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JLS-PPC: A Jump Linear System Framework for Networked Control
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DOI：10.1109/TCST.2016.2589822.png)
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Abstract
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We present a unified formalism for multiagent networked control and estimation with scheduling, delays, and packet loss in the communication channels between the controller and distributed sensors and actuators. The modular framework is a combined construction of a stochastic jump linear system (JLS) description of the plant and network effects, a Kalman filter-based estimator, and packetized predictive control, a receding horizon optimization technique with buffering at the actuator. Integration of these elements enables the synthesis of a novel estimation technique that generalizes prior approaches for control and measurement packet loss to the case with schedules, delays, control buffering, and most importantly, delayed and lossy control packet acknowledgments (ACKs). The JLS framework allows a clean separation of jump variable estimation and a posteriori state estimation using a backup-and-rerun strategy, and can handle variable-length ACK histories for multiple independent control communication channels. Finally, we derive modified covariance priors for the filter that account for uncertainty in the control action applied at the vehicle when ACKs are not available and control buffers are used. Simulations with single vehicle and multivehicle systems demonstrate the methods and show the benefits of utilizing delayed and lossy ACKs.
Keywords:
Multi-robot systems
networked control systems
quadratic programming
state estimation
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