非参数不确定系统约束迭代学习控制

讨论一类非参数不确定系统的约束迭代学习控制问题.构造二次分式型障碍李雅普诺夫函数(Barrier Lyapunov functions),用于学习控制器设计.控制方案采用鲁棒方法与学习机制相结合的手段处理非参数不确定性,鲁棒方法对处理后的不确定性的界予以补偿,学习机制对处理后的不确定性进行估计.可实现系统状态在整个作业区间上完全跟踪参考轨迹,并使得系统误差的二次型在迭代过程中囿于预设的界内,进而在运行过程中实现状态约束.提出的迭代学习算法包括部分限幅与完全限幅学习算法.采用这种BLF约束控制系统有利于提高控制系统中设备安全性.仿真结果用于验证所提出控制方法的有效性.

Constrained iterative learning control of a class of non-parametric uncertain systems

A Barrier-Lyapunov-function-based state-constrained iterative learning control is presented for a class of nonparametric uncertain systems. The suggested Barrier Lyapunov function (BLF) is a quadratic fraction, which is simple in form in comparison with the existing nonlinear ones. Through Lyapunov synthesis, the learning controller design is carried out. The nonparametric uncertainty of system dynamics are tackled through the robust treatment and learning mechanism. It is shown that the system state can track the reference trajectory over the entire time interval as iteration increases, while the quadratic tracking error, as a measure of the constraint, is enforced to stay in the pre-specified range. The constrained system state is in turn achieved. The performances of partially and fully saturated learning algorithms are characterized, respectively. The proposed learning control scheme is helpful for the effective protection of equipment in the closed-loop system, due to the employment of the Barrier Lyapunov function. Numerical results are presented to demonstrate the effectiveness of the learning control scheme.