8,609 research outputs found
Discrete Mechanics and Optimal Control Applied to the Compass Gait Biped
This paper presents a methodology for generating locally optimal control policies for simple hybrid mechanical systems, and illustrates the method on the compass gait biped. Principles from discrete mechanics are utilized to generate optimal control policies as solutions of constrained nonlinear optimization problems. In the context of bipedal walking, this procedure provides a comparative measure of the suboptimality of existing control policies. Furthermore, our methodology can be used as a control design tool; to demonstrate this, we minimize the specific cost of transport of periodic orbits for the compass gait biped, both in the fully actuated and underactuated case
On-line Non-stationary Inventory Control using Champion Competition
The commonly adopted assumption of stationary demands cannot actually reflect
fluctuating demands and will weaken solution effectiveness in real practice. We
consider an On-line Non-stationary Inventory Control Problem (ONICP), in which
no specific assumption is imposed on demands and their probability
distributions are allowed to vary over periods and correlate with each other.
The nature of non-stationary demands disables the optimality of static (s,S)
policies and the applicability of its corresponding algorithms. The ONICP
becomes computationally intractable by using general Simulation-based
Optimization (SO) methods, especially under an on-line decision-making
environment with no luxury of time and computing resources to afford the huge
computational burden. We develop a new SO method, termed "Champion Competition"
(CC), which provides a different framework and bypasses the time-consuming
sample average routine adopted in general SO methods. An alternate type of
optimal solution, termed "Champion Solution", is pursued in the CC framework,
which coincides the traditional optimality sense under certain conditions and
serves as a near-optimal solution for general cases. The CC can reduce the
complexity of general SO methods by orders of magnitude in solving a class of
SO problems, including the ONICP. A polynomial algorithm, termed "Renewal Cycle
Algorithm" (RCA), is further developed to fulfill an important procedure of the
CC framework in solving this ONICP. Numerical examples are included to
demonstrate the performance of the CC framework with the RCA embedded.Comment: I just identified a flaw in the paper. It may take me some time to
fix it. I would like to withdraw the article and update it once I finished.
Thank you for your kind suppor
Model-based dependability analysis : state-of-the-art, challenges and future outlook
Abstract: Over the past two decades, the study of model-based dependability analysis has gathered significant research interest. Different approaches have been developed to automate and address various limitations of classical dependability techniques to contend with the increasing complexity and challenges of modern safety-critical system. Two leading paradigms have emerged, one which constructs predictive system failure models from component failure models compositionally using the topology of the system. The other utilizes design models - typically state automata - to explore system behaviour through fault injection. This paper reviews a number of prominent techniques under these two paradigms, and provides an insight into their working mechanism, applicability, strengths and challenges, as well as recent developments within these fields. We also discuss the emerging trends on integrated approaches and advanced analysis capabilities. Lastly, we outline the future outlook for model-based dependability analysis
Invisible control of self-organizing agents leaving unknown environments
In this paper we are concerned with multiscale modeling, control, and
simulation of self-organizing agents leaving an unknown area under limited
visibility, with special emphasis on crowds. We first introduce a new
microscopic model characterized by an exploration phase and an evacuation
phase. The main ingredients of the model are an alignment term, accounting for
the herding effect typical of uncertain behavior, and a random walk, accounting
for the need to explore the environment under limited visibility. We consider
both metrical and topological interactions. Moreover, a few special agents, the
leaders, not recognized as such by the crowd, are "hidden" in the crowd with a
special controlled dynamics. Next, relying on a Boltzmann approach, we derive a
mesoscopic model for a continuum density of followers, coupled with a
microscopic description for the leaders' dynamics. Finally, optimal control of
the crowd is studied. It is assumed that leaders exploit the herding effect in
order to steer the crowd towards the exits and reduce clogging. Locally-optimal
behavior of leaders is computed. Numerical simulations show the efficiency of
the optimization methods in both microscopic and mesoscopic settings. We also
perform a real experiment with people to study the feasibility of the proposed
bottom-up crowd control technique.Comment: in SIAM J. Appl. Math, 201
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