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首页> 外文期刊>Journal of Process Control >Consistent hierarchical economic NMPC for a class of hybrid systems using neighboring-extremal updates
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Consistent hierarchical economic NMPC for a class of hybrid systems using neighboring-extremal updates

机译:使用相邻极值更新的一类混合系统的一致分层经济NMPC

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摘要

A hierarchical two-layer control algorithm is developed for a class of hybrid (discrete-continuous dynamic) systems to support economically optimal operation of batch or continuous processes with a predefined production schedule. For this class of hybrid systems, the optimal control moves as well as the controlled switching times between two adjacent modes are determined online. In contrast to closely related schemes for integrated scheduling and control, the sequence of modes is not optimized. On the upper layer, the economic optimal control problem is solved rigorously by a slow hybrid economic model predictive controller at a low sampling rate. On the lower layer, a fast hybrid neighboring-extremal controller is based on the same economic optimal control problem as the slow controller to ensure consistency between both layers. The fast neighboring-extremal controller updates rather than tracks the optimal trajectories from the upper layer to account for disturbances. Consequently, the fast controller steers the process to its operational bounds under disturbances and the economic potential of the process is exploited anytime. The suggested two-layer control algorithm provides fully consistent control action on the fast and slow time-scale and thus avoids performance degradation and even infeasibilities which are commonly encountered if inconsistent optimal control problems are formulated and solved.
机译:针对一类混合(离散-连续动态)系统开发了一种分层的两层控制算法,以支持具有预定生产计划的批量或连续过程的经济最佳运行。对于此类混合动力系统,可以在线确定最佳控制运动以及两个相邻模式之间的受控切换时间。与密切相关的用于集成调度和控制的方案相比,未优化模式序列。在上层,通过慢速混合经济模型预测控制器以低采样率严格解决了经济最优控制问题。在较低的层上,快速混合邻居-外部控制器基于与慢速控制器相同的经济最优控制问题,以确保两层之间的一致性。快速的相邻极值控制器更新而不是跟踪来自上层的最佳轨迹来解决干扰问题。因此,快速控制器可在受到干扰的情况下将过程引导至其操作范围,并且可随时利用过程的经济潜力。所建议的两层控制算法在快速和慢速时间尺度上提供了完全一致的控制动作,因此避免了性能恶化,甚至避免了在制定和解决不一致的最佳控制问题时通常遇到的不可行性。

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