Fuzzy logic and gradient descent-based optimal adaptive robust controller with inverted pendulum verification

This paper develops an adaptive robust combination of feedback linearization (FL) and sliding mode controller (SMC) based on fuzzy rules and gradient descent laws. The new suggested control algorithm is tested to stabilize a fourth-order under-actuated nonlinear inverted pendulum system. More precisely, the reliable feedback linearization approach and the robust SMC controller are combined to design a stable control effort. In order to enhance the performance of the suggested controller, an adaptation technique as long as fuzzy rules are applied to update the control gains and the boundary layer parameter. Then, a novel evolutionary algorithm termed multi-objective ant lion optimizer (MOALO) is implemented to determine the control coefficients. The analysis and results conducted on the inverted pendulum system demonstrate the desired performance of the proposed control scheme by providing an optimal smooth control input, suitable tracking performance, and proper time responses. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper presents an adaptive robust control algorithm based on fuzzy rules and gradient descent laws, which combines reliable feedback linearization approach with robust sliding mode controller to design a stable control effort for an under-actuated nonlinear inverted pendulum system. The analysis and results on the inverted pendulum system demonstrate the desired performance of the proposed control scheme by providing optimal smooth control input, suitable tracking performance, and proper time responses.