A Feedback‐Assisted Inverse Neural Network Controller for Cart‐Mounted Inverted Pendulum

A vast variety of neural network (NN)–based controllers use indirect adaptive control structures for their implementation, which primarily aims at estimating the nonlinear dynamics of the system and thereby generating a suitable control action. However, the most commonly used gradient descent weight update rule of the NN‐based indirect adaptive controllers often fails to identify the system dynamics appropriately, and the control action becomes ineffective on the system under control. Further, a significant number of existing works in this domain employ randomized or suboptimal initial NN weights, which can potentially hamper the transient performance of the control loop. To address these issues, this paper proposes an innovative control scheme that utilizes the strength of indirect adaptive control of NN, along with the robustness of the PID controller. Since the PID control structure is often independent of plant dynamics and generates a control action to mitigate any error between the reference and the current plant output, it can be easily augmented with the control action generated by inverse neural network (INN) to mitigate any effects of unlearnt dynamics by INN. Further, we have used a bio‐inspired optimization algorithm, that is, particle swarm optimization (PSO), to optimize the initial weights of the INN along with the PID controller’s parameters to get an optimal control performance. The proposed INN + PID controller scheme has been tested on a cart‐mounted inverted pendulum system due to its challenging control requirement owing to its intricate nonlinear dynamics. Detailed simulation studies for the proposed INN + PID and PID controllers have been carried out for various control requirements, viz. set point tracking, disturbance rejection, and robustness testing. Further, an extensive comparative study has been devised based on the integral of absolute error (IAE) to test the efficacy of the proposed INN + PID controller against the conventional PID controller. Through extensive comparative studies, it was deduced that the proposed INN + PID controller is capable of handling the intricate nonlinear dynamics of the cart‐mounted inverted pendulum system and provides a sturdy stabilization of the angular position of the pendulum with respect to the desired trajectory and superior transient control in comparison with the conventional PID controller. In terms of quantitative comparison, the improvement in IAE achieved by the proposed INN + PID controller was found to be 94.84%, 94.62%, and 69.86% better in comparison to the conventional PID controller for set point tracking, disturbance rejection for introduced impulsive force, and time‐varying force variation, respectively.