FFANN Optimization by ABC for Controlling a 2nd Order SISO System’s Output with a Desired Settling Time

Abstract

In this study, a control strategy is aimed to ensure the settling time of a 2nd order system’s output value while its input reference value is changed. Here, Feed Forward Artificial Neural Network (FFANN) nonlinear structure has been chosen as a control algorithm. In order to implement the intended control strategy, FFANN’s normalization coefficient (K), learning coefficients (ŋ), momentum coefficients (μ) and the sampling time (Ts) were optimized by Artificial Bee Colony (ABC) but FFANN’s values of weights were chosen arbitrary on start time of control system. After optimization phase, the FFANN behaves as an adaptive optimal discrete time non-linear controller that forces the system output to take the same value with the input reference for a desired settling time (ts). The success of the optimization algorithm was proved with close loop feedback control simulations on Matlab’s Simulink platform based on 2nd order transfer functions. Also, the success was proved with a 2nd order physical system (buck converter) that was structured with power electronics elements on Simulink platform. Finally, the success of the control process was discussed by observing results.

Recent Advances on Optimization for Control, Observation, and Safety

“…Aydın Mühürcü [3] considers a combination of a feed-forward artificial neural network (FFANN) and an artificial bee colony (ABC) optimization algorithm to ensure the settling time of a second-order system. The FFANN is the nonlinear control structure adopted for a buck converter and its parameters are optimized using the ABC algorithm.…”

Increasing Surface Coating Quality Using New Generation Current Waveforms

“…The ANN algorithm has become a discrete time, nonlinear, optimal, adaptive control algorithm for these operating conditions. In order to realize this transformation, parameters of ANN algorithm should be optimized [40]. However, after this optimization, ANN can be converted to the targeted control algorithm.…”

Reactive Power Optimization of Large-Scale Power Systems: A Transfer Bees Optimizer Application

“…There are two kinds of bees in Figure 2, e.g., scout and worker, determined by their nectar amounts (fitness values) [19], which are responsible for global searching and local searching. As a consequence, a bee colony can balance the exploration and exploitation through different action policies in a nectar source.…”

“…According to the given typical daily load curves shown in Figure 5, the active power demand was discretized into 20 and 22 load intervals, respectively, where every interval was 125 MW and 500 MW, respectively, i.e., {[3500, 3625), [3625, 3750), …, [5875, 6000]} MW and { [19,000,19,500), [19,500,20,000), …, [28,500,29,000]} MW. Moreover, the implementation time of RPO was set at 15 min.…”

“…In the past decades, artificial intelligence (AI) [8][9][10][11][12][13][14][15][16][17][18] has been widely used as an effective alternative because of its high independence from an accurate system model and strong global optimization ability. Inspired by nectar gathering of bees in wild nature, the artificial bee colony (ABC) [19] has been applied to optimal distributed generation allocation [8], global maximum power point (GMPP) tracking [20], multi-objective UC [21], and so on, and has the merits of simple structure, high robustness, strong universality, and efficient local search.…”