Fractional order fuzzy-PID control of a combined cycle power plant using Particle Swarm Optimization algorithm with an improved dynamic parameters selection

Abstract

The effectiveness of the Particle Swarm Optimization (PSO) algorithm in solving any optimization problem is highly dependent on the right selection of tuning parameters. A better control parameter improves the flexibility and robustness of the algorithm. In this paper, a new PSO algorithm based on dynamic control parameters selection is presented in order to further enhance the algorithm's rate of convergence and the minimization of the fitness function. The powerful Dynamic PSO (DPSO) uses a new mechanism to dynamically select the best performing combinations of acceleration coefficients, inertia weight, and population size. A fractional order fuzzy-PID (fuzzy-FOPID) controller based on the DPSO algorithm is proposed to perform the optimization task of the controller gains and improve the performance of a single-shaft Combined Cycle Power Plant (CCPP). The proposed controller is used in speed control loop to improve the response during frequency drop or change in loading. The performance of the fuzzy-FOPID based DPSO is compared with those of the conventional PSO, Comprehensive Learning PSO (CLPSO), Heterogeneous CLPSO (HCLPSO), Genetic Algorithm (GA), Differential Evolution (DE), and Artificial Bee Colony (ABC) algorithm. The

PLC based fractional-order PID temperature control in pipeline: design procedure and experimental evaluation

“…where y m ðtÞ-the measured plant output, y SP ðtÞ-setpoint. PI k D l controller can be tuned according to the optimization method described in [49,52,65], assuming the controller transfer function in the form (14) and the process transfer function in the form…”

“…The implementation process of the PI k D l control algorithm (14) on PLC can be divided into two phases.…”

“…FOPID fuzzy control of the combined cycle power plant is proposed in [14]. In [25] the FOPID controller is implemented in the pneumatic servo-system.…”

“…Most FOPID algorithms are usually tested and simulated in a MATLAB-Simulink environment [10,14,15,26,50,58,61,62,64]. Controllers that implement the fractional-order control algorithms usually are equipped with fast and efficient DSP processors (e.g.…”

Robust fractional order PID controller synthesis for the first order plus integral system

“…For example, a fuzzy FOPID controller is tuned by a dynamic particle swarm optimization method. 10 An optimal FOPID controller is designed by minimizing the time-domain performance index under the frequency-domain constraints. 11 In the engineering applications of these optimization methods, sufficient time and high-performance micro-controllers may be needed for the optimization process.…”