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Design of a CMAC-Based PID Controller Using Operating Data

机译:基于运行数据的基于CMAC的PID控制器设计

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In industrial processes, PID control strategy is still applied in a lot of plants. However, real process systems are nonlinear, thus it is difficult to obtain the desired control performance using fixed PID parameters. Cerebellar model articulation controller (CMAC) is attractive as an artificial neural network in designing control systems for nonlinear systems. The learning cost is drastically reduced when compared with other multi-layered neural networks. On the other hand, theories which directly calculate control parameters without system parameters represented by Virtual Reference Feedback Tuning (VRFT) or Fictitious Reference Iterative Tuning (FRIT) have received much attention in the last few years. These methods can calculate control parameters using closed-loop data and are expected to reduce time and economic costs. In this paper, an offline-learning scheme of CMAC is newly proposed. According to the proposed scheme, CMAC is able to learn PID parameters by using a set of closed-loop data. The effectiveness of the proposed method is evaluated by a numerical example.
机译:在工业过程中,PID控制策略仍在许多工厂中应用。但是,实际的过程系统是非线性的,因此使用固定的PID参数很难获得所需的控制性能。小脑模型关节控制器(CMAC)作为人工神经网络在设计非线性系统的控制系统方面具有吸引力。与其他多层神经网络相比,学习成本大大降低。另一方面,在过去几年中,直接计算控制参数而没有以虚拟参考反馈调整(VRFT)或虚拟参考迭代调整(FRIT)表示的系统参数的理论受到了广泛的关注。这些方法可以使用闭环数据计算控制参数,并有望减少时间和经济成本。本文提出了一种新的CMAC离线学习方案。根据提出的方案,CMAC能够通过使用一组闭环数据来学习PID参数。数值算例验证了该方法的有效性。

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