论文标题
使用干扰预测，对非线性过程的收缩约束模型预测控制
A Contraction-constrained Model Predictive Control for Nonlinear Processes using Disturbance Forecasts
论文作者
论文摘要
模型预测控制（MPC）已成为过程行业中最广泛使用的高级控制方法。在许多情况下，可以根据天气预报预测可再生能源的预测可再生能源。虽然干扰的预测可能不是准确的，但利用信息可以显着改善控制性能，以响应干扰。通过利用过程和干扰模型，可以预测未来的系统行为，并用于通过最小化结合这些预测的经济成本函数来优化控制动作。但是，当过程是非线性时，在这种方法中通常很难对所得闭环系统的稳定性保证。下文中提出的是一个受收缩约束的预测控制器，该控制器优化了过程经济，同时确保稳定对经受干扰测量和预测的操作目标。
Model predictive control (MPC) has become the most widely used advanced control method in process industry. In many cases, forecasts of the disturbances are available, e.g., predicted renewable power generation based on weather forecast. While the predictions of disturbances may not be accurate, utilizing the information can significantly improve the control performance in response to the disturbances. By exploiting process and disturbance models, future system behaviour can be predicted and used to optimise control actions via minimisation of an economical cost function which incorporates these predictions. However, stability guarantee of the resulting closed-loop system is often difficult in this approach when the processes are nonlinear. Proposed in the following article is a contraction-constrained predictive controller which optimises process economy whilst ensuring stabilisation to operating targets subject to disturbance measurements and forecasts.
