Convex programming based robust localization in NLOS prone cluttered environments.

In a large variety of industrial scale processes, fixed or mobile sensors are typically deployed in large-scale vessels to monitor parameters such as temperature, pressure and chemical concentration. When these vessels are cluttered with obstacles, e.g. large cooling ponds cluttered with nuclear waste containers, it becomes increasingly difficult for the sensors to estimate their position. The acoustic ranging signals used for estimating distances between each sensor node and reference nodes fixed to the vessel infrastructure can suffer from Non-Line-Of-Sight (NLOS) signal propagation and thus introduce large positive errors in some of the estimated distances. In this paper we present a robust localization algorithm for localizing sensors in cluttered NLOS environments. We show that if the number of erroneous range measurements is less than half, it is possible to accurately estimate these NLOS errors at each sensor node by solving a convex optimization problem. Each sensor node can then use its estimate of NLOS errors to accurately localize itself. Our approach is completely independent of the physical hardware used to perform range measurements and thus can be used to localize sensor nodes in any NLOS prone environment. We demonstrate this with the help of experimental results with three different hardware platforms each employing a different ranging mechanism. © 2011 ACM

Robust Regulation of Polytopic Uncertain Linear Hybrid Systems with Networked Control System Applications

Abstract: In this chapter, a class of discrete time uncertain linear hybrid systems, affected by both parameter variations and exterior disturbances, is considered. The main question is whether there exists a controller such that the closed loop system exhibits desired behavior under dynamic uncertainty and exterior disturbances. The notion of attainability is introduced to refer to the specified behavior that can be forced to the plant by a control mechanism. We give a method for attainability checking that employs the predecessor operator and backward reachability analysis, and a procedure for controller design that uses finite automata and linear programming techniques. Finally, Networked Control Systems (NCS) are proposed as a promising application area of the results and tools developed here, and the ultimate boundedness control problem for the NCS with uncertain delay, package dropout and quantization effects is formulated as a regulation problem for an uncertain hybrid system

Time optimal control systhesis for discrete time hybrid automata

In this paper, the problem of time-optimal control for hybrid systems with discrete-time dynamics is considered. The hybrid controller steers all trajectories starting from a maximal set to a given target set in minimum time. We derive an algorithm that computes this maximal winning set. Also, algorithms for the computation of level sets associated with the value function rather than the value function itself are presented. We show that by solving the reachability problem for the discrete time hybrid automata we obtain the time optimal solution as well. The control synthesis is subject to hard constraints on both control inputs and states. For linear discrete-time dynamics, linear programming and quantifier elimination techniques are employed for the backward reachability analysis. Emphasis is given on the computation of operators for non-convex sets using an extended convex hull approach. A two-tank example is considered in order to demonstrate the techniques of the paper