论文标题
通过神经非线性自回归外源模型学到的系统的强大无偏移非线性模型预测控制
Robust offset-free nonlinear model predictive control for systems learned by neural nonlinear autoregressive exogenous models
论文作者
论文摘要
本文提出了一个可靠的模型预测控制（MPC）方案，该方案为神经非线性自动回归外源性（NNARX）模型所描述的系统提供了无偏移的设定值跟踪。 NNARX模型从输入输入数据中学习了工厂的动态，在训练期间，增量输入到国家稳定性（$δ$ ISS）属性被迫保证稳定性。然后，通过对输出跟踪误差的明确积分动作进行增强训练的NNARX模型，该模型允许控制方案享有无偏移跟踪能力。最终设计了基于管的MPC，利用模型的独特结构，以确保在存在模型植物不匹配或未知的干扰的情况下，稳健的渐近零误差调节恒定参考信号。水加热系统上的数值模拟显示了提出的对照算法的有效性。
This paper presents a robust Model Predictive Control (MPC) scheme that provides offset-free setpoint tracking for systems described by Neural Nonlinear AutoRegressive eXogenous (NNARX) models. The NNARX model learns the dynamics of the plant from input-output data, and during the training the Incremental Input-to-State Stability ($δ$ISS) property is forced to guarantee stability. The trained NNARX model is then augmented with an explicit integral action on the output tracking error, which allows the control scheme to enjoy offset-free tracking ability. A tube-based MPC is finally designed, leveraging the unique structure of the model, to ensure robust stability and robust asymptotic zero error regulation for constant reference signals in the presence of model-plant mismatch or unknown disturbances. Numerical simulations on a water heating system show the effectiveness of the proposed control algorithm.
