25,554 research outputs found
The Bi-objective Periodic Closed Loop Network Design Problem
© 2019 Elsevier Ltd. This manuscript is made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0). For further details please see: https://creativecommons.org/licenses/by-nc-nd/4.0/Reverse supply chains are becoming a crucial part of retail supply chains given the recent reforms in the consumers’ rights and the regulations by governments. This has motivated companies around the world to adopt zero-landfill goals and move towards circular economy to retain the product’s value during its whole life cycle. However, designing an efficient closed loop supply chain is a challenging undertaking as it presents a set of unique challenges, mainly owing to the need to handle pickups and deliveries at the same time and the necessity to meet the customer requirements within a certain time limit. In this paper, we model this problem as a bi-objective periodic location routing problem with simultaneous pickup and delivery as well as time windows and examine the performance of two procedures, namely NSGA-II and NRGA, to solve it. The goal is to find the best locations for a set of depots, allocation of customers to these depots, allocation of customers to service days and the optimal routes to be taken by a set of homogeneous vehicles to minimise the total cost and to minimise the overall violation from the customers’ defined time limits. Our results show that while there is not a significant difference between the two algorithms in terms of diversity and number of solutions generated, NSGA-II outperforms NRGA when it comes to spacing and runtime.Peer reviewedFinal Accepted Versio
Distributed Event-Based State Estimation for Networked Systems: An LMI-Approach
In this work, a dynamic system is controlled by multiple sensor-actuator
agents, each of them commanding and observing parts of the system's input and
output. The different agents sporadically exchange data with each other via a
common bus network according to local event-triggering protocols. From these
data, each agent estimates the complete dynamic state of the system and uses
its estimate for feedback control. We propose a synthesis procedure for
designing the agents' state estimators and the event triggering thresholds. The
resulting distributed and event-based control system is guaranteed to be stable
and to satisfy a predefined estimation performance criterion. The approach is
applied to the control of a vehicle platoon, where the method's trade-off
between performance and communication, and the scalability in the number of
agents is demonstrated.Comment: This is an extended version of an article to appear in the IEEE
Transactions on Automatic Control (additional parts in the Appendix
Stabilizing Scheduling Policies for Networked Control Systems
This paper deals with the problem of allocating communication resources for
Networked Control Systems (NCSs). We consider an NCS consisting of a set of
discrete-time LTI plants whose stabilizing feedback loops are closed through a
shared communication channel. Due to a limited communication capacity of the
channel, not all plants can exchange information with their controllers at any
instant of time. We propose a method to find periodic scheduling policies under
which global asymptotic stability of each plant in the NCS is preserved. The
individual plants are represented as switched systems, and the NCS is expressed
as a weighted directed graph. We construct stabilizing scheduling policies by
employing cycles on the underlying weighted directed graph of the NCS that
satisfy appropriate contractivity conditions. We also discuss algorithmic
design of these cycles
PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles
There exists an increasing demand for a flexible and computationally
efficient controller for micro aerial vehicles (MAVs) due to a high degree of
environmental perturbations. In this work, an evolving neuro-fuzzy controller,
namely Parsimonious Controller (PAC) is proposed. It features fewer network
parameters than conventional approaches due to the absence of rule premise
parameters. PAC is built upon a recently developed evolving neuro-fuzzy system
known as parsimonious learning machine (PALM) and adopts new rule growing and
pruning modules derived from the approximation of bias and variance. These rule
adaptation methods have no reliance on user-defined thresholds, thereby
increasing the PAC's autonomy for real-time deployment. PAC adapts the
consequent parameters with the sliding mode control (SMC) theory in the
single-pass fashion. The boundedness and convergence of the closed-loop control
system's tracking error and the controller's consequent parameters are
confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's
efficacy is evaluated by observing various trajectory tracking performance from
a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing
micro aerial vehicle called hexacopter. Furthermore, it is compared to three
distinctive controllers. Our PAC outperforms the linear PID controller and
feed-forward neural network (FFNN) based nonlinear adaptive controller.
Compared to its predecessor, G-controller, the tracking accuracy is comparable,
but the PAC incurs significantly fewer parameters to attain similar or better
performance than the G-controller.Comment: This paper has been accepted for publication in Information Science
Journal 201
Evolvability signatures of generative encodings: beyond standard performance benchmarks
Evolutionary robotics is a promising approach to autonomously synthesize
machines with abilities that resemble those of animals, but the field suffers
from a lack of strong foundations. In particular, evolutionary systems are
currently assessed solely by the fitness score their evolved artifacts can
achieve for a specific task, whereas such fitness-based comparisons provide
limited insights about how the same system would evaluate on different tasks,
and its adaptive capabilities to respond to changes in fitness (e.g., from
damages to the machine, or in new situations). To counter these limitations, we
introduce the concept of "evolvability signatures", which picture the
post-mutation statistical distribution of both behavior diversity (how
different are the robot behaviors after a mutation?) and fitness values (how
different is the fitness after a mutation?). We tested the relevance of this
concept by evolving controllers for hexapod robot locomotion using five
different genotype-to-phenotype mappings (direct encoding, generative encoding
of open-loop and closed-loop central pattern generators, generative encoding of
neural networks, and single-unit pattern generators (SUPG)). We observed a
predictive relationship between the evolvability signature of each encoding and
the number of generations required by hexapods to adapt from incurred damages.
Our study also reveals that, across the five investigated encodings, the SUPG
scheme achieved the best evolvability signature, and was always foremost in
recovering an effective gait following robot damages. Overall, our evolvability
signatures neatly complement existing task-performance benchmarks, and pave the
way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary
figures. Accepted at Information Sciences journal (in press). Supplemental
videos are available online at, see http://goo.gl/uyY1R
Optimal co-design of control, scheduling and routing in multi-hop control networks
A Multi-hop Control Network consists of a plant where the communication
between sensors, actuators and computational units is supported by a (wireless)
multi-hop communication network, and data flow is performed using scheduling
and routing of sensing and actuation data. Given a SISO LTI plant, we will
address the problem of co-designing a digital controller and the network
parameters (scheduling and routing) in order to guarantee stability and
maximize a performance metric on the transient response to a step input, with
constraints on the control effort, on the output overshoot and on the bandwidth
of the communication channel. We show that the above optimization problem is a
polynomial optimization problem, which is generally NP-hard. We provide
sufficient conditions on the network topology, scheduling and routing such that
it is computationally feasible, namely such that it reduces to a convex
optimization problem.Comment: 51st IEEE Conference on Decision and Control, 2012. Accepted for
publication as regular pape
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