Control under communication constraints

By Sekhar Chandra Tatikonda, Sanjoy K. Mitter. IEEE Transactions on Automatic Control, 2004

1,756 citations recorded by the source corpus.

There is an increasing interest in studying control systems employing multiple sensors and actuators that are geographically distributed. Communication is an important component of these distributed and networked control systems. Hence, there is a need to understand the interactions between the control components and the communication components of the distributed system. In this paper, we formulate a control problem with a communication channel connecting the sensor to the controller. Our task involves designing the channel encoder and channel decoder along with the controller to achieve different control objectives. We provide upper and lower bounds on the channel rate required to achieve these different control objectives. In many cases, these bounds are tight. In doing so, we characterize the "information complexity" of different control objectives.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/72f814b1bcd97aa4caa0f5896493c00d1fb68669

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Citations in the corpus

For the Grid and Through the Grid: The Role of Power Line Communications in the Smart Grid (2010, neutral): “d even in the simplest settings [68]. Many theories are emerging that deal with the issue of control under communication constraints that apply broadly to cyber-physical systems (see e.g. [69], [70], [71], [72], [73]), however modular and scalable solutions of network control are still elusive in ma”
The sector bound approach to quantized feedback control (2005, background): “The problem of minimum feedback information rate is studied in more details in [13] by analyzing the structures of the encoder and decoder.”
The Necessity and Sufficiency of Anytime Capacity for Stabilization of a Linear System Over a Noisy Communication Link—Part I: Scalar Systems (2006, methodology): “[4], [5] bounded control performance in the vector case using sequen tial rate distortion theory.”
Adaptive Backstepping Stabilization of Nonlinear Uncertain Systems With Quantized Input Signal (2014, background): “Recently, research on stabilization of linear and nonlinear systems with quantized control signals has received great attention, see for examples, [3], [4], [7]–[11].”
Stabilization of linear systems over networks with bounded packet loss (2007, neutral): “…Shi, Chen, & Huang, 2005), packet losses (Azimi-Sadjadi, 2003; Seiler & Sengupta, 2005; Yu, Wang, Chu, & Xie, 2004; Zhang et al., 2001), and limited information issue (Shi & Murray, 2005; Tatikonda & Mitter, 2004), scheduling (Walsh & Ye, 2001) were presented and some useful results were reported.”
Wireless Network Design for Control Systems: A Survey (2017, methodology): “troller is collocated with the actuators, a Markov jump linear system can be used to analyze the effect of the message dropout [65], [107], [109], [119].”
Distributed Average Consensus With Quantization Refinement (2013, background): “In what follows, we show that under a few simplifying assumptions the recursive relation of the previous section leads to an exponential model whose parameters can be determined in closed form.”
Feedback stabilization over signal-to-noise ratio constrained channels (2004, background): “Similar results have been obtained in [4] and [2].”
Stabilization of nonlinear systems with limited information feedback (2005, background): “Among the many references on this subject, the ones particularly close in spirit to the present work are [3], [4], [10], [16], [17], [21], [24], and [27].”
A Survey of Recent Results in Networked Control Systems (2007, background): “The simplest stochastic model assumes that dropouts are realizations of a Bernoulli process [60], [ 65 ].”
A new approach to quantized feedback control systems (2008, background): “Results on feedback control with dynamic quantizers can be found in Brockett and Liberzon (2000), Ling and Lemmon (2005), Nair and Evans (2000, 2003), Tatikonda and Mitter (2004).”
Stability and persistent disturbance attenuation properties for a class of networked control systems: switched system approach (2005, neutral): “(2004), Tatikonda and Mitter (2004).”
Minimal data rate stabilization of nonlinear systems over networks with large delays (2007, neutral): “Furthermore, Ti(s) =ET 9(SEi), withEi a positive real number.”
Towards the Control of Linear Systems with Minimum Bit-Rate (2002, background): “[5] and later for bringing to our attention the PhD thesis of Tatikonda [8].”
When bode meets shannon: control-oriented feedback communication schemes (2004, background): “This result complements [2], [4] on the minimum rate necessary to stabilize an unstable linear system.”
Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy (2010, background): “5 Previous Applications of Causal Information Causal information theory has found applicability as an information-theoretic measure in the analysis of communication channels with feedback (Massey, 1990; Kramer, 1998), decentralized control (Tatikonda, 2000), and sequential investment and online comp”
Distributed Consensus With Limited Communication Data Rate (2011, background): “It is well known that for feedback control of single-agent systems with communication constraints, Tatikonda and Mitter ([26]) and Nair and Evans ([27]) gave the minimal bit rate (channel capacity) for stabilizing a linear time-invariant system and the case with a logarithmic quantizer was considere”