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首页> 外文期刊>IEEE Transactions on Energy Conversion >A least-squares based model-fitting identification technique for diesel prime-movers with unknown dead-time
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A least-squares based model-fitting identification technique for diesel prime-movers with unknown dead-time

机译：死区时间未知的柴油原动机的最小二乘模型拟合识别技术

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摘要

The recursive least-squares estimation technique is extended for application to systems with small but unknown dead-time delays. This problem is studied with particular reference to when the diesel engine is used as a prime-mover, in which case the dead-time leads to a significant variation in dynamic performance. In practice, least-squares estimators are found to have slow convergence and large output errors, when applied to systems with time-delays. It is shown that by a suitably constrained algorithm, convergence can be quickened and output error can be kept within acceptable limits. Low-order system models with changing dead-time are in general, characterized by modeling errors that are to an extent correlated with the input control signal to the plant. Despite this fact, the developed approach yields very small prediction errors, even when large deterministic disturbances are present. This suggests its possible use in conjunction with adaptive controllers of various types.
机译：递归最小二乘估计技术已扩展到适用于具有较小但未知死区延迟的系统。专门针对将柴油发动机用作原动机的问题进行了研究，在这种情况下，停滞时间会导致动态性能发生重大变化。在实践中，将最小二乘估计器应用于具有时延的系统时，收敛速度较慢，输出误差较大。结果表明，通过适当约束的算法，可以加快收敛速度​​，并将输出误差保持在可接受的范围内。通常，具有死区时间变化的低阶系统模型的特征是建模误差在一定程度上与设备的输入控制信号相关。尽管如此，即使存在较大的确定性干扰，所开发的方法也会产生非常小的预测误差。这表明它可能与各种类型的自适应控制器结合使用。

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