LQG cheap control over SNR-limited lossy channels with delay

Chiuso, Alessandro; Laurenti, Nicola; Schenato, Luca; Zanella, Andréa

doi:10.1109/cdc.2013.6760499

Cited by 9 publications

(7 citation statements)

References 32 publications

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“…If we were to consider a controlled unstable system, where the control action is determined by the receiver, then the very same model of the system will be different from the one used in this manuscript and it is not obvious how the results obtained here can be extended. Recently, some of the authors of this paper have looked at the case of controlled unstable systems when no pre-processing is done at the sensor [10], [11]. These two works are complementary and future work will focus on combining these ideas with the current work.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…Note that the "noise" n t is known at the transmitter side since the transmitter generates s q t starting from s t . Note that s t = t i=0 α i e t−i , and both the noise n t and e s are uncorrelated with z s , s < t [see also (11)]. Also z t is uncorrelated with z s , s < t. Therefore, the estimator x rx t|t := IE γ [x t |Z t ], satisfies…”

Section: A Optimal Strategy Derivationmentioning

confidence: 99%

“…Other researchers have tried to tackle the channel limitations by using analog models in order to avoid the difficulties associated with explicit design of digital channel encoder/decoder and to optimize some performance metrics among all possible stabilizing controllers subject to packet loss [3], [4] or subject to a maximum signal-to-noise ratio (SNR) [5], [6]. Finally, another well-explored approach is the analysis, under an LQG framework, of control systems subject to random packet loss, quantization [7]- [10] and possibly delays [11]. All these works have been concerned with stability in control systems.…”

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confidence: 99%

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Remote Estimation With Noisy Measurements Subject to Packet Loss and Quantization Noise

Dey

Chiuso

Schenato

2014

IEEE Trans. Control Netw. Syst.

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In this paper, we consider the problem of designing coding and decoding schemes to estimate the state of a scalar stable stochastic linear system subject to noisy measurements and in the presence of a wireless communication channel between the sensor and the estimator. In particular, we consider a communication channel which is prone to packet loss and includes quantization noise due to its limited capacity. We study two scenarios: the first with channel feedback and the second with no channel feedback. More specifically, in the first scenario the transmitter is aware of the quantization noise and the packet loss history of the channel, while in the second scenario the transmitter is aware of the quantization noise only. We show that in the first scenario, the optimal strategy among all possible linear encoders corresponds to the transmission of the Kalman filter innovation, similar to the differential pulse-code modulation (DPCM) technique used in digital communications. In the second scenario, we show that there is a critical packet loss probability above which it is better to transmit the state rather than the innovation. We also propose a heuristic strategy based on the transmission of a convex combination of the state and the Kalman filter innovation which is shown to provide a performance close to the one obtained with channel feedback.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: A Optimal Strategy Derivationmentioning

confidence: 99%

mentioning

confidence: 99%

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Remote Estimation With Noisy Measurements Subject to Packet Loss and Quantization Noise

Dey

Chiuso

Schenato

2014

IEEE Trans. Control Netw. Syst.

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“…Such data packets are said to be as effective sensor packets. The data packets during network transmissions are prone to time delays and packet losses [13], [14]. These uncertainties during data transfer are considered in the communication model.…”

Section: Communication Protocolmentioning

confidence: 99%

On an Integrated Approach to Networked Climate Control of a Smart Home

Dhar

Verma

Behera

et al. 2018

IEEE Systems Journal

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Climate control of thermal spaces or zones is very important for complex systems like smart home. The climate control system is connected to network for the transfer of measurement data and control action packets from sensors to controller and from controller to actuators, respectively. The system can, therefore, be categorized as a cyber-physical system (CPS). Heterogeneous nature of control and cyber domains poses a great challenge in dealing with CPS development. An integrated framework of intelligent control and communication is presented in this paper for performance improvements in the climate control system. The joint framework considers relevant system objectives based on system states and actuator actions. The constraints related to errors and delays in communication along with the limited capabilities of the devices are also taken care of. The formulated problem has been solved through the real-time optimization approach following the communication protocol using two separate controller methodologies: 1) learning-based proportional-integral (PI) controller and 2) adaptive critic-based controller. The gradient descent algorithm updates the parameters of the PI controller, whereas a properly trained adaptive critic controller generates real-time control actions for achieving desired states. The real-time performance obtained for both the controllers are significantly improved even in inconsistent data communication.Index Terms-Adaptive critic (AC), climate control, cyberphysical system (CPS), packet loss, proportional integral (PI), smart homes, time delay.

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“…The main contributions of this work are as follows: (i) we provide a feedback channel model that is characterized in terms of delay d, Signal-to-Noise-Ratio (SNR) ρ (and, in turn, code rate limit), and packet loss probability , while still being mathematically amenable to analysis; (ii) we setup an optimal LQG control problem and provide, for a scalar unstable channel, necessary and sufficient conditions for stabilizability in terms of the parameters of our channel model, and analyze the joint effect of these parameters on the system stability; (iii) as a byproduct we also obtain an expression for the system performance that can hence be used to optimize the channel parameters. Preliminary results can be found in [6,5] and [26].…”

Section: Introductionmentioning

confidence: 99%