Abstract
Motion control involving DC motors requires a closed-loop system with a suitable compensator if tracking performance with high precision is desired. In the case where structural model errors of the motors are more dominating than the effects from noise disturbances, accurate system modelling will be a considerable aid in synthesizing the compensator. The focus of this paper is on enhancing the tracking performance of a wheeled mobile robot (WMR), which is driven by two DC motors that are subject to model parametric uncertainties and uncertain deadzones. For the system at hand, the uncertain nonlinear perturbations are greatly induced by the time-varying power supply, followed by behaviour of motion and speed. In this work, the system is firstly modelled, where correlations between the model parameters and different input datasets as well as voltage supply are obtained via polynomial regressions. A robust -fuzzy logic approach is then proposed to treat the issues due to the aforementioned perturbations. Via the proposed strategy, the controller and the fuzzy logic (FL) compensator work in tandem to ensure the control law is robust against the model uncertainties. The proposed technique was validated via several real-time experiments, which showed that the speed and path tracking performance can be considerably enhanced when compared with the results via the controller alone, and the with the FL compensator, but without the presence of the robust control law.
Highlights
Motor systems play a foundational role for precise positioning and motion control in robotics and automation, but they are usually subject to nonlinearities, disturbances, as well as sensors’and environmental noise [1,2]
The main contribution of this work is on enhancing the tracking performance of a wheeled mobile robot (WMR), which is driven by two DC motors that are subject to structural model errors that are induced by time-varying power supply as well as behaviour of motions and motor speed
A robust H∞ -fuzzy logic compensator is proposed to enhance the speed and tracking performance of a WMR, which is driven by two DC motors that are subject to model parametric uncertainties and varying deadzones
Summary
Motor systems play a foundational role for precise positioning and motion control in robotics and automation, but they are usually subject to nonlinearities, disturbances, as well as sensors’and environmental noise [1,2]. DC motors in particular are typically vulnerable to the mentioned perturbations; they can vary widely in performance, they are constructed by the same manufacturer using similar raw materials [3,4]. In this regard, many efforts have been devoted towards modelling DC motors in order to ensure the associated closed-loop systems can operate at varying conditions while preserving the desired performance [5]. Deadzone is one of the most common actuator nonlinearities present at the input of DC motors [9] This nonlinearity may lead to undesirable effects on closed-loop dynamics and control performance under certain regions. A series of control schemes have been developed to alleviate these problems ranging from intelligent [10] to modern robust control approaches [11]
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