Search Results (4)

The growing reliance on mobile robots has resulted in applications where users have limited or no control over operating conditions. These applications require advanced controllers to ensure the system’s performance by dynamically changing its parameters. Nowadays, online bioinspired controller tuning approaches are among the most successful and innovative tools for dealing with uncertainties and disturbances. Nevertheless, these bioinspired approaches present a main limitation in real-world applications due to the extensive computational resources required in their exhaustive search when evaluating the controller tuning of complex dynamics. This paper develops an online bioinspired controller tuning approach leveraging a surrogate modeling strategy for an omnidirectional mobile robot controller. The polynomial response surface method is incorporated as an identification stage to model the system and predict its behavior in the tuning stage of the indirect adaptive approach. The comparative analysis concerns state-of-the-art controller tuning approaches, such as online, offline robust, and offline non-robust approaches, based on bioinspired optimization. The results show that the proposal reduces its computational load by up to $62.85\%$ while maintaining the controller performance regarding the online approach under adverse uncertainties and disturbances. The proposal also increases the controller performance by up to $93\%$ compared to offline tuning approaches. Then, the proposal retains its competitiveness on mobile robot systems under adverse conditions, while other controller tuning approaches drop it. Furthermore, a posterior comparison against another surrogate tuning approach based on Gaussian process regression corroborates the proposal as the best online controller tuning approach by reducing the competitor’s computational load by up to $91.37\%$ while increasing its performance by $63\%$. Hence, the proposed controller tuning approach decreases the execution time to be applied in the evolution of the control system without deteriorating the closed-loop performance. To the best of the authors’ knowledge, this is the first time that such a controller tuning strategy has been tested on an omnidirectional mobile robot. Full article

Chemical reactions with oscillating behavior can present a chaos state in specific conditions. In this study, we analyzed the dynamic of the chaotic Belousov–Zhabotinsky (BZ) reaction using the Györgyi–Field model in order to identify the conditions of the chaos behavior. We studied the behavior of the reaction under different parameters that included both a low and high flux of chemical species. We performed our analysis of the flow regime in the conditions of an open reaction system, as this provides information about the behavior of the reaction over time. The proposed method for determining the favorable conditions for obtaining the state of chaos is based on the time evolution of the intermediate species and phase portraits. The synchronization of two Györgyi–Field systems based on the adaptive feedback method of control is presented in this work. The transient time until synchronization depends on the initial conditions of the two systems and on the strength of the controllers. Among the areas of interest for possible applications of the control method described in this paper, we can include identification of the reaction parameters and the extension to the other chaotic systems. Full article

This paper presents a stochastic model for the least-mean-square algorithm with symmetric/antisymmetric properties (LMS-SAS), operating in a system identification setup with Gaussian input data. Specifically, model expressions are derived to describe the mean weight behavior of the (global and virtual) adaptive filters, learning curves, and evolution of some correlation-like matrices, which allow predicting the algorithm behavior. Simulation results are shown and discussed, confirming the accuracy of the proposed model for both transient and steady-state phases. Full article

Currently, abnormality detection and/or prediction is a very hot topic. In this paper, we addressed it in the frame of activity monitoring of a human in bed. This paper presents a comprehensive formulation of a requirements engineering dossier for a monitoring system of a “human in bed” for abnormal behavior detection and forecasting. Hereby, practical and real-world constraints and concerns were identified and taken into consideration in the requirements dossier. A comprehensive and holistic discussion of the anomaly concept was extensively conducted and contributed to laying the ground for a realistic specifications book of the anomaly detection system. Some systems engineering relevant issues were also briefly addressed, e.g., verification and validation. A structured critical review of the relevant literature led to identifying four major approaches of interest. These four approaches were evaluated from the perspective of the requirements dossier. It was thereby clearly demonstrated that the approach integrating graph networks and advanced deep-learning schemes (Graph-DL) is the one capable of fully fulfilling the challenging issues expressed in the real-world conditions aware specification book. Nevertheless, to meet immediate market needs, systems based on advanced statistical methods, after a series of adaptations, already ensure and satisfy the important requirements related to, e.g., low cost, solid data security and a fully embedded and self-sufficient implementation. To conclude, some recommendations regarding system architecture and overall systems engineering were formulated. Full article