An Improved Real-Time Implementation of Adaptive Neuro-Fuzzy Controller

An adaptive neural network-based control method is highly effective for controlling complex systems with various operating situations since it can adapt and train its control strategy to compensate for changes in the system's behavior. The backpropagation algorithm is utilized to optimize the neural controller values and reach the desired system reference. Compared to other neural control methods, the proposed neuro-fuzzy controller's learning algorithm has a quick response time, making it suitable for a wide range of real-world applications that require ANNs. Fuzzy rules can be established to improve the adaptability and performance of a neural controller by optimizing its decisions and taking into account various factors such as data and desired outcomes. Furthermore, these fuzzy rules can help determine the optimal momentum and learning rate values for a given problem. The proposed method has been executed using LabViewprogram and validated in practical through a research lab actuation control structure, demonstrating that optimizing the neuro-fuzzy controller with optimal parameters of the neural network model leads to improved control and faster response times in nonlinear controller systems.