This study investigates the consensus problem of a nonlinear discrete-time multi-agent system (MAS) under bounded additive disturbances. We propose a self-triggered robust distributed model predictive control consensus algorithm. A new cost function is constructed and MAS is coupled through this function. Based on the proposed cost function, a self-triggered mechanism is adopted to reduce the communication load. Furthermore, to overcome additive disturbances, a local minimum-maximum optimization problem under the worst-case scenario is solved iteratively by the model predictive controller of each agent. Sufficient conditions are provided to guarantee the iterative feasibility of the algorithm and the consensus of the closed-loop MAS. For each agent, we provide a concrete form of compatibility constraint and a consensus error terminal region. Numerical examples are provided to illustrate the effectiveness and correctness of the proposed algorithm.

本研究调查了有界加性扰动下非线性离散时间多智能体系统 (MAS) 的一致性问题。我们提出了一种自触发的鲁棒分布式模型预测控制共识算法。构建了一个新的成本函数，并通过该函数耦合了 MAS。基于所提出的成本函数，采用自触发机制来减少通信负载。此外，为了克服加性干扰，每个代理的模型预测控制器迭代地解决最坏情况下的局部最小-最大优化问题。提供了足够的条件来保证算法的迭代可行性和闭环MAS的共识。对于每个代理，我们提供了兼容性约束的具体形式和共识错误终端区域。