采用动态协调的混杂分布式预测控制方法

针对多智能体复杂协调控制问题,提出基于动态协调规则的分布式预测控制。将避碰约束处理为基于方位的混杂规则,并在代价函数中引入布尔函数项。为适应复杂时变的工况,在每个采样时刻,根据各智能体的位置关系及其与目标的相对距离,设计动态协调规则以确定布尔函数项的权值。此方法增强了运动方向一致性和控制行为一致性,改进了分布式预测控制的稳定性和可行性。由于取较短的预测时域即可达到控制目标,此方法也提高了分布式预测控制方法的实时性和实用性。给出仿真例子验证了此方法的有效性。

Hybrid distributed model predictive control using dynamic cooperation

For complex cooperative control problem of multi-agent systems, a distributed model predictive control scheme based on the dynamic cooperative rules is proposed. The collision avoidance constraint is transformed into hybrid rules based on the positions, and the Boolean function term was introduced in the cost function. In order to accommodate the complex time-varying environment, at each sample moment, the dynamic cooperative rules are designed according to the relative positions among the agents and the relative distances between the agents and the destination, so as to determine the weights in the Boolean function. This scheme reinforces consistencies of motion direction and control action, and improves stability and feasibility of distributed predictive control. As the control target can be achieved via a small prediction horizon, this scheme also enhances the real-time ability and practicality of distributed model predictive control. Simulation examples are given to illustrate the effectiveness of the proposed scheme.