One weakness of the model predictive control method is that the predicted states/outputs are constructed by an exact nominal model. Its accuracy varies if uncertainties exist, which will ultimately deteriorate the closed-loop control performances. To this end, we propose a generalized dynamic predictive control method for a class of lower-triangular systems subjected to nonparametric uncertainties. Instead of relying on the inherent robustness property of the standard predictive controller or on-/off-line parameter identification, a dual-layer adaptive law is designed to estimate the lumped effect of system uncertainties. As another main contribution, under a less ambitious but more practical control objective, namely semi-global stability, various nonlinearity growth constraints utilized in the existing related methods could be essentially relaxed. Numerical simulation and illustrative experimental tests of a series elastic actuator system are provided to demonstrate both simplicity and effectiveness of the proposed method.

中文翻译：

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非参数不确定系统的广义动态预测控制及其在串联弹性执行器中的应用


模型预测控制方法的一个弱点是预测状态/输出是由精确的标称模型构建的。如果存在不确定性，其精度就会变化，最终会恶化闭环控制性能。为此，我们提出了一种针对非参数不确定性下三角系统的广义动态预测控制方法。设计双层自适应律来估计系统不确定性的集总效应，而不是依赖于标准预测控制器或在线/离线参数识别的固有鲁棒性特性。作为另一个主要贡献，在不太雄心勃勃但更实际的控制目标下，即半全局稳定性，现有相关方法中使用的各种非线性增长约束可以本质上放松。提供了串联弹性致动器系统的数值模拟和说明性实验测试，以证明所提出方法的简单性和有效性。