基于状态空间方法的显式GPC算法 全局收敛性分析
Analysis of Globe Convergence of Explicit GPC Algorithm Based on State-Space Method
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摘要: 采用状态方程的方法,给出了基于CARMA模型的显式GPC(Generalized Predictive Control) 算法的稳定性及收敛性证明,辨识算法采用增广最小二乘法。并从理论上阐明了基于CARMA模 型的GPC算法优于目前使用的一些自校正算法,象广义最小方差法、极点配置等方法。在较弱的 条件下即具有良好的稳定与收敛性。Abstract: The global stability and convergence of the explicit GPC (generalized predictive control) based on CARMA model are proved by employing the state-equation, in which the extended least-square method is taken as the identification method. It is shown in theory that the GPC algorithm based on CARMA is better than some currently used self-tuning algorithms such as the generalized minimum-variance method and pole-placement method, and so on, because it has good stability and convergence even underweaker conditions.
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Key words:
- explicit methods /
- state-space method /
- convergence /
- GPC
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