Neural Network-Based Feedback Linearization Control Process

In this section, we introduce the neural network-based feedback linearization control process. Feedback linearization is a control methodology that utilizes feedback mechanisms to cancel out system nonlinearities, thereby achieving linear dynamics in the closed-loop system. Specifically, this control approach involves measuring system outputs, comparing them with desired references, and generating compensation signals to counteract nonlinear effects through carefully designed control laws. This methodology finds extensive applications across various domains including mechanical engineering, electronic systems, and control system design. By employing neural networks as computational tools for feedback linearization control, we can effectively handle complex nonlinearities through adaptive approximation capabilities, significantly enhancing control performance and system stability. The implementation typically involves neural network architectures that learn inverse dynamics or nonlinear mappings, with training algorithms adjusting synaptic weights to minimize tracking errors. Therefore, neural network-based feedback linearization represents a crucial control technique that provides substantial advantages for solving challenging nonlinear control problems, particularly through its ability to approximate unknown nonlinear functions without requiring explicit system models.