Subspace Predictive Dynamic Control Allocation for Overactuated System with Actuator Dynamics
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摘要: 针对具有冗余执行机构的过驱动系统的最优控制分配问题, 基于数据驱动的子空间辨识方法和预测控制理论, 提出了一种考虑执行器动力学特性的动态控制分配新方法. 在考虑范数有界不确定性的在线子空间辨识的基础上, 对执行器动力学特性进行不确定性建模, 再结合预测控制理论进行动态控制分配. 从而将执行机构的动力学建模、控制量最优分配和执行机构控制律的设计包含在一个子系统框架内, 对执行机构的模型不确定性具有更好的鲁棒性. 最后给出仿真实例验证了算法的有效性.Abstract: In this paper, a new data driven subspace predictive dynamic control allocation approach is proposed for overactuated system with actuator dynamics．Through online subspace identification, the uncertainty model is used for better description of the actuator dynamic characteristic．Then the novel dynamic control allocation with actuator dynamics is designed under the subspace predictive control framework. The actuator modeling, design of control allocator and the actuator controller are then included in a framework which increases robustness of the control system．Finally, an example is given to demonstrate the efficiency of the proposed method.
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