Feedback Control Systems Stabilization Using a Bio-inspired Neural Network

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.

Deep Learning Application Pros And Cons Over Algorithm

“…Currently, popular virtual assistants include Amazon Alexa, Apple's Siri, Google Assistant, and Microsoft's Cortana digital assistant, all built into Windows Phone 8.1 and Windows 10 [35][36]. Although the definition focuses on the digital form of virtual assistants, the term virtual assistant or virtual personal assistant is also often used to describe contract workers who work from home, who usually perform administrative tasks by administrative assistants or secretaries [37][38][39]. Virtual assistants can also be compared with another type of consumer-oriented AI programming (called smart advisors).…”

Credit and Loan Approval Classification Using a Bio-Inspired Neural Network

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