Doctor of Philosophy (PhD)


The Division of Electrical & Computer Engineering

Document Type



A multi-agent system (MAS) is a system in which multiple autonomous agents interact with each other to achieve a common goal. Nevertheless, current designs of MAS controllers typically rely on particular requirements, such as time-triggered communication, linear dynamics, and access to global information. These limitations restrict the applicability of MASs. This thesis aims to eliminate these constraints and optimize the energy consumption of a specific MAS, namely connected autonomous electric vehicles (CAEVs).

This thesis first addresses the consensus problem of linear MASs with intermittent communication. An adaptive distributed control algorithm that integrates edge-based event-triggered (ET) communication mechanisms is presented in this thesis. Unlike existing approaches, this algorithm does not employ any form of global topology information or absolute state information. Instead, agents update their control signals and check triggering conditions based on relative state information. The ET control method presented in this thesis guarantees that all agents attain global asymptotic synchronization with strictly positive minimum inter-event times in a connected and undirected communication graph.

The thesis then proceeds to address the output consensus problem for high-order MASs with matched disturbances and provides a solution. The disturbance is modeled as an extended state, and an extended state observer is designed for each agent to estimate the disturbance and state information jointly. By utilizing delayed estimated state information from neighbors, a distributed algorithm is developed to guarantee that the MAS achieves output consensus. The goal is to

Finally, the thesis explores the application of MASs in electric vehicle (EV) platooning. Previous studies have mainly formulated vehicle platooning as an energy optimization problem. However, with the advent of EVs, practical issues such as battery health and charging time have not been considered. The imbalance in energy consumption resulting from fixed positions of vehicles in a platoon can be mitigated by changing fleet formations during a trip. This raises the question of determining the optimal sequences of vehicles at fixed re-sequencing locations, which is NP-hard. To address this problem, this thesis proposes five approaches for a deterministic environment and one reinforcement learning approach for a stochastic environment. The time and space complexities of different solutions are also investigated.

Overall, this thesis aims to enhance the performance and applicability of MASs, particularly in the context of CAEVs.



Committee Chair

Meng, Xiangyu

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