Luca Schenato

Foundations of Control and Estimation Over Lossy Networks

This paper considers control and estimation problems where the sensor signals and the actuator signals are transmitted to various subsystems over a network. In contrast to traditional control and estimation problems, here the observation and control packets may be lost or delayed. The unreliability of the underlying communication network is modeled stochastically by assigning probabilities to the successful transmission of packets. This requires a novel theory which generalizes classical control/estimation paradigms. The paper offers the foundations of such a novel theory. The central contribution is to characterize the impact of the network reliability on the performance of the feedback loop. Specifically, it is shown that for network protocols where successful transmissions of packets is acknowledged at the receiver (e.g., TCP-like protocols), there exists a critical threshold of network reliability (i.e., critical probabilities for the successful delivery of packets), below which the optimal controller fails to stabilize the system. Further, for these protocols, the separation principle holds and the optimal LQG controller is a linear function of the estimated state. In stark contrast, it is shown that when there is no acknowledgement of successful delivery of control packets (e.g., UDP-like protocols), the LQG optimal controller is in general nonlinear. Consequently, the separation principle does not hold in this circumstance

Distributed control applications within sensor networks

Sensor networks are gaining a central role in the research community. This paper addresses some of the issues arising from the use of sensor networks in control applications. Classical control theory proves to be insufficient in modeling distributed control problems where issues of communication delay, jitter, and time synchronization between components are not negligible. After discussing our hardware and software platform and our target application, we review useful models of computation and then suggest a mixed model for design, analysis, and synthesis of control algorithms within sensor networks. We present a hierarchical model composed of continuous time-trigger components at the low level and discrete event-triggered components at the high level.

Distributed Kalman filtering based on consensus strategies

In this paper, we consider the problem of estimating the state of a dynamical system from distributed noisy measurements. Each agent constructs a local estimate based on its own measurements and on the estimates from its neighbors. Estimation is performed via a two stage strategy, the first being a Kalman-like measurement update which does not require communication, and the second being an estimate fusion using a consensus matrix. In particular we study the interaction between the consensus matrix, the number of messages exchanged per sampling time, and the Kalman gain for scalar systems. We prove that optimizing the consensus matrix for fastest convergence and using the centralized optimal gain is not necessarily the optimal strategy if the number of exchanged messages per sampling time is small. Moreover, we show that although the joint optimization of the consensus matrix and the Kalman gain is in general a non-convex problem, it is possible to compute them under some relevant scenarios. We also provide some numerical examples to clarify some of the analytical results and compare them with alternative estimation strategies.

Flapping flight for biomimetic robotic insects: part I-system modeling

This paper presents the mathematical modeling of flapping flight inch-size micro aerial vehicles (MAVs), namely micromechanical flying insects (MFIs). The target robotic insects are electromechanical devices propelled by a pair of independent flapping wings to achieve sustained autonomous flight, thereby mimicking real insects. In this paper, we describe the system dynamic models which include several elements that are substantially different from those present in fixed or rotary wing MAVs. These models include the wing-thorax dynamics, the flapping flight aerodynamics at a low Reynolds number regime, the body dynamics, and the biomimetic sensory system consisting of ocelli, halteres, magnetic compass, and optical flow sensors. The mathematical models are developed based on biological principles, analytical models, and experimental data. They are presented in the Virtual Insect Flight Simulator (VIFS) and are integrated together to give a realistic simulation for MFI and insect flight. VIFS is a software tool intended for modeling flapping flight mechanisms and for testing and evaluating the performance of different flight control algorithms

Average TimeSynch

This paper describes a new consensus-based protocol, referred to as Average TimeSync (ATS), for synchronizing the clocks of a wireless sensor network. This algorithm is based on a cascade of two consensus algorithms, whose main task is to average local information. The proposed algorithm has the advantage of being totally distributed, asynchronous, robust to packet drop and sensor node failure, and it is adaptive to time-varying clock drifts and changes of the communication topology. In particular, a rigorous proof of convergence to global synchronization is provided in the absence of process and measurement noise and of communication delay. Moreover, its effectiveness is shown through a number of experiments performed on a real wireless sensor network.

A distributed consensus protocol for clock synchronization in wireless sensor network

This paper describes a novel consensus-based protocol, referred as Average TimeSync (ATS), for synchronizing a wireless sensor. This algorithm is based on a class of popular distributed algorithms known as consensus, agreement, gossip or rendezvous whose main idea is averaging local information. The proposed algorithm has three main features. Firstly, it is fully distributed and therefore robust to node failure and to new node appearance. Secondly, it compensates for clock skew differences among nodes, thus maintaining the network synchronized for longer periods than using simple clock offset compensation. Finally, it is computationally lite as it involves only simple sum/product operations. The algorithm has been implemented and preliminary experimental results are provided.

To Zero or to Hold Control Inputs With Lossy Links

This technical note studies the linear quadratic (LQ) performance of networked control systems where control packets are subject to loss. In particular we explore the two simplest compensation strategies commonly found in the literature: the zero-input strategy, in which the input to the plant is set to zero if a packet is dropped, and the hold-input strategy, in which the previous control input is used if packet is lost. We derive expressions for computing the optimal static gain for both strategies and we compare their performance on some numerical examples. Interestingly, none of the two can be claimed superior to the other, even for simple scalar systems, since there are scenarios where one strategy performs better then the other and scenarios where the converse occurs.

Tracking and Coordination of Multiple Agents Using Sensor Networks: System Design, Algorithms and Experiments

This paper considers the problem of pursuit evasion games (PEGs), where the objective of a group of pursuers is to chase and capture a group of evaders in minimum time with the aid of a sensor network. The main challenge in developing a real-time control system using sensor networks is the inconsistency in sensor measurements due to packet loss, communication delay, and false detections. We address this challenge by developing a real-time hierarchical control system, named LochNess, which decouples the estimation of evader states from the control of pursuers via multiple layers of data fusion. The multiple layers of data fusion convert noisy, inconsistent, and bursty sensor measurements into a consistent set of fused measurements. Three novel algorithms are developed for LochNess: multisensor fusion, hierarchical multitarget tracking, and multiagent coordination algorithms. The multisensor fusion algorithm converts correlated sensor measurements into position estimates, the hierarchical multitarget tracking algorithm based on Markov chain Monte Carlo data association (MCMCDA) tracks an unknown number of targets, and the multiagent coordination algorithm coordinates pursuers to chase and capture evaders using robust minimum-time control. The control system LochNess is evaluated in simulation and successfully demonstrated using a large-scale outdoor sensor network deployment

A Survey on Distributed Estimation and Control Applications Using Linear Consensus Algorithms

In this chapter we present a popular class of distributed algorithms, known as linear consensus algorithms, which have the ability to compute the global average of local quantities. These algorithms are particularly suitable in the context of multi-agent systems and networked control systems, i.e. control systems that are physically distributed and cooperate by exchanging information through a communication network. We present the main results available in the literature about the analysis and design of linear consensus algorithms,for both synchronous and asynchronous implementations. We then show that many control, optimization and estimation problems such as least squares, sensor calibration, vehicle coordination and Kalman filtering can be cast as the computation of some sort of averages, therefore being suitable for consensus algorithms. We finally conclude by presenting very recent studies about the performance of many of these control and estimation problems, which give rise to novel metrics for the consensus algorithms. These indexes of performance are rather different from more traditional metrics like the rate of convergence and have fundamental consequences on the design of consensus algorithms.

Optimal estimation in networked control systems subject to random delay and packet loss

In this paper we study optimal estimation design for sampled linear systems where the sensors measurements are transmitted to the estimator site via a generic digital communication network. Sensor measurements are subject to random delay or might even be completely lost. We present two time-invariant estimator architectures and, surprisingly, we show that stability does not depend on packet delay but only on the packet loss probability. Finally, algorithms to compute critical packet loss probability and estimators performance in terms of their error covariance are given and applied to some numerical examples