Uncertain nonlinear system control using hybrid fuzzy LQR-sliding mode technique optimized with evolutionary algorithm

Abstract

Purpose This paper aims to propose an advanced tracking control of the uncertain nonlinear dynamic system using a novel hybrid fuzzy linear quadratic regulator (LQR)-proportional-integral-derivative (PID) sliding mode control (SMC) optimized by differential evolution (DE) algorithm. Design/methodology/approach First, a swing-up and balancing control is presented for an experimental uncertain nonlinear Pendubot system perturbed with friction. The DE-based optimal SMC scheme is used to optimally swing up the Pendubot system to the top equilibrium position. Then the novel hybrid fuzzy-based on LQR fusion function and PID controller optimized by DE algorithm is innovatively applied for balancing and control the position of the first link of the Pendubot in the down-right position with tracking sinusoidal signal reference. Findings Experimental results demonstrate the robustness and effectiveness of the proposed approach in balancing control for an uncertain nonlinear Pendubot system perturbed with internal friction. Originality/value This manuscript is an original research paper and has never been submitted to any other journal.

Hybrid Fuzzy Sliding Mode Control for Uncertain PAM Robot Arm Plant Enhanced with Evolutionary Technique

“…Authors in [28] applied DE technique for optimizing parameters of an adaptive evolutionary neural controller used in an uncertain nonlinear serial PAM robot system. Son et al [29] applied a hybrid fuzzy LQR-SMC technique optimized with modified DE to control uncertain nonlinear systems. Recently, several advanced intelligent digital twin-based controllers, including fault-tolerant tracking T-S fuzzy-based control against sensor faults introduced by Kukurowski et al [30] and digital twin-based optimal fuzzy control proposed by Guerra et al [31], Lermer and Reich [32], have obtained promising results in adaptively and precisely optimal control of highly uncertain nonlinear systems.…”