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首页> 外文期刊>Connection Science >Multi-objective optimal design of online PID controllers using model predictive control based on the group method of data handling-type neural networks
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Multi-objective optimal design of online PID controllers using model predictive control based on the group method of data handling-type neural networks

机译：基于数据处理型神经网络分组方法的模型预测控制在线PID控制器多目标优化设计

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摘要

In this paper, model predictive control (MPC) is used for optimal selection of proportional-integral-derivative (PID) controller gains. In conventional tuning methods a history of response error of the system under control in the passed time is measured and used to adjust PID parameters in order to improve the performance of the system in proceeding time. But MPC obviates this characteristic of classic PID. In fact MPC tries to tune the controller by predicting the system's behaviour some time steps ahead. In this way, PID parameters are adjusted before any real error occurs in the system's response. For this purpose, polynomial meta-models based on the evolved group method of data handling neural networks are obtained to simply simulate the time response of the dynamic system. Moreover, a non-dominated sorting genetic algorithm has been used in a multi-objective Pareto optimisation to select the parameters of the MPC which are prediction horizon, control horizon and relation of weight of △u and error, to minimise simultaneously two objective functions that are control effort and integral time absolute error of the system response. The results mentioned at the end obviously declare that the proposed method surpasses conventional tuning methods for PID controllers, and Pareto optimal selection of predictive parameters also improves the performance of the introduced method.
机译：在本文中，模型预测控制（MPC）用于比例积分微分（PID）控制器增益的最佳选择。在常规的调谐方法中，测量在经过的时间内受控制的系统的响应误差的历史，并用于调整PID参数，以改善系统在运行时间内的性能。但是MPC消除了经典PID的这一特性。实际上，MPC会通过提前一些时间预测系统行为来尝试调整控制器。通过这种方式，可以在系统响应中发生任何实际错误之前调整PID参数。为此，获得了基于数据处理神经网络的演化群方法的多项式元模型，以简单地模拟动态系统的时间响应。此外，在多目标Pareto优化中使用了非支配的排序遗传算法来选择MPC的参数，这些参数是预测范围，控制范围以及△u和误差的权重关系，以同时最小化两个目标函数：是系统响应的控制努力和积分时间绝对误差。最后提到的结果显然表明，该方法超越了PID控制器的常规调整方法，并且Pareto预测参数的最优选择也提高了所引入方法的性能。

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