Autonomous system (mathematics)

About: Autonomous system (mathematics) is a research topic. Over the lifetime, 1648 publications have been published within this topic receiving 38373 citations.

Lyapunov-based fractional-order controller design to synchronize a class of fractional-order chaotic systems

Abstract: In this paper, a novel adaptive fractional-order feedback controller is first developed by extending an adaptive integer-order feedback controller. Then a simple but practical method to synchronize almost all familiar fractional-order chaotic systems has been put forward. Through rigorous theoretical proof by means of the Lyapunov stability theorem and Barbalat lemma, sufficient conditions are derived to guarantee chaos synchronization. A wide range of fractional-order chaotic systems, including the commensurate system and incommensurate case, autonomous system, and nonautonomous case, is just the novelty of this technique. The feasibility and validity of presented scheme have been illustrated by numerical simulations of the fractional-order Chen system, fractional-order hyperchaotic Lu system, and fractional-order Duffing system.

Enhancing Digital Twins through Reinforcement Learning

Abstract: Digital Twins are core enablers of smart and autonomous manufacturing systems. Although they strive to represent their physical counterpart as accurately as possible, slight model or data errors will remain. We present an algorithm to compensate for those residual errors through Reinforcement Learning (RL) and data fed back from the manufacturing system. When learning, the Digital Twin acts as teacher and safety policy to ensure minimal performance. We test the algorithm in a sheet metal assembly context, in which locators of the fixture are optimally adjusted for individual assemblies. Our results show a fast adaption and improved performance of the autonomous system.

Toward a More Dependable Software Architecture for Autonomous Robots

Abstract: Autonomous robots are complex systems that require the interaction/cooperation of numerous heterogeneous software components. Nowadays, robots are getting closer to humans and as such are becoming critical systems which must meet safety properties including in particular logical, temporal and real-time constraints. We present an evolution of the LAAS Architecture for Autonomous System and its tool GenoM. This evolution is based on the BIP (Behaviors Interactions Priorities) component based design framework which has been successfully used in other domains (e.g. embedded systems). In this study, we show how we seamlessly integrate BIP in the preexisting methodology. We present the componentization of the functional level of a robot, the synthesis of an execution controller as well as validation techniques for checking essential safety properties. This approach has been integrated in the LAAS architecture and we have performed a number of experiment in simulation but also on a real robot (DALA).

Generative Modeling of Autonomous Robots and their Environments using Reservoir Computing

Abstract: Autonomous mobile robots form an important research topic in the field of robotics due to their near-term applicability in the real world as domestic service robots. These robots must be designed in an efficient way using training sequences. They need to be aware of their position in the environment and also need to create models of it for deliberative planning. These tasks have to be performed using a limited number of sensors with low accuracy, as well as with a restricted amount of computational power. In this contribution we show that the recently emerged paradigm of Reservoir Computing (RC) is very well suited to solve all of the above mentioned problems, namely learning by example, robot localization, map and path generation. Reservoir Computing is a technique which enables a system to learn any time-invariant filter of the input by training a simple linear regressor that acts on the states of a high-dimensional but random dynamic system excited by the inputs. In addition, RC is a simple technique featuring ease of training, and low computational and memory demands.