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Connected and automated vehicles mandate integrated design of communications and control to achieve coordination of highway vehicles. Random features of wireless communications introduce new types of uncertainties into networked systems and impact control performance significantly. Due to typical packet loss, erasure channels create random link interruption and switching in network topologies. This paper models such switching network topologies by Markov chains and derives their probability transition matrices from stochastic characterizations of the channels. Impact of communication erasure channels on vehicle platoon formation and robustness under a weighted and constrained consensus framework is analyzed. By comparing convergence properties of networked control algorithms under different communication channel features, we characterize some intrinsic relationships between packet delivery ratio and convergence rate. Simulation case studies are performed to verify the theoretical findings. [ABSTRACT FROM PUBLISHER]

Establishing real-time models for electric motors is important when capturing authentic dynamic behavior of the motors to improve control performance, enhance robustness, and support diagnosis. Quantized sensors are less expensive, and remotely controlled motors mandate signal quantization. Such limitations on observations introduce challenging issues in motor parameter estimation. This paper develops estimators for model parameters of permanent-magnet direct current (PMDC) motors using quantized speed measurements. A typical linearized model structure of PMDC motors is used as a benchmark platform to demonstrate the technology and its key properties and benefits. Convergence properties are established. Simulations and experimental studies are performed to illustrate potential applications of the technology. [ABSTRACT FROM AUTHOR]