Abstract
In this paper, a novel distributed predictive control (DMPC) method for multi-agent systems based on error upper bounds is proposed. To reduce the communication burden, the error upper bound condition between the subsystem and the neighbor subsystems is calculated by introducing the min-max function from the local state error of neighbouring subsystems. Additionally, an improved coupling constraints to describe the relationship between the neighbouring subsystems is introduced. Then, the proposed DMPC algorithm with kinds of the constraints is given, including the terminal cost, the terminal set and the terminal controller. Furthermore, the feasibility of the proposed DMPC algorithm is analyzed and the stability conditions of multi-agent systems are derived. Finally, a numerical example is given to verify the effectiveness of the method.
Highlights
In recent years, with more and more in-depth research on multi-agent systems, it has been widely applied in military operations [1], [2], transportation [3], mobile sensor network [4], micro-grid [5] and many other fields
DISTRIBUTED PREDICTIVE CONTROL FOR MULTI-AGENT SYSTEMS a new distributed predictive control algorithm is designed by introducing an error upper bound condition considering the influence of neighbors
Based on the above analysis of the error upper bound condition, the new DMPC scheme is proposed in this subsection
Summary
With more and more in-depth research on multi-agent systems, it has been widely applied in military operations [1], [2], transportation [3], mobile sensor network [4], micro-grid [5] and many other fields. Due to the ability to effectively deal with constraints and enable agents to estimate the future behaviors of neighbors, distributed predictive control has became a widely used method for multi-agent systems. Wei et al proposed a model predictive control method [11]–[13], which used the information between the agent systems in the future moment to solve the control problems, it can get the optimal state of the agent systems in the future, and optimize the related cost function. Xue: Distributed Predictive Control of Multi-Agent Systems Based on Error Upper Bound Approach other neighborhood used feasible solutions. A distributed model predictive control based on the error upper bound for multi-agent systems is studied. Is Pontryagin difference. bT , b−1 are respectively used to represent the transpose and inverse of the matrix b
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