Study On Distributed Model Predictive Control For Multi-agent Systems

Multi-agent systems is the forum of control theory and artificial intelligence in which the central interest is to construct a kind of effective cooperation mechanism which enables function independent agent to achieve a complex controlling task, or solve a complex problem. A multi-agent system is composed of many spatial distributed subsystems with decoupled dynamics, such as multi-satellite systems, multi-robot systems, multi-aircraft systems, etc. Under the network environment, the system-level control objective is achieved by cooperation among agents. Cooperation refers to the agreement among the agents: Each agent has a common objective with neighbor agents, and share information online to realize the objective. Agents are function independent (autonomous) in that they are individually capable of sensing their environment and possibly other agents, communicating with other agents, and computing and implementing control actions to meet their portion of the objective.Compared to the traditional approach, model predictive control has the ability to redefine cost functions and constraints as needed to reflect changes in the system and/or the environment. Hence, MPC is extensively applied to the cooperative control of multi-agent systems, which makes the agents operate close to the constraint boundaries, and obtain better performance than traditional approaches. However, the research results of centralized model predictive control could not be simply extended to distributed model predictive control. For distributed control problem, the coupled global cost function, coupled constraints, limited communication constraints (communication failure) etc, need to be considered, which could lead to conflictions among the independent control actions, affect the consistency of control actions and global stability of system. Hence, how to avoid conflictions among agents, or improve the consistency and efficiency, becomes an important issue in the area of multi-agent systems, which is very important for engineering applications.So this thesis focuses on distributed predictive control (DMPC) for multi-agent systems under the coupled global cost function, coupled constraint, limited communication constraint (complete communication failure). The main work is as follows.For the distributed control problem of multi-agent systems with the coupled global cost function, this paper addresses an improved DMPC scheme using deviation penalty and compatibility constraint. This scheme analyzes the decomposition approach for the global and centralized cost function, gets the local and distributed cost function of each agent, establishes the distributed predictive control problem with finite horizon, and designs different and time-varying compatibility constraint for each agent. The deviation between what an agent is actually doing and what its neighbors believe that agent is doing is penalized in the local cost function of each agent. At each sampling instant the compatibility constraint of each agent is set tighter than the previous sampling instant. Like the traditional approach, the performance cost is utilized as the Lyapunov function to prove closed-looped stability.For the distributed control problem of multi-agent systems with coupled constraints, this paper addresses an improved DMPC scheme. The coupled constraints are transformed as non-coupled constraints. The results are extended to the typical coupling constraints such as avoidance constraint and limited distance constraint. The sufficient condition for coupled constraints is analyzed. Furthermore, the feasibility and stability of this method are investigated.For complex cooperative control problem of multi-agent systems, distributed model predictive control scheme based on the dynamic cooperative rules is addressed. The collision avoidance constraints are transformed as hybrid rules based on the positions, and the Boolean function term is incorporated in the cost function. In order to accommodate the complex time-varying environment, at each sample instant, the dynamic cooperative rules are designed according to the relative positions between the agents and between each agent and its destination, so as to determine the weights in the Boolean function.For the distributed control problem of multi-agent systems with complete communication failure, this paper proposes a dual-mode DMPC scheme. This scheme combines the DMPC with compatibility constraint and the DMPC with neighbor agent state optimization. For the normal communication, by using the DMPC with compatibility constraint, each agent gets the assumed state trajectory of its neighbors through communication. For the complete communication failure, by using DMPC with neighbor agent state optimization, each agent only detects the local information (position and velocity), including the prediction of neignbos agent's state into its optimal problem. Each agent constructs the DMPC schme based on the optimization of neighbor agent state, realizes the dual-mode DMPC of multi-agent systems with complete communication failure .