Adaptive Fuzzy Type-II Controller for Wheeled Mobile Robot with Disturbances and Wheelslips

Abstract

This paper proposed adaptive fuzzy type-II controllers for the wheeled mobile robot (WMR) systems under conditions of wheel slips and disturbances. The system includes two control loops: outer loop for position tracking and the inner loop for velocity tracking. In each loop, the controller has two parts: the feedback which keeps the system stable and the adaptive type-II fuzzy part which is used to compensate the unknown components that act on the system. The stability of each loop as well as the overall system is proven mathematically based on the Lyapunov theory. Finally, the simulation is setup to verify the effectiveness of the presented algorithm. The simulation results show that, in comparison with the corresponding fuzzy type-I controller, the performance of the adaptive fuzzy type-II controller is better, i.e., the position error is smaller and the velocity is almost smooth under the conditions that the reference trajectory is changed, and the system is affected by wheel slips and external disturbances.

An improved fuzzy inference strategy using reinforcement learning for trajectory-tracking of a mobile robot under a varying slip ratio

“…However, numerous existing works such as refs. [14,16,17,21,23,[29][30][31][32][33][34][35] do not demonstrate experimental results.…”

“…However, as documented by refs. [32][33][34][35], the fuzzy inference system (FIS) can be very complex and the performance is highly limited on the designer's knowledge. Therefore, it becomes imperative to explore alternative approaches to tune the FIS for optimal outcomes.…”

“…Fuzzy logic control (FLC) presents an advantageous alternative in these circumstances because it limits the inputs within the range of the predefined membership functions as demonstrated in refs. [25][26][27][28][29][30][31][32][33][34][35]. The membership functions are typically designed to overlap with one another.…”

“…In addition, unlike prior works in refs. [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] which completely rely on designers' expert knowledge to relate the fuzzy inputs and outputs, this study avoids complete dependency on expert knowledge by utilizing the gathered knowledge of an RL agent after extensive training in a simulated environment.…”

Adaptive sliding mode control for uncertain wheel mobile robot

“…Figure 1 is the block diagram of the system. In considering to the wheel slips and input disturbance, the kinematic and dynamic of the WMR in the coordinate of Figure 1 is expressed as (1) and (2) [29], [30]:…”