A New Method for Tuning PID-Type Fuzzy Controllers Using Particle Swarm Optimization

Haggège, Joseph; Benrejeb, Mohamed

doi:10.5772/48305

Cited by 11 publications

(12 citation statements)

References 21 publications

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“…Recall that the reference model itself is trying to follow or track the reference input or set-point, thus we can select the set-point as a reference model, and simply by minimizing the error between the system output and set-point we can guarantee that the tracking problem is achieved and the RST controller perfectly performed its job. Integral Square Error (ISE) or Integral Absolute Error (IAE) can be used as an objective function to be minimized [4], [5].…”

Section: B Objective Function Formulationmentioning

confidence: 99%

Design of RST controllers based on intelligent optimization algorithms

Jacknoon

Hassan

El-Férik

2016

2016 Conference of Basic Sciences and Engineering Studies (SGCAC)

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The Proportional Integral Derivative controller (PID) and the RST controller are the dominant controllers nowadays, since they have simple structure and perform well in most of control loops application. However, unlike PID the RST controller doesn't have direct tuning procedure. In this paper we present an approach for designing and tuning the RST controller by using intelligence optimization algorithms. Three algorithms namely (BSOA, DE and PSO) have been tested in tuning RST controller that control electrical DC motor that has been used as a benchmark. Simulation results show PSO tuned RST structure to be more robust and have high performance.

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Section: B Objective Function Formulationmentioning

confidence: 99%

Design of RST controllers based on intelligent optimization algorithms

Jacknoon

Hassan

El-Férik

2016

2016 Conference of Basic Sciences and Engineering Studies (SGCAC)

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Section: Control Statementmentioning

confidence: 99%

“…To overcome these design intricacies, the optimization theory is found to be a successful way to calculate and optimize effortlessly the PID gains. [41][42][43][44][45] The WCA has been suggested and studied using other PID structures and systems in the literatures. [46][47][48] The optimization-based PID tuning dilemma in the airflow strategy intends to compute the control design parameters…”

Section: Airflow Controlmentioning

confidence: 99%

“…The objective function is minimized considering a range of time-domain constraints that are associated with the steady-state error E ss , the rise time t r , the settling time t s and the overshoot d(%) criteria of the closed-loop step response. 41 The tuning problem regarding the airflow approach for the OWC unit can be defined by the constrained and nonlinear optimization problem of equation (30) which will be solved with the WCA algorithm where f:R 3 ! R is the objective function, S = fx 2 R 3 + , x low ł x ł x upper g represents the bounded search space for the decision variables and g i :R 3 !…”

Section: Airflow Controlmentioning

confidence: 99%

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Water cycle algorithm–based airflow control for oscillating water column–based wave energy converters

M’zoughi

Garrido

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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The stalling behavior is a feature of the Wells turbine that limits the generated output power of power plants using this turbine. The NEREIDA wave power plant installed in the harbor of Mutriku in the northern Spanish shoreline constitutes an excellent example of this phenomenon. This article deals with the modeling, simulation and control of an oscillating water column unit within the NEREIDA wave power plant. The stalling behavior is investigated and two control strategies are proposed to avoid it. The first control approach is the airflow control which aims to adjust the airflow in the turbine duct using a proportional–integral–derivative controller tuned with the water cycle algorithm. The second control approach is the rotational speed control adjusting the rotor speed using the rotor-side converter of the back-to-back converter which is wired to the doubly fed induction generator. Results of comparative studies show a power generation improvement even relative to the real measured data.

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“…The AFL-PID controller is nonlinear and it is scarcely possible to establish accurate mathematical model. So it gets hard to analysis the stability of the system with an AFL-PID controllers [5]. But still there are several ways for solving the problem, such as Lyapunov stability theory and circle stability criterion method, etc... PSO algorithm is appropriate for parameter optimization in continuous search space in many dimensions.…”

Section: Introductionmentioning

confidence: 99%

Application of Adaptive PSO and Adaptive Fuzzy Logic Controllers to Speed Control PMSM Motor Servo Systems

Hieu

Olegovich

2018

MATEC Web Conf.

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Three phases Permanent Magnet Synchronous Motors (PMSM) are non-linear resistors, resistance of stator winding, air gap flux, cross-coupling, saturation variable times and cogging torque in operation. Due to the nonlinear nature of PMSM, it is a challenge to control exactly the speed, torque and position. This paper presents two methods for speed control stabilization of the PMSM using the Adaptive Fuzzy Logic -Proportional Integral Derivative controller (AFL-PID) and Adaptive Particle Swarm Optimization -Proportional Integral Derivative controller (APSO-PID). The response results of the speed control PMSM Servo Systems use AFLC-PID and APSO-PID methods are compared and the conclusions are given.

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A New Method for Tuning PID-Type Fuzzy Controllers Using Particle Swarm Optimization

Cited by 11 publications

References 21 publications

Design of RST controllers based on intelligent optimization algorithms

Design of RST controllers based on intelligent optimization algorithms

Water cycle algorithm–based airflow control for oscillating water column–based wave energy converters

Application of Adaptive PSO and Adaptive Fuzzy Logic Controllers to Speed Control PMSM Motor Servo Systems

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