This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

Thank you for downloading linear and nonlinear programming. As you may know, people have search numerous times for their favorite novels like this linear and nonlinear programming, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some infectious bugs inside their desktop computer. linear and nonlinear programming is available in our book collection an online access to it is set as public so you can download it instantly. Our digital library spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the linear and nonlinear programming is universally compatible with any devices to read.

Linear And Nonlinear Programming

Initialization-free distributed algorithms for optimal resource allocation with feasibility constraints and application to economic dispatch of power systems

Distributed gradient algorithm for constrained optimization with application to load sharing in power systems

This technical note presents a continuous-time multi-agent system for distributed optimization with an additive objective function composed of individual objective functions subject to bound, equality, and inequality constraints. Each individual objective function is assumed to be convex in the region defined by its local bound constraints only without the need to be globally convex. All agents in the system communicate using a proportional-integral protocol with their output information instead of state information to reduce communication bandwidth. It is proved that all agents with any initial state can reach output consensus at an optimal solution to the given constrained optimization problem, provided that the graph describing the communication links among agents is undirected and connected. It is further proved that the system with only integral protocol is also convergent to the unique optimal solution if each individual objective function is strictly convex. Simulation results are presented to substantiate the theoretical results.

A Multi-Agent System With a Proportional-Integral Protocol for Distributed Constrained Optimization

In this paper, we explore the relationship between dual decomposition and the consensus-based method for distributed optimisation. The relationship is developed by examining the similarities between the two approaches and their relationship to gradient-based constrained optimisation. By formulating each algorithm in continuous-time, it is seen that both approaches use a gradient method for optimisation with one using a proportional control term and the other using an integral control term to drive the system to the constraint set. Therefore, a significant contribution of this paper is to combine these methods to develop a continuous-time proportional-integral distributed optimisation method. Furthermore, we establish convergence using Lyapunov stability techniques and utilising properties from the network structure of the multi-agent system.

Continuous-time proportional-integral distributed optimisation for networked systems

This paper presents a new method for injecting human inputs into mixed-initiative interactions between humans and robots. The method is based on a model-predictive control (MPC) formulation, which inevitably involves predicting the system (robot dynamics as well as human input) into the future. These predictions are complicated by the fact that the human is interacting with the robot, causing the prediction method itself to have an effect on future human inputs. We investigate and develop different prediction schemes, including fixed and variable horizon MPCs and human input estimators of different orders. Through a search-and-rescue-inspired human operator study, we arrive at the conclusion that the simplest prediction methods outperform the more complex ones, i.e., in this particular case, less is indeed more.

http://users.ece.gatech.edu/magnus/Papers/TROrahul13.pdf

Less Is More: Mixed-Initiative Model-Predictive Control With Human Inputs

In this paper we explore the relationship between dual decomposition and the consensus-based method for distributed optimization. The relationship is developed by examining the similarities between the two approaches and their relationship to gradient-based constrained optimization. By formulating each algorithm in continuous-time, it is seen that both approaches use a gradient method for optimization with one using a proportional control term and the other using an integral control term to drive the system to the constraint set. Therefore, a significant contribution of this paper is to combine these methods to develop a continuous-time proportional-integral distributed optimization method. Furthermore, we establish convergence using Lyapunov stability techniques and utilizing properties from the network structure of the multi-agent system.

Continuous-time Proportional-Integral Distributed Optimization for Networked Systems

In this paper, we study the problem of making multiple agents spread out equidistantly on a circle. The proposed solution is a new Kuramoto-like model for multi-robot coordination, in which the standard sine-terms have been replaced by cosines. This new interaction model enables the balanced deployment of agents on a circle, while only taking into account the local information of each agent's two neighbors on a cycle graph. This means that individual agents do not need to know the (relative) states of all agents nor how many other agents are indeed present in the network. We illustrate the operation of the proposed protocol in simulation as well as extend it to a nonlinear scenario by optimizing over the coupling weights.

Balanced deployment of multiple robots using a modified kuramoto model

Whenever the control task involves the tracking of a reference signal the performance is typically improved if one knows the future behavior of this reference. However, in many applications, this is typically not the case, e.g., when the reference signal is generated by a human operator, and a remedy to this can be to try and model the reference signal over a short time horizon. In this paper, we address the problem of selecting this horizon in an adaptive fashion by minimizing a cost that takes into account the performance of the underlying control problem (that prefers longer time horizons) and the effectiveness of the reference signal model (that prefers shorter time horizons). The result is an adaptive time horizon controller that operates in a manner reminiscent of Model Predictive Control (MPC).

/pdf/adaptive-time-horizon-optimization-in-model-predictive-3zirivhemf.pdf

Greg Droge

Papers

Continuous-time proportional-integral distributed optimisation for networked systems

Less Is More: Mixed-Initiative Model-Predictive Control With Human Inputs

Continuous-time Proportional-Integral Distributed Optimization for Networked Systems

Balanced deployment of multiple robots using a modified kuramoto model

Adaptive time horizon optimization in model predictive control