Data Transmission Over Networks for Estimation and Control (original) (raw)
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Estimation over communication networks: Performance bounds and achievability results
Proceedings of the American Control Conference, 2007
This paper considers the problem of estimation over communication networks. Suppose a sensor is taking measurements of a dynamic process. However the process needs to be estimated at a remote location connected to the sensor through a network of communication links that drop packets stochastically. We provide a framework for computing the optimal performance in the sense of expected error covariance. Using this framework we characterize the dependency of the performance on the topology of the network and the packet dropping process. For independent and memoryless packet dropping processes we find the steady-state error for some classes of networks and obtain lower and upper bounds for the performance of a general network. Finally we find a necessary and sufficient condition for the stability of the estimate error covariance for general networks with spatially correlated and Markov type dropping process. This interesting condition has a max-cut interpretation.
Redundant data transmission in control/estimation over lossy networks
Automatica, 2012
In wireless networks the probability of successful communication can be significantly increased by transmitting multiple copies of a same packet. Communication protocols that exploit this by dynamically assigning the number of transmitted copies of the same data can significantly improve the control performance in a networked control system with only a modest increase in the total number of transmissions. In this paper we develop techniques to design communication protocols that exploit multiple packets transmissions while seeking a balance between stability/estimation performance and communication rate. An average cost optimality criterion is employed to obtain optimal protocols. Optimal protocols are also obtained for networks whose nodes are subject to limited computation.
Control and estimation over unreliable communication networks
2012
The topic of this thesis is control and estimation over unreliable communication networks such as wireless network. It is assumed that the plant and control unit are connected though unreliable channels. We considered the problems of estimation and control under two different protocols. In the TCP-like protocol, where the control unit provides acknowledgments successfully delivered of the packets, while the acknowledgments are absent in case of UDP-like protocol. This thesis investigates techniques for designing linear quadratic Gaussian LQG controller and estimation schemes subject to packet dropout using state and output feedback. Firstly, LQG optimal controller is designed using optimal theory based on Linear quadratic regulator and a discrete Kalman filter with packet dropout according to Bernoulli process. Necessary and sufficient conditions to guaranty stability are stated. Then estimation schemes are elaborated for a class of networked control system with nonstationary data lost. Two observer based stabilizing controller of networked control systems (NCSs) are designed in case of zero input and hold input strategies. Sufficient conditions for stability are dex rived in terms of using linear matrix inequality (LMIs). Theoretical analysis and simulation results are presented using MATLAB software for several numerical examples.
Estimation under uncontrolled and controlled communications in Networked Control Systems
2005
An LTI estimation framework is proposed for networked control systems (NCS), in which local Kalman filter estimates are sent to the remote estimator. Both controlled and uncontrolled data communications are considered. For uncontrolled communication, minimum rate requirements are given for stochastic moment stability, which depend only on the least stable poles. For controlled communication, sufficient stability conditions are formulated. The framework also makes it possible to improve the trade-off between estimation performance and communication cost. is an observable pair. The Gaussian white disturbance w ∈ R n and noise v ∈ R l are mutually independent and zero-mean with covariance matrices Σ w > 0 ∈ R n×n and Σ v > 0 ∈ R l×l .
Redundant data transmission in control/estimation over wireless networks
2009
In wireless networks the probability of successful communication can be significantly increased by transmitting multiple copies of a same packet. Communication protocols that exploit this by dynamically assigning the number of transmitted copies of the same data can significantly improve the control performance in a networked control system with only a modest increase in the total number of transmissions. In this paper we develop techniques to design communication protocols that exploit multiple packets transmissions while seeking a balance between stability/estimation performance and communication rate. An average cost optimality criterion is employed to obtain optimal protocols. Optimal protocols are also obtained for networks whose nodes are subject to limited computation.
State estimation with remote sensors and intermittent transmissions
Systems & Control Letters, 2012
This paper deals with the problem of estimating the state of a discrete-time linear stochastic dynamical system on the basis of data collected from multiple sensors subject to a limitation on the communication rate from the remote sensor units. The optimal probabilistic measurement-independent strategy for deciding when to transmit estimates from each sensor is derived. Simulation results show that the derived strategy yields certain advantages in terms of worst-case time-averaged performance with respect to periodic strategies when coordination among sensors is not possible. (L. Chisci). This happens, for example, in wireless sensor networks wherein every transmission typically reduces the lifetime of the sensor devices, wireless communication being the major source of energy consumption [1]. Further, a reduction in the sensors' data transmission rate can be crucial in networked control systems in order to reduce the network traffic and hopefully avoid congestion .
Constrained State Estimation and Control over Communication Networks
2003
This paper performs a joint analysis of control and coding for stability and performance of LTI systems connected over communication networks. We study the communication rate required for stability of the differential entropy and mean-square stability of the state estimation error. We show that the optimal control and coding problems in the minimization of an LQR cost are separable; we further show that the optimal control is linear in its argument and we provide the solution to the optimal quantization problem. Mean-square stability of an LTI system, as a function of the rate and network reliability, is also studied.
IEEE Transactions on Control of Network Systems, 2014
This paper presents a novel design methodology for optimal transmission policies at a smart sensor to remotely estimate the state of a stable linear stochastic dynamical system. The sensor makes measurements of the process and forms estimates of the state using a local Kalman filter. The sensor transmits quantized information over a packet dropping link to the remote receiver. The receiver sends packet receipt acknowledgments back to the sensor via an erroneous feedback communication channel which is itself packet dropping. The key novelty of this formulation is that the smart sensor decides, at each discrete time instant, whether to transmit a quantized version of either its local state estimate or its local innovation. The objective is to design optimal transmission policies in order to minimize a long term average cost function as a convex combination of the receiver's expected estimation error covariance and the energy needed to transmit the packets. The optimal transmission policy is obtained by the use of dynamic programming techniques. Using the concept of submodularity, the optimality of a threshold policy in the case of scalar systems with perfect packet receipt acknowledgments is proved. Suboptimal solutions and their structural results are also discussed. Numerical results are presented illustrating the performance of the optimal and suboptimal transmission policies.
State estimation in a centralized sensor network under limited communication rate
This paper deals with the problem of estimating the state of a discrete-time linear stochastic dynamical system on the basis of data collected from multiple sensors subject to a limitation on the communication rate from the remote sensor units. More specifically, the attention is devoted to a centralized sensor network consisting of: (1) S remote nodes which collect measurements of the given system, compute state estimates at the full measurement rate and transmit them at a reduced communication rate; (2) a fusion node F that, based on received estimates, provides an estimate of the system state at the full rate. A measurement-independent strategy for deciding when transmitting estimates from each sensor to F will be considered. Sufficient conditions for the boundedness of the state covariance at node F will be given. Further, the possibility of determining a communication strategy with optimal performance in terms of minimum mean square estimation error will be investigated. △ = {s k1 , s k1+1 , . . . , s k2 } .
Optimal tracking with feedback-feedforward control separation over a network
2006
This paper studies tracking of a reference path in a networked control system where the controller consists of a central decision maker and an on-site controller, which are connected through a discrete noiseless channel. The reference path is available noncausally to the central decision maker and the on-site controller has access to noisy observations from the plant and the reference information provided by the central decision maker. For a quadratic optimization objective, we provide the optimal control using dynamic programming and show that the optimal controller can be separated into a noncausal feedforward term (generated by the central decision maker) plus a feedback term (generated by the on-site controller) which has causal access to the controls applied without any loss of performance. We show that the feedforward control is the solution of a deterministic quadratic program, i.e., certainty equivalence holds. We later study the problem of transmission of the feedforward controls to the on-site controller over a discrete noiseless channel. We formulate and solve an optimization problem for the optimal time-varying and time invariant uniform quantization of the feedforward control signals sent by the central decision maker to the on-site controller over a communication network.