Tracking Under Measurable and Unmeasurable State Information

ILC is a useful control mechanism for enhancing dynamic systems’ tracking performance by incorporating mistake knowledge into the control for successive iterations. Because of its simplicity and efficacy, ILC has been discovered to be a good alternative in various fields and applications, such as robotic manipulators Tayebi (2004); Sun et al. (2006), hard disk drives Wu and Tomizuka (2010), data-driven control Chi et al. (2015, 2016), rapid thermal processing Yang et al. (2003), and multi-agent systems Meng et al. (2014, 2015); Li and Li (2014). For instance, in Chien and Yao (2004), an iterative learning controller is designed for a class of repeatable nonlinear systems with uncertain parameters and initial output resetting error using a model reference adaptive control technique. The relative formation between agents approaches the target formation exponentially, as demonstrated in Meng and Moore (2016). When the union of interaction graphs with spanning trees occurs frequently along the iteration axis, a good performance in formation is observed. In addition, the iterative learning problem for discrete-time systems with an event-triggered scheme and quantization has been studied in Xiong et al. (2016).