Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1401.6220

Maximally persistent connections for the periodic type

Abdul Basit Memon·Erik I. Verriest
View original source ↗Read source document ↗Export metadata ↓

Abstract

This paper considers the optimal control problem of connecting two periodic trajectories with maximal persistence. A maximally persistent trajectory is close to the periodic type in the sense that the norm of the image of this trajectory under the operator defining the periodic type is minimal among all trajectories. A solution is obtained in this paper for the case when the two trajectories have the same period but it turns out to be only piecewise continuous and so an alternate norm is employed to obtain a continuous connection. The case when the two trajectories have different but rational periods is also solved. The problem of connecting periodic trajectories is of interest because of the observation that the operating points of many biological and artificial systems are limit cycles and so there is a need for a unified optimal framework of connections between different operating points. This paper is a first step towards that goal.

Explore related subjects

eess.SYcs.SYmath.OC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Abdul Basit Memon, Erik I. Verriest. 2014-01-24. Maximally persistent connections for the periodic type. https://arxiv.org/abs/1401.6220

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Safe learning-based control via function-based uncertainty quantification

Uncertainty quantification is essential when deploying learning-based control methods in safety-critical systems. This is commonly realized by constructing uncertainty tubes that enclose the unknown function of interest, e.g., the reward and constraint functions or the underlying dynamics model, with high probability. However, existing approaches for uncertainty quantification typically rely on restrictive assumptions that encode smoothness properties of the unknown function, such as a known norm in a function space. Moreover, these methods usually struggle with discontinuities. In this paper, we model the unknown function as a random function from which independent and identically distributed realizations can be generated. We then construct uncertainty tubes via the scenario approach that hold with high probability. Our uncertainty tubes rely solely on sampled realizations and can therefore accommodate discontinuities represented by the sampling model. We integrate these uncertainty tubes into a safe Bayesian optimization algorithm with which we safely tune control parameters on a real Furuta pendulum.

eess.SY↗
arXiv2026

Enhanced ShockBurst for Ultra Low-Power On-Demand Sensing

On-demand sensing requires battery-powered Internet-of-Things (IoT) and implantable medical devices to remain in deep sleep and activate wireless communication only when data transmission is required. In such systems, battery lifetime depends strongly on radio active time. This work investigates how communication architecture and physical layer (PHY) configuration influence radio active time by comparing connection-oriented Bluetooth Low Energy (BLE) with connectionless Enhanced ShockBurst (ESB) on identical BLE-compatible hardware. Under identical 2 Mbps PHY configurations, ESB reduces wake-up latency and energy consumption to approximately one-twentieth of BLE by eliminating connection establishment and maintenance overhead. Increasing the ESB PHY rate from 2 to 4 Mbps further shortens packet airtime by approximately 52% and reduces transmission energy by approximately 43%. Finally, a first-in, first-out (FIFO)-triggered implantable loop recorder prototype demonstrates that jointly optimizing communication architecture, PHY configuration, and buffered transmission enables sleep-wake operation and reduces total system power consumption by approximately 60% compared with conventional BLE operation. These results identify minimizing radio active time as a key design principle for ultra-low-power on-demand sensing and provide practical guidance for battery-powered sensing systems.

eess.SY↗
arXiv2026

Receding Horizon Multi-Agent Deceptive Path Planner

Deceptive path planning enables autonomous agents to obscure their true goals from observers by deviating from an expected optimal path. Prior work largely solves full-horizon, end-to-end optimization for single agents, which is expensive to recompute online and difficult to scale or adapt en route. We propose a unified framework for deceptive path planning using a Boltzmann distribution, computing over short-horizon candidate trajectories within a receding-horizon loop. By param- By iterating a user-defined cost that captures deception, resources, and smoothness, and optionally includes coupling terms between agents, the framework yields stochastic policies that balance the tradeoff between optimal paths and deceptive deviation. Policies are updated locally and do not require training. The level of deception and adherence to constraints can be dynamically tuned, enabling online adaptation to changes in goals and constraints such as obstacles. This step-by-step tuning opens the door to new forms of dynamic deception. Simulation studies demonstrate the flexibility of our approach, maintaining deception while adapting to environmental and constraint updates, avoiding the recomputation required by full-horizon methods, and supporting intuitive tuning via a small set of parameters

eess.SY↗