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Animals, humans, and multi-robot systems operate in dynamic environments, where the ability to respond to changing circumstances is paramount. An effective collective response requires suitable information transfer among agents and thus critically depends on the interaction network. To investigate the influence of the network topology on collective response, we consider an archetypal model of distributed decision-making and study the capacity of the system to follow a driving signal for varying topologies and system sizes. Experiments with a swarm of robots reveal a nontrivial relationship between frequency of the driving signal and optimal network topology. The emergent collective response to slow-changing perturbations increases with the degree of the interaction network, but the opposite is true for the response to fast-changing ones. These results have far-reaching implications for the design and understanding of distributed systems: a dynamic rewiring of the interaction network is essential to effective collective operations at different time scales.

This paper addresses the problem of Multiple Model Adaptive Estimation (MMAE) for discrete-time, linear, time-invariant MIMO plants with parameter uncertainty and unmodeled dynamics. Model identification is analyzed in a deterministic setting by adopting a Minimum Energy selection criterion. The MMAE system relies on a finite number of local observers, each designed using a selected model (SM) from the original set of possibly infinite plant models. Results akin to those previously obtained in a stochastic setting are derived in a far simpler manner, in a deterministic framework. We show, under suitable distinguishability conditions, that the SM identified is the one that corresponds to the observer with smallest output prediction error energy. We also develop a procedure to analyze the behavior of MMAE when the true plant is not one of the SMs. This leads to an algorithm that computes, for each SM, the set of equivalently identified plants, that is, the set of plants that will be identified as that particular SM. The impact of unmodeled dynamics on model identification is discussed. Simulation results with a model of a motor coupled to a load via an elastic shaft illustrate the performance of the methodology derived.

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Abstract:The Robust Multiple Model Adaptive Control (RMMAC) methodology was first introduced in Fekri et al. [2006] for open-loop stable plants with parametric uncertainty and unmodeled dynamics subjected to external disturbances and measurement noise. This paper addresses the stability of RMMAC systems. We show, using concepts and analysis tools that borrow from Supervisory Control, that all closed-loop signals in a RMMAC system are bounded. It is further shown that robust performance is recovered in steady state.

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A research vessel (RV) plays an important role in many fields such as oceanography, fisheries and polar research, hydrographic surveys, and oil exploration. It also has a unique function in maritime research and developments. Full-scale sea trials that require vessels, are usually extremely expensive; however, research vessels are more available than other types of ship. This paper presents the results of a time-domain simulation model of R/V Gunnerus, the research vessel of the Norwegian University of Science and Technology (NTNU), using MARINTEK’s vessel simulator (VeSim). VeSim is a time-domain simulator which solves dynamic equations of vessel motions and takes care of seakeeping and manoeuvring problems simultaneously. In addition to a set of captive and PMM tests on a scale model of Gunnerus, full-scale sea trials are carried out in both calm and harsh weather and the proposed simulation model is validated against sea trial data.