Nonlinear Control Law Design for Inverted Pendulum Systems via RBF Neural Networks

This paper presents the design of a nonlinear control law based on the Backstepping method combined with Radial Basis Function (RBF) neural networks to ensure the stability of an inverted pendulum system with unknown model parameters. The control design is developed using a general form of the system’s mathematical model, in which the unknown nonlinear functions are approximated by RBF neural networks. Experimental results conducted on the STM32F4 embedded platform demonstrate that the proposed approach not only guarantees system stability but also verifies the effectiveness and practical applicability of the control law.