Commit your works to the Lord, and your thoughts shall be established (Prov. 16:3). Inter-stable control systems

Algebraic structures are discussed for control systems that maintain stability in the presence of resonance uncertainties. Dual algebraic operations serve as elementary connections that propagate the stability of inter-stable subsystems. Frequency responses within complex half-planes define different types of inter-stability. Dominance between incompatible types is discussed. Inter-stability produces sufficient but unnecessary stability conditions, except for conservative systems where the conditions become also necessary. Multivariable systems, colocation of actuator and sensor, and virtual colocation are treated. Instead of passivity, inter-stability relates stability to the mapping of poles and zeros by transfer functions and transfer matrices. Inter-stability determines stability on the subsystem level, is less complex even for multivariable systems, adds design flexibility, and relaxes the dynamic data problem of large systems such as space stations

Cluster synchronization of diffusively-coupled nonlinear systems: A contraction based approach

Finding the conditions that foster synchronization in networked oscillatory systems is critical to understanding a wide range of biological and mechanical systems. However, the conditions proved in the literature for synchronization in nonlinear systems with linear coupling, such as has been used to model neuronal networks, are in general not strict enough to accurately determine the system behavior. We leverage contraction theory to derive new sufficient conditions for cluster synchronization in terms of the network structure, for a network where the intrinsic nonlinear dynamics of each node may differ. Our result requires that network connections satisfy a cluster-input-equivalence condition, and we explore the influence of this requirement on network dynamics. For application to networks of nodes with neuronal spiking dynamics, we show that our new sufficient condition is tighter than those found in previous analyses which used nonsmooth Lyapunov functions. Improving the analytical conditions for when cluster synchronization will occur based on network configuration is a significant step toward facilitating understanding and control of complex oscillatory systems

The GRAVITY instrument software / High-level software

GRAVITY is the four-beam, near- infrared, AO-assisted, fringe tracking, astrometric and imaging instrument for the Very Large Telescope Interferometer (VLTI). It is requiring the development of one of the most complex instrument software systems ever built for an ESO instrument. Apart from its many interfaces and interdependencies, one of the most challenging aspects is the overall performance and stability of this complex system. The three infrared detectors and the fast reflective memory network (RMN) recorder contribute a total data rate of up to 20 MiB/s accumulating to a maximum of 250 GiB of data per night. The detectors, the two instrument Local Control Units (LCUs) as well as the five LCUs running applications under TAC (Tools for Advanced Control) architecture, are interconnected with fast Ethernet, RMN fibers and dedicated fiber connections as well as signals for the time synchronization. Here we give a simplified overview of all subsystems of GRAVITY and their interfaces and discuss two examples of high-level applications during observations: the acquisition procedure and the gathering and merging of data to the final FITS file.Comment: 8 pages, 7 figures, published in Proc. SPIE 9146, Optical and Infrared Interferometry IV, 91462

Automation and Robotics: Latest Achievements, Challenges and Prospects

This SI presents the latest achievements, challenges and prospects for drives, actuators, sensors, controls and robot navigation with reverse validation and applications in the field of industrial automation and robotics. Automation, supported by robotics, can effectively speed up and improve production. The industrialization of complex mechatronic components, especially robots, requires a large number of special processes already in the pre-production stage provided by modelling and simulation. This area of research from the very beginning includes drives, process technology, actuators, sensors, control systems and all connections in mechatronic systems. Automation and robotics form broad-spectrum areas of research, which are tightly interconnected. To reduce costs in the pre-production stage and to reduce production preparation time, it is necessary to solve complex tasks in the form of simulation with the use of standard software products and new technologies that allow, for example, machine vision and other imaging tools to examine new physical contexts, dependencies and connections

Connectivity Influences on Nonlinear Dynamics in Weakly-Synchronized Networks: Insights from Rössler Systems, Electronic Chaotic Oscillators, Model and Biological Neurons

Natural and engineered networks, such as interconnected neurons, ecological and social networks, coupled oscillators, wireless terminals and power loads, are characterized by an appreciable heterogeneity in the local connectivity around each node. For instance, in both elementary structures such as stars and complex graphs having scale-free topology, a minority of elements are linked to the rest of the network disproportionately strongly. While the effect of the arrangement of structural connections on the emergent synchronization pattern has been studied extensively, considerably less is known about its influence on the temporal dynamics unfolding within each node. Here, we present a comprehensive investigation across diverse simulated and experimental systems, encompassing star and complex networks of Rössler systems, coupled hysteresis-based electronic oscillators, microcircuits of leaky integrate-and-fire model neurons, and finally recordings from in-vitro cultures of spontaneously-growing neuronal networks. We systematically consider a range of dynamical measures, including the correlation dimension, nonlinear prediction error, permutation entropy, and other information-theoretical indices. The empirical evidence gathered reveals that under situations of weak synchronization, wherein rather than a collective behavior one observes significantly differentiated dynamics, denser connectivity tends to locally promote the emergence of stronger signatures of nonlinear dynamics. In deterministic systems, transition to chaos and generation of higher-dimensional signals were observed; however, when the coupling is stronger, this relationship may be lost or even inverted. In systems with a strong stochastic component, the generation of more temporally-organized activity could be induced. These observations have many potential implications across diverse fields of basic and applied science, for example, in the design of distributed sensing systems based on wireless coupled oscillators, in network identification and control, as well as in the interpretation of neuroscientific and other dynamical data

Neuroscience out of control: control-theoretic perspectives on neural circuit dynamics.

A major challenge in systems neuroscience is to understand how the dynamics of neural circuits give rise to behaviour. Analysis of complex dynamical systems is also at the heart of control engineering, where it is central to the design of robust control strategies. Although a rich engineering literature has grown over decades to facilitate the analysis of such systems, little of it has percolated into neuroscience so far. Here, we give a brief introduction to a number of core control-theoretic concepts that provide useful perspectives on neural circuit dynamics. We introduce important mathematical tools related to these concepts, and establish connections to neural circuit analysis, focusing on a number of themes that have arisen from the modern 'state-space' view on neural population dynamics

Adaptive neuro-fuzzy model with fuzzy clustering for nonlinear prediction and control

Nonlinear systems have more complex manner and profoundness than linear systems.Thus, their analyses are much more difficult.This paper presents the use of neuro-fuzzy networks as means of implementing algorithms suitable for nonlinear black-box prediction and control.In engineering applications, two attractive tools have emerged recently.These two attractive tools are: the artificial neural networks and the fuzzy logic system. One area of particular importance is the design of networks capable of modeling and predicting the behavior of systems that involve complex, multi-variable processes.To illustrate the applicability of the neuro-fuzzy networks, a case study involving air-fuel ratio is presented here.Air- fuel ratio represents complex, nonlinear and stochastic behavior.To monitor the engine conditions, an adaptive neuro-fuzzy inference system (ANFIS) is used to capture the nonlinear connections between the air- fuel ratio and control parameters such manifold air pressure, throttle position, manifold air temperature, engine temperature, engine speed, and injection opening time.This paper describes a fuzzy clustering method to initialize the ANFIS

Leadership for Emergence: Exploring organisations through a living system lens

In this article, we outline a research project with adolescent-focused NGOs (non-government organisations) in Christchurch, New Zealand. This project involved 25 managers who used appreciative inquiry process methodology to explore their leadership practices, beliefs, and values. Throughout the article, we construct a conceptual leadership frame for fostering the emergence of adaptive, innovative and responsive organisational capacity that allows organisations to more readily adapt to the complex and changing conditions in which they operate. We describe this frame as a living system lens that is based on viewing organisations as complex adaptive systems of the kind readily found in the natural world. We go on to outline the leaders’ reflections as they drew strong connections between the dynamics found in complex adaptive systems and their own organisations. Proactive mentoring, fostering interaction and shared learning, strategies for distributing power and decentralising control, and exploration and articulation of deeply held values emerged as the key leadership enactments that these leaders implemented in their roles