Distributed Approach to Dynamic Quantization for Multi-Agent Systems

We propose a distributed approach to dynamic quantization for discrete time multi-agent systems with local memory storage at each agent. The usual assumption of a common memory storage is relaxed and the zooming-in/zooming-out strategy is based on a scaling factor which is additionally communicated. We use the notion of small l^p signal l^p stability to prove the effectiveness of our approach and provide a bound on the quantization error.