线性二次型及模型预测控制编队控制研究

多智能体集群作战是未来战场的重要形式。作为集群作战中的关键技术,编队控制有着广泛的应用。以编队控制律为研究目标,选择2种优化控制方法:线性二次型调节器(linear quadratic regulator,LQR)方法和模型预测控制(model predictive control,MPC)方法。针对圆形编队轨迹,采用二阶动力学模型,基于相似一致性算法,在二维平面上仿真4个智能体绕轨迹中心旋转的编队运动场景,探究2种优化控制方法仿真效果。结果表明模型预测控制相比线性二次型调节器控制收敛速度更快,控制稳定性更高,侧向误差更小。增加侧向加速度补偿之后编队智能体在模型预测控制的作用下,圆形轨迹侧向追踪误差大幅度降低。

Formation Control with Linear Quadratic Regulator and Model Predictive Control

Multi-agent group combat is an important form in the future battlefield.As a key technology in group operations,formation control has a wide range of application.Focusing on the formation control law,two optimal control methods are applied:linear quadratic regulator(LQR)and model predictive control(MPC).The methods are analyzed in a circular formation scene.Based on the similarity consistency algorithm,utilizes second-order dynamic model to explore the optimal control effect of four agents rotating around the trajectory center in the two-dimension plane.The results show that the model predictive control has larger speed of convergence,higher control stability and smaller side errors than the linear quadratic regulator method.The lateral error of the formation agent following a circular trajectory is greatly reduced by MPC with lateral acceleration compensation.