This study addresses the problem of distributed formation control for a multi-agent system with collision avoidance between agents and with obstacles, in the presence of various constraints. The authors proposed solut...
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This study addresses the problem of distributed formation control for a multi-agent system with collision avoidance between agents and with obstacles, in the presence of various constraints. The authors proposed solution incorporates a controllyapunov function (CLF) into a distributedmodelpredictivecontrol scheme, which inherits the strong stability property of the CLF and optimises the formation performance. For each agent, the formation tracking objective is formulated through the CLF, while the collision avoidance objective being explicitly considered as constraints. A relaxation parameter is introduced into the CLF condition to make the trade-off between the two conflicting objectives. The terminal constraint is constructed based on the concept of velocity obstacle, which characterises the set of states that lead to collisions. They show that the terminal constraint together with the relaxed CLF-based constraint guarantees the recursive feasibility and stability of the multi-agent system for almost any prediction horizon. Furthermore, the theoretical effectiveness and advantageous implementation properties are demonstrated through simulation for multi-agent formation control with several obstacles.
To enhance the tracking accuracy, safety, robustness and efficiency of a system composed of multiple nonlinear unmanned underwater vehicles under external disturbances, a distributed formation tracking controller name...
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To enhance the tracking accuracy, safety, robustness and efficiency of a system composed of multiple nonlinear unmanned underwater vehicles under external disturbances, a distributed formation tracking controller named SREDFTC is proposed by integrating a sampled-data-based event-triggered mechanism, a collision avoidance method, and a distributed lyapunov-based model predictive control strategy. Above all, the sampled-data-based event-triggered mechanism with a newly developed triggering rule is designed. Meanwhile, a new collision avoidance method consisting of a collision avoidance constraint, a supplemental terminal collision avoidance constraint and a constraint convexification strategy is proposed. Besides, an innovative contraction constraint is established according to a novel nonlinear backstepping technique-basedlyapunov auxiliary control law considering external disturbances, which is introduced into the online local optimal control problem to form the distributed lyapunov-based model predictive control strategy. Furthermore, the recursive feasibility and closed-loop stability of the system are strictly analyzed. Finally, the effectiveness and superiority of SREDFTC are demonstrated by extensive comparative simulations. Simulations demonstrate that the distributed lyapunov-based model predictive control strategy not only improves the convergence speed and the tracking accuracy of the system significantly, but also enhances the system's stability and robustness against external disturbances greatly. In addition to this, the collision avoidance method exhibits superior capability in collision avoidance to ensure the safety of the system, and the constraint convexification strategy reduces the computational complexity and improves the solving speed of the online local optimal control problem greatly. What's more, the sampled-data-based event-triggered mechanism decreases optimization times of the online local optimal control problem and the number of data t
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