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No abstract

Idealized passive dynamic walkers (PDW) exhibit limit cycle stability at steady state. Yet in reality, uncertainty in ground interaction forces result in variability in limit cycles even for a simple walker known as the Rimless Wheel (RW) on seemingly even slopes. This class of walkers is called metastable walkers in that they usually walk in a stable limit cycle, though guaranteed to eventually fail. Thus, control action is only needed if a failure state (i.e. RW stopping down the ramp) is imminent. Therefore, efficiency of estimating the time to reach a failure state is key to develop a minimal intervention controller to inject just enough energy to overcome a failure state when required. Current methods use what is known as a Mean First Passage Time (MFPT) from current state (rotary speed of RW at the most recent leg collision) to an arbitrary state deemed to be a failure in the future. The frequently used Markov chain based MFPT prediction requires an absorbing state, which in this case is a collision where the RW comes to a stop without an escape. Here, we propose a novel method to estimate an MFPT from current state to an arbitrary state which is not necessarily an absorbing state. This provides freedom to a controller to adaptively take action when deemed necessary. We demonstrate the proposed MFPT predictions in a minimal intervention controller for a RW. Our results show that the proposed method is useful in controllers for walkers showing up to 44.1% increase of time-to-fail compared to a PID based closed-loop controller.

The effectiveness of managing cyclone-induced floods is highly dependent on how fast reasonably accurate predictions can be made, which is a particularly difficult task given the multitude of highly variable physical factors. Even with supercomputers, collecting and processing vast amounts of data from numerous asynchronous sources makes it challenging to achieve high prediction efficiency. This paper presents a model that combines prior knowledge, including rainfall data statistics and topographical features, with any new precipitation data to generate a probabilistic prediction using Bayesian learning, where the advantages of dataoriented and heuristic modeling are combined. The terrain is partitioned into geographic primitives (GPs) based on manual inspection of flood propagation vector fields in order to simplify the stochastic system identification. High calculation efficiency is achieved through statistically summarizing simultaneous events spread across geography into primitives, allowing a distributed updating algorithm leading to parallel computing. Markov chain processes identified for each of these GPs, based on both simulation and measured rainfall data, are then used in real-time predictions of water flow probabilities. The model takes a comprehensive approach, which enables flood prediction even before the landfall of a cyclone through modularizing the algorithm into three prediction steps: cyclone path, rainfall probability density distribution, and temporal dynamics of flood density distribution. Results of comparative studies based on real data of two cyclones (Yasi and Tasha) that made landfall in Queensland, Australia, in 2010/11 show that the model is capable of predicting up to 3 h ahead of the official forecast, with a 33% improvement of accuracy compared to the models presently being used.

No abstract