Sending the most recent observation is not optimal in networked control: Linear temporal coding and towards the design of a control specific transport protocol

Robinson, Craig; Kumar, P. R.

doi:10.1109/cdc.2007.4434804

Cited by 33 publications

(26 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These results corroborate our findings with S-PPC. 16 October had significant chop with sustained winds of fifteen knots, gusting to thirty. As with S-PPC, we see a bias due to the disturbances, and a reduction in control fidelity with increased packet loss.…”

Section: B Mif-lqgmentioning

confidence: 99%

See 1 more Smart Citation

Experiments in dynamic control of autonomous marine vehicles using acoustic modems

Gilbertson

Reed

Leighton

et al. 2013

2013 IEEE International Conference on Robotics and Automation

View full text Add to dashboard Cite

Abstract-Marine robots are an increasingly attractive means for observing and monitoring in the ocean, but underwater acoustic communication ("acomms") remains a major challenge, especially for real-time control. Packet loss occurs widely, bit rates are low, and there are significant delays. We consider here strategies for feedback control with acomms links in either the sensor-controller channel, or the controlleractuator channel. On the controller-actuator side we implement sparse packetized predictive control (S-PPC), which simultaneously addresses packet-loss and the data rate limit. For the sensor-controller channel we study a modified information filter (MIF) in a Linear Quadratic Gaussian (LQG) control scheme. Field experiments were carried out with both approaches, regulating crosstrack error in a robotic kayak using acomms. Outcomes with both the S-PPC and MIF LQG confirm that good performance is achievable.

show abstract

Section: B Mif-lqgmentioning

confidence: 99%

“…An LQG approach using a modified information filter (MIF) was developed by Gupta et al [15]; the method does not require acknowledgements. Linear coding for estimation under packet loss is considered in [16], but a major drawback is that acknowledgements are required.…”

Section: Introductionmentioning

confidence: 99%

Experiments in dynamic control of autonomous marine vehicles using acoustic modems

Gilbertson

Reed

Leighton

et al. 2013

2013 IEEE International Conference on Robotics and Automation

View full text Add to dashboard Cite

show abstract

“…between the sensor and controller in an LQG control loop [108]. Alternative encoding schemes for dealing with packet loss include multiple-description coding [126,175] and temporal packet coding [205]. Control techniques for the scenario with lossy channels between the sensor and controller and the controller and actuator have been presented by Schenato et al [214] using linear matrix inequalities (LMIs), and by…”

Section: Constructive Techniquesmentioning

confidence: 99%

Controller design for underwater vehicle systems with communication constraints

Reed¹

2015

View full text Add to dashboard Cite

Real-time cooperation between autonomous vehicles can enable time-critical missions such as tracking and pursuit of a dynamic target or environmental feature, but relies on wireless communications. Underwater, communication over distances beyond about one hundred meters is almost exclusively accomplished through acoustics, which bring challenges such as propagation delays, low data rates, packet loss, and scheduling constraints due to interference and limited bandwidth. These limitations make underwater pursuit missions preeminent applications of networked control. Motivated by such applications, this thesis presents contributions towards multi-vehicle feedback control in the presence of severe communication constraints.The first major area of work considers the formulation and solution of new underwater multi-vehicle tracking and pursuit problems using closed-loop control. We begin with a centralized robust optimization approach for multicast routing and power control which is suitable for integration with vehicle control. Next, we describe field experiments in range-based target pursuit at high tracking bandwidths in a challenging shallow-water environment. Finally, we present a methodology for pursuit of dynamic ocean features such as fronts, which we validate using hindcast ocean model data. The primary innovation is a projection algorithm which carries out linearization of ocean model forecast dynamics and uncertainty directly in vehicle coordinates via a forward model technique. The resulting coupled linear stochastic system is suitable for networked control.The second area of work presents a unified formalism for multi-vehicle control and estimation with measurement, control, and acknowledgment packets all subject to scheduling, delays and packet loss. The modular framework we develop is built around a jump linear system description incorporating receding horizon optimization and buffering at actuators. Integration of these elements enables synthesis of a novel technique for estimation using delayed and lossy control acknowledgments-a desirable and practical capability of fielded systems that has not been considered to date.Simulations and field experiments demonstrate the effectiveness of our approach.

show abstract

“…In a network scenario with nine nodes, it has been shown that the architecture is able to provide an upper bound on packet delay comprised between 10 ms and 20 ms, depending on the network conditions. In (Robinson & Kumar, 2007), the problem of selecting what information should be sent between a sensor and a controller in a networked control system where the two components are separated by an unreliable, bandwidth limited communication link, such as a wireless one, has been analyzed. It has been shown that the common practice of sending the most recent observation is not optimal.…”

Section: State Of the Art On Wireless Real-time Systemsmentioning

confidence: 99%