Abstract
The proportional–integral–derivative (PID) control systems, which have become a standard for technical and industrial applications, are the fundamental building blocks of classical and modern control systems. In this paper, a three-layer feed-forward neural network (NN) model trained to replicate the behavior of a PID controller is employed to stabilize control systems through a NN feedback controller. A novel bio-inspired weights-and-structure-determination (BIWASD) algorithm, which incorporates a metaheuristic optimization algorithm dubbed beetle antennae search (BAS), is used to train the NN model. More presicely, the BIWASD algorithm identifies the ideal weights and structure of the BIWASD-based NN (BIWASDNN) model utilizing a power sigmoid activation function while handling model fitting and validation. The results of three simulated trials on stabilizing feedback control systems validate and demonstrate the BIWASDNN model’s exceptional learning and prediction capabilities, while achieving similar or better performance than the corresponding PID controller. The BIWASDNN model is compared to three other high-performing NN models, and a MATLAB repository is accessible in public through GitHub to encourage and enhance this work.
Highlights
BIWASDNN is the name of the neural network (NN) model, which is trained using a novel bio-inspired weights-and-structure-determination (BIWASD) algorithm
Note that the BIWASD algorithm is responsible for finding the ideal weights W and the structure of the NN, i.e. finding the corresponding activation function (AF) Fv(X) varying powers v
Numerical findings are presented in this subsection to prove the practicality of the suggested BIWASDNN model and the BIWASD algorithm on regression problems that involve datasets for training NN feedback controllers
Summary
The proportional–integral–derivative (PID) controllers have been used successfully in processcontrolled fields of industry such as machinery, metallurgy, power, and light industry since they first emerged decades ago [1]. According to the previous analysis, obtaining the optimal connecting weights and the optimal number of HLNs for the multi-input NN are useful and important, especially in the general multi-input NNs, because they can considerably reduce the computational complexity and promote hardware realization That is, they improve the efficiency of the NNs [15]. Another approach for improving the performance of artificial NNs is to use meta-heuristics In this concept, the Beetle Antennae Search (BAS) algorithm has been used to optimize Elman NN [16], feed-forward highdimensional NN [17], fog computing networks [18], and back-propagation NN [19]. The BIWASD algorithm identifies the ideal weights and structure of the BIWASDNN model utilizing a power sigmoid activation function (AF), while employing cross-validation to address bias and prevent being stuck in local optima during the training process.
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