Category Archives: Systems Engineering

A formal definition of autonomy and of its degrees

Antsaklis, P.J. & Rahnama, A. , Control and Machine Intelligence for System Autonomy, Journal of Intelligent & Robotic Systems
July 2018, Volume 91, Issue 1, pp 23–34 DOI: 10.1007/s10846-018-0832-6.

Autonomous systems evolve from control systems by adding functionalities that increase the level of system autonomy. It is very important to the research in the field that autonomy be well defined and so in the present paper a precise, useful definition of autonomy is introduced and discussed. Autonomy is defined as the ability of the system to attain a set of goals under a set of uncertainties. This leads to the notion of degrees or levels of autonomy. The Quest for Autonomy in engineered systems throughout the centuries is noted, connections to research work of 30 years ago are made and a hierarchical functional architecture for autonomous systems together with needed functionalities are outlined. Adaptation and Learning, which are among the most important functions in achieving high levels of autonomy are then highlighted and recent research contributions are briefly discussed.

A survey on the concept of Entropy as a measure of the intelligence and autonomy of a system, modeled hierarchically

Valavanis, K.P., The Entropy Based Approach to Modeling and Evaluating Autonomy and Intelligence of Robotic Systems, J Intell Robot Syst (2018) 91: 7 DOI: 10.1007/s10846-018-0905-6.

This review paper presents the Entropy approach to modeling and performance evaluation of Intelligent Machines (IMs), which are modeled as hierarchical, multi-level structures. It provides a chronological summary of developments related to intelligent control, from its origins to current advances. It discusses fundamentals of the concept of Entropy as a measure of uncertainty and as a control function, which may be used to control, evaluate and improve through adaptation and learning performance of engineering systems. It describes a multi-level, hierarchical, architecture that is used to model such systems, and it defines autonomy and machine intelligence for engineering systems, with the aim to set foundations necessary to tackle related challenges. The modeling philosophy for the systems under consideration follows the mathematically proven principle of Increasing Precision with Decreasing Intelligence (IPDI). Entropy is also used in the context of N-Dimensional Information Theory to model the flow of information throughout such systems and contributes to quantitatively evaluate uncertainty, thus, autonomy and intelligence. It is explained how Entropy qualifies as a unique, single, measure to evaluate autonomy, intelligence and precision of task execution. The main contribution of this review paper is that it brings under one forum research findings from the 1970’s and 1980’s, and that it supports the argument that even today, given the unprecedented existing computational power, advances in Artificial Intelligence, Deep Learning and Control Theory, the same foundational framework may be followed to study large-scale, distributed Cyber Physical Systems (CPSs), including distributed intelligence and multi-agent systems, with direct applications to the SmartGrid, transportation systems and multi-robot teams, to mention but a few applications.

Interesting approach to deal with the design of complex systems based on analogies with simpler ones

Victor Ragusila, M. Reza Emami, Mechatronics by analogy and application to legged locomotion, Mechatronics, Volume 35, May 2016, Pages 173-191, ISSN 0957-4158, DOI: 10.1016/j.mechatronics.2016.02.007.

A new design methodology for mechatronic systems, dubbed as Mechatronics by Analogy (MbA), is introduced. It argues that by establishing a similarity relation between a complex system and a number of simpler models it is possible to design the former using the analysis and synthesis means developed for the latter. The methodology provides a framework for concurrent engineering of complex systems while maintaining the transparency of the system behavior through making formal analogies between the system and those with more tractable dynamics. The application of the MbA methodology to the design of a monopod robot leg, called the Linkage Leg, is also presented. A series of simulations show that the dynamic behavior of the Linkage Leg is similar to that of a combination of a double pendulum and a spring-loaded inverted pendulum, based on which the system kinematic, dynamic, and control parameters can be designed concurrently.