Stabilizing control of two-wheeled wheelchair with movable payload using optimized interval type-2 fuzzy logic

Abstract

The control schemes of a wheelchair having two wheels with movable payload utilizing the concept of a double-link inverted pendulum have been investigated in this article. The proposed wheelchair has been simulated using SimWise 4D software considering the most efficient parameters. These parameters are extracted using the spiral dynamic algorithm while being controlled with interval type-2 fuzzy logic controller (IT2FLC). The robustness and stability of the implemented controller are assessed under different situations including standing upright, forward motion and application of varying directions and magnitudes of outer disturbances to movable (up and down) system payload. It is shown that the two-wheeled wheelchair adopted by the newly introduced controller has achieved a 94% drop in torque for both Link1 and Link2 and more than 98% fall in distance travelled in comparison with fuzzy logic control type-1 (FLCT1) controller employed in an earlier design. The present study has further considered the increased nonlinearity and complexity of the additional moving payload. From the outcome of this study, it is obvious that the proposed IT2FLC-spiral dynamic algorithm demonstrates better performance than FLCT1 to manage the uncertainties and nonlinearities in case of a movable payload two-wheel wheelchair system.

Discrete reference tracking control to swing up an electric wheelchair

“…Several works are available in the literature for two‐wheeled machines swing‐up. Takahashi et al [2, 3] consider PI and H2 controls, without any proof of stability/stabilization; the same observation for Chotikunnan and Panomruttanarug [4] and Jamin et al [5] using fuzzy logic control approaches. In Goher and Fadlallah [6], PID and PD‐fuzzy logic controls are investigated with real‐time experiments including some robustness issue according to the center of mass.…”

Robot Path Planning Based on Interval Type-2 Fuzzy Controller Optimized by an Improved Aquila Optimization Algorithm

“…In 1975, L. A. Zadeh proposed type-2 fuzzy sets [32]. An interval type-2 fuzzy set can be represented by the following formula in [33]:…”

Real-Time Metaheuristic Algorithm for Dynamic Fuzzification, De-Fuzzification and Fuzzy Reasoning Processes

“…This is because the growth of type-2 FLS uncertainty can be directly integrated into fuzzy sets, as described in Section 6. Furthermore, in the last three years of studies on higher-order types of FLS in particular, the designed and developed applications of interval type-2 fuzzy logic have increased significantly [48][49][50][51][52][53][54]. These type-2-based FLS applications have been identified in artificial intelligence (AI) [55][56][57][58][59], adaptive control [60][61][62][63][64][65][66], electric motor control [67][68][69][70][71][72], Internet of Things (IoT) [73][74][75][76][77], digital image processing [78][79][80][81][82][83][84] and other areas [85][86][87].…”