Development of an Adaptive Trajectory Tracking Control of Wheeled Mobile Robot

Abstract

Classical modeling and control methods applied to differential locomotion mobile robots generate mathematical equations that approximate the dynamics of the system and work relatively well when the system is linear in a specific range. However, they may have low accuracy when there are many variations of the dynamics over time or disturbances occur. To solve this problem, we used a recursive least squares (RLS) method that uses a discrete-time structure first-order autoregressive model with exogenous variable (ARX). We design and modify PID adaptive self-adjusting controllers in phase margin and pole allocation. The main contribution of this methodology is that it allows the permanent and online update of the robot model and the parameters of the adaptive self-adjusting PID controllers. In addition, a Lyapunov stability analysis technique was implemented for path and trajectory tracking control, this makes the errors generated in the positioning and orientation of the robot when performing a given task tend asymptotically to zero. The performance of the PID adaptive self-adjusting controllers is measured through the implementation of the criteria of the integral of the error, which allows to determine the controller of best performance, being in this case, the PID adaptive self-adjusting type in pole assignment, allowing the mobile robot greater precision in tracking the trajectories and paths assigned, as well as less mechanical and energy wear, due to its smooth and precise movements.

Criterios de desempeño para evaluar algoritmos de navegación de robots móviles: una revisión

“…Como criterio de comparación también se puede usar una función de costo o un mapa de costo. Una función de costo puede ser definida por el esfuerzo de los actuadores en alcanzar la trayectoria de referencia (Suarez et al, 2021), también puede ser determinada para optimizar el consumo energético (Serralheiro et al, 2019), etc.…”

Designing New Model-Based Adaptive Sliding Mode Controllers for Trajectory Tracking Control of an Unmanned Ground Vehicle

“…However, in real applications, it is difficult to use SMC alone for UGV trajectory tracking control due to the unknown system parameters, nonlinear UGV model and disturbances. Therefore, combining SMC with other control methods such as neural control, fuzzy control and adaptive control has been considered [20], [21], [22], [23], [24] and the adaptive control method is often preferred because it can adapt to changing system conditions [25], [26], [27]. This paper presents three new ASMC based on dynamic models for trajectory tracking control of UGVs.…”

Z-Number-Based Fuzzy Logic Approach for Mobile Robot Navigation

“…Nossier et al present path-planning algorithms [50] and multi-obstacle avoidance strategies [51] grounded in support vector machines, catering to dynamic environments and autonomous vehicle safety. Meanwhile, Suarez-Rivera et al [52] delve into adaptive trajectory tracking for wheeled mobile robots, addressing the challenges of real-time control. Parque's et al work [53] explores advanced fairness functionals for smooth path planning, highlighting the ongoing efforts to enhance the efficiency and fairness of autonomous navigation algorithms.…”