论文标题
集中式模型预测控制具有分布式适应性
Centralized model predictive control with distributed adaptation
论文作者
论文摘要
出现了一个不知道局部不确定性的集中式模型预测控制器（MPC）。假定局部不确定性是匹配，界限和结构化的。为了遇到干扰并提高绩效，在本地采用了自适应控制机制。提出的方法确保了闭环状态的投入到国家稳定性，并收敛到平衡点。此外，通过使用自适应控制机制，根据给定的特征基础学习了不确定性。另外，满足了状态和控制的严格约束。
A centralized model predictive controller (MPC), which is unaware of local uncertainties, for an affine discrete time nonlinear system is presented. The local uncertainties are assumed to be matched, bounded and structured. In order to encounter disturbances and to improve performance, an adaptive control mechanism is employed locally. The proposed approach ensures input-to-state stability of closed-loop states and convergence to the equilibrium point. Moreover, uncertainties are learnt in terms of the given feature basis by using adaptive control mechanism. In addition, hard constraints on state and control are satisfied.
