This paper proposes a tractable robust nonlinear model predictive control for continuous-time uncertain systems with stability guaranteed. The uncertainty is considered in parameters or additive form. First, a sampled-data model predictive control for the nominal system is designed to provide the desired performance. Then, an adaptive sliding mode control is designed to recover the nominal performance for the uncertain system. By merging sampled-data model predictive control and sliding mode control in-between samples, the effect of the uncertainty is reduced efficiently. The computational complexity of the proposed robust model predictive control is the same as for the model predictive control while asymptotic stability of the closed-loop system is achieved. The simulation results illustrate the effectiveness of the proposed approaches.

中文翻译：

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不确定非线性系统的自适应滑模可追踪鲁棒模型预测控制

针对连续时间不确定系统的稳定性保证，本文提出了一种易于处理的鲁棒非线性模型预测控制。不确定性以参数或附加形式考虑。首先，标称系统的采样数据模型预测控制旨在提供所需的性能。然后，设计了一种自适应滑模控制来恢复不确定系统的标称性能。通过合并采样数据模型预测控制和样本之间的滑模控制，有效降低了不确定性的影响。所提出的鲁棒模型预测控制的计算复杂度与模型预测控制相同，同时实现了闭环系统的渐近稳定性。仿真结果说明了所提出方法的有效性。