Offline robust tuning of the motion control for omnidirectional mobile robots

In recent years, mobile robots have been helpful systems to perform a wide variety of complex tasks in daily life applications from industry, academy, and home. These robots carry out mobility on flat terrains, mainly in narrow spaces that are difficult to access or dangerous for humans. Therefore, increasing the efficiency of their movements through control technologies has become a topic of great interest for researchers. Among controllers, the linear ones are widely used to improve the efficiency of mobile robots because of their simplicity, reliability, and practicality, notwithstanding advanced control strategies. A well-tuned linear controller can show outstanding performances in controlled environments where the modeled and simulated conditions used for its adjustment are not too far from reality. However, actual operating environments are subject to uncertainties and disturbances that can hardly be accounted for during the controller tuning process. The above compromises the performance of the mobile robot in practice, and finding the appropriate controller parameters that enhance robustness becomes a crucial task. Therefore, this work presents a robust tuning approach for the controller of an omnidirectional mobile robot based on the solution of a nonlinear dynamic optimization problem through meta-heuristics. Robustness is incorporated in the optimization problem by minimizing the sensitivity to the control performance indexes. Simultaneously, this is included through dynamic and stochastic variations in the meta-heuristic optimizer hyperparameters. A comparative statistical analysis is performed using robust and non-robust tuning approaches. Based on simulated and experimental tests, the proposed robust approach shows notable performance improvements regarding the non-robust one while minimizing operation errors in the presence of different uncertainty magnitudes.