by the second and third regulators, which are performed by the optimal PI controller using adaptive particle swarm optimization. The DC link voltage is kept constant by the fourth controller. The fifth and sixth are employed to pilot the rotor powers and ensure that the power factor is maintained to 1. These three controllers are synthesized by using the nonlinear integral backstepping control. These control strategies show excellent results compared to field-oriented control under a variable wind speed profile and changing generator settings in a Matlab/Simulink environment. According to the test findings, using integral backstepping, the overshoot of the DC-link voltage is decreased by 99.16 %. Furthermore, the particle swarm optimization reduces its time to reach the equilibrium state to 4.3 m s and demonstrates robustness against parameter generator changes. HIGHLIGHTS The regulation of the produced power by the wind energy conversion system (WECS) based on a doubly-fed induction generator is becoming increasingly important to researchers. This system is modeled and simulated in the Matlab/Simulink software environment to apply the proposed control In order to extract the maximum power from the variable wind source, a maximum power point tracking method is developed based on the PI controller For piloting the wind energy system conversion (WECS) based on a DFIG, a combination of the integrated Backstepping controller and adaptive PSO is proposed and realized in this paper Robustness tests are established by adjusting the generator parameters, and a comparative study is conducted to verify the superiority of the suggested control over the indirect vector control GRAPHICAL ABSTRACT

4 citations

Book ChapterDOI
01 Jan 2020
TL;DR: In this paper, various tuning methods have been presented and comparison of established algorithmic tuning methods has been discussed on system response, which leads to higher controller efficiency over a predefined finite set of ramp-hold cycles, ensuring lesser rise and settling time, reduced or no overshoot, minimized mean squared error, and maximum stability.
Abstract: PID controllers are implemented in more than 90% of the control system applications. In this review paper, various tuning methods have been presented and comparison of established algorithmic tuning methods has been discussed on system response. There have been many approaches used in the past for tuning and obtaining optimized gain factors such as Ziegler–Nichols method, genetic algorithm (GA), particle swarm optimization (PSO) method, and artificial neural network (ANN). The primary goal of this paper is to establish a proper understanding about different tuning and optimization methods and their effect on process efficiency and stability. The secondary goal is to provide a pathway for future development of a tuning algorithm for a high-temperature research grade furnace controller, based on machine learning (ML). This leads to higher controller efficiency over a predefined finite set of ramp–hold cycles, ensuring lesser rise and settling time, reduced or no overshoot, minimized mean squared error, and maximum stability. Critical manufacturing processes like investment casting, metal injection molding, and other thermal cycling processes like physical vapor deposition/chemical vapor deposition, e-waste processing, which require precise control of temperature are expected to be benefited by ML-integrated PID parameter auto-tuning and control.

3 citations

References
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Journal ArticleDOI
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed The relationships between particle swarm optimization and both artificial life and genetic algorithms are described

18,439 citations

Journal ArticleDOI
TL;DR: In this paper, the three principal control effects found in present controllers are examined and practical names and units of measurement are proposed for each effect and corresponding units for a classification of industrial processes in terms of two principal characteristics affecting their controllability.
Abstract: In this paper, the three principal control effects found in present controllers are examined and practical names and units of measurement are proposed for each effect. Corresponding units are proposed for a classification of industrial processes in terms of the two principal characteristics affecting their controllability. Formulas are given which enable the controller settings to be determined from the experimental or calculated values of the lag and unit reaction rate of the process to be controlled

5,412 citations

01 Jan 1942
TL;DR: In this paper, the three principal control effects found in present controllers are examined and practical names and units of measurement are proposed for each effect.

3,869 citations

Journal ArticleDOI
TL;DR: A simple method for estimating the critical gain and the critical frequency is described, which may be used for automatic tuning of simple regulators as well as initialization of more complicated adaptive regulators.

1,763 citations

01 Jan 1942

1,018 citations