Process-Based Triggering and Accelerated Dual Averaging Algorithm for Dynamic Parameter Estimation

Abstract

In the large-scale cyber-physical systems, to conserve communication resources holds critical significance, thereby driving extensive research interest toward distributed optimization algorithms with high communication efficiency. This paper investigates the constrained distributed dynamic parameter estimation problem (CDPE) for communication resource conservation, and further considers how to cope with more generally directed communication structure, unavoidable arbitrary bounded communication delays, and diverse update strategies. We introduce a new process-based triggering strategy and develop an efficient Process-based Triggering Accelerated Dual Averaging Algorithm(PTADA). Compared with the traditional time-dependent threshold, the PTADA can well adapt to the dynamic behavior of distributed optimization and save communication resources. Our dynamic bound is linear and is independent of the explicit time horizon. Moreover, we further extend PTADA to address scenarios where gradient information cannot be directly obtained, while ensuring no performance degradation. This extension can make the algorithm more realistic and universal. Finally, a distributed multi-sensor network is set up to verify the effectiveness of the algorithm.