## 1 Introduction  
An artificial neural network (ANN) is a category of algorithms within the domain of artificial intelligence (AI). It emerged in the 1980s as a result of significant progress in both cognitive science and computer science research [1]. Extensive research utilizing computer simulations has demonstrated the capacity of ANNs to map, model, simulate, and categorize nonlinear systems [2]. The unique attributes of ANNs, including their ability to learn from case observations, adaptability, parallel processing, robustness against noise, and fault tolerance, have led their implementation in diverse domains, such as engineering, science, economics, and beyond. Explicitly, in engineering, the ANN is utilized in the speed control of AC motors [3], oil, petroleum, and gas industries [4, 5, 6], solar power forecasting [7], stand-alone power system controllers [8], robotic manipulator controller design [9], and the control of a pneumatic artificial muscle (PAM) [10]. Controller design and algorithms establish minimal challenges when industrial applications utilize actuators with linear characteristics. However, controller and control algorithm design become challenging when the response of the actuator in the system is nonlinear and the characteristics of the other components in the system cannot be accurately modeled. Artificial pneumatic muscles are frequently preferred in industrial applications due to their high-capacity force characteristics and high accuracy and repeatability, but they are one of the actuators with nonlinear characteristics. PAM-based actuators exhibit high nonlinearity due to their complex joint geometry. Static and dynamic modeling of these types of actuators are extensively studied in the literature [11; 12; 13]. Programmable logic controllers (PLCs) are often the preferred option for real-time controllers used in automation systems and field applications due to their inherent advantages, including flexibility, reliability, and cost-effectiveness when managing complex systems [14; 15]. Whether it is a single task or an entire system with nonlinear output responses determined by inputs, the use of real-time algorithms within PLCs is essential for effective control. The complexity of the control algorithm design increases consistently with the specific tasks to be controlled and the quantity of discrete or dependent variables involved. The process of modeling nonlinear systems and designing real-time control algorithms for use in the field is time consuming and challenging. The application of ANNs to obtain responses from nonlinear systems or predict and manage outputs in unforeseen system situations can offer significant advantages in terms of efficiency and reductions in modeling time and associated costs. Consequently, this eliminates the requirement for complex system modeling. A well-trained ANN implemented on PLCs can be beneficial in generating responses for unexpected system inputs and generating functions based on field-collected data. In a structural context, earlier studies can be categorized into two main sections: embedded neural network systems utilizing PLCs [16; 17; 18] and hybrid systems [19; 20; 21; 15], which include an additional controller, such as a PC or workstation, in communication with the PLCs. This paper focuses on the multilayer perceptron (MLP) neural network in the ANN-based embedded data-driven controller design for a complete fluidic muscle station that uses PAM as an actuator. A control algorithm was crafted with an ANN without establishing a model of a nonlinear system, the experimentally obtained data were trained, and the code of the ANN algorithm was then embedded in the PLC. By employing machine learning algorithms on local devices or embedded systems, edge artificial intelligence (Edge AI) with transfer learning prioritizes data processing at the source, where AI computations are executed and data are generated, thereby reducing the reliance on continuous connectivity to remote servers.