Tracking Control of an Inverted Pendulum System: A Novel Radial Basis Function Neural Network Supervisory Control Approach

The control of an Inverted Pendulum (IP) is one of the benchmark problems in control systems. It is very difficult to control due to its innate instability and extremely nonlinear properties. Here, a novel supervisory control method based on Radial Basis Function Neural Networks (RBFNN) approach aims to enhance the tracking performance of an IP system. The RBFNN is used as a dynamic compensator to improve the performance of the typical Proportional-Integral-Derivative (PID) controller. The network is trained to approximate the unknown nonlinear dynamics of the IP system. The corrective control signal is then produced by the trained network and added to the PID controller's output. An adaptive tuning algorithm is created to continuously update the RBFNN's parameters to achieve accurate tracking control. As a result, the system can instantly adapt to modifications in the dynamics of the plant. The effectiveness of the suggested approach in achieving reliable tracking control of the IP system is demonstrated by simulation results. When compared to the traditional PID and FUZZY-PID controller, the RBFNN supervisory control strategy significantly enhances the system's tracking performance with minimum overshoot and settling time. The method offers a promising framework for the control of complex nonlinear systems.