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This paper presents a distributed receding horizon approach for multi-agent systems. First, the consensus problem with input constraints is considered. Second, the trajectory planning of multi-agent systems (MASs) evolving in environment with obstacles is studied, in which the information of obstacles can be obtained online once the obstacles are sensed by the agents within a limited sensing range. Based on receding horizon approach, a distributed control algorithm is developed to solve the two problems simultaneously. The algorithm can be given by solving the optimal control problem, in which the cost function is coupled with the dynamics of each interacting agent. The exchanged information between agents is the previous optimal control states of their neighbors at each update step. The system stability can be guaranteed with a terminal-state penalty in the cost function and a terminal-state region in the optimal control problem. Simulation studies are provided to verify the effectiveness of the proposed approach.
This paper presents a distributed receding horizon approach for multi-agent systems. First, the consensus problem with input constraints is considered. Second, the trajectory planning of multi-agent systems (MASs) evolving in environment with obstacles is studied, in which the information of obstacles can be obtained online once the obstacles are sensed by the agents within a limited sensing range. Based on receding horizon approach, a distributed control algorithm is developed to solve the two problems simultaneously. The algorithm can be given by solving the optimal control problem , in which the cost function is coupled with the dynamics of each interacting agent. The information information between agents is the previous optimal control states of their neighbors at each update step. The system stability can be guaranteed with a terminal-state penalty in the cost function and a terminal-state region in the optimal control problem. Simulation studies are provided to verify the effective ness of the proposed approach.