An inverse-model approach to multivariable norm optimal iterative learning control with auxiliary optimisation

Abstract

Motivated by the commonly encountered problem in which tracking is only required at selected intermediate points within the time interval, a general optimization-based Iterative Learning Control (ILC) algorithm is derived that ensures convergence of tracking errors to zero whilst simultaneously minimizing a specified quadratic objective function of the input signals and chosen auxiliary (state) variables. In practice the proposed solutions enable a repeated tracking task to be accurately completed whilst simultaneously reducing undesirable effects such as payload spillage, vibration tendencies and actuator wear. The theory is developed using the well-known Norm Optimal ILC (NOILC) framework, using general linear, functional operators between real Hilbert spaces. Solutions are derived using feedforward action, convergence is proved and robustness bounds are presented using both norm bounds and positivity conditions. Algorithms are specified for both continuous and discrete-time state space representations, with the latter including application to multi-rate sampled systems. Experimental results using a robotic manipulator confirm the practical utility of the algorithms and the closeness with which observed results match theoretical predictions.