This paper combines learning theory with robust control and discusses robust control design problems involving real parameter uncertainty in control systems based on randomized algorithms.
将学习理论与鲁棒控制相结合，采用随机化算法针对实参数不确定系统讨论了鲁棒控制器的设计问题。
Theoretical analysis indicates that iterative learning control algorithm is robust if initial shift and System parameter disturbance within limited bound.
理论分析表明，当系统状态初值漂移和系统参数扰动在一定范围内，迭代学习控制算法关于是鲁棒的。
Simulation results of contrapose Bioreactor show that the proposed method can accelerate learning process and is robust to larger parameter changes.
生化反应器定值控制的仿真结果表明，该方法加快了学习过程，并对更大范围的参数变化具有鲁棒性。
Simulation results of contrapose Bioreactor show that the proposed method can accelerate learning process and is robust to larger parameter changes.
生化反应器定值控制的仿真结果表明，该方法加快了学习过程，并对更大范围的参数变化具有鲁棒性。
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