Kernel-based identification of non-causal systems with application to inverse model control

Models of inverse systems are commonly encountered in control, e.g., feedforward. The aim of this paper is to address several aspects in identification of inverse models, including model order selection and dealing with unstable inverse systems that originate from inverting non-minimum phase dynamics. A kernel-based regularization framework is developed for identification of non-causal systems. It is shown that ‘unstable’ models can be viewed as bounded, but non-causal, operators. As the main contribution, a range of the required kernels for non-causal systems is developed, including non-causal stable spline kernels. Benefits of the approach are confirmed in an example, including non-causal feedforward control for non-minimum phase systems

Design and modeling aspects in multivariable iterative learning control

Iterative Learning Control (ILC) can significantly improve the performance of systems that perform repeating tasks. Typically, several decentralized ILC controllers are designed and implemented. Such ILC designs tacitly ignore interaction. The aim of this paper is to further analyze the consequences of interaction in ILC, and develop a solution framework, covering a spectrum of systematic decentralized designs to centralized designs. The proposed set of solutions differs in design, i.e., performance and robustness, and modeling requirements, which are investigated in detail. The benefits and differences are demonstrated through a simulation study. Iterative Learning Control (ILC) can significantly improve the performance of systems that perform repeating tasks. Typically, several decentralized ILC controllers are designed and implemented. Such ILC designs tacitly ignore interaction. The aim of this paper is to further analyze the consequences of interaction in ILC, and develop a solution framework, covering a spectrum of systematic decentralized designs to centralized designs. The proposed set of solutions differs in design, i.e., performance and robustness, and modeling requirements, which are investigated in detail. The benefits and differences are demonstrated through a simulation stud

Design framework for multivariable ILC : (de)centralized design & modeling considerations

abstract onl

Kernel-based regression of non-causal systems for inverse model feedforward estimation

\u3cp\u3eInversion-based feedforward control enables high performance for industrial motion systems. To this end, accurate knowledge of the inverse system is required, and non-causal control actions must be enabled. The aim of this paper is to accurately identify non-causal inverse models in view of high feedforward control performance. The developed method employs kernel-based regularization to minimize the mean squared error of the estimate. The performance benefits of the presented approach are demonstrated on an industrial printing system, including non-causal feedforward control actions.\u3c/p\u3

Inverse system estimation for feedforward:a kernel-based approach for non-causal systems

\u3cp\u3eAccurate models of inverse systems are required for high performance in inverse model-based feedforward control. Identification of inverse systems can be challenging, especially if the inverse system has poles outside the typical stability region. The aim of this paper is to estimate non-causal models of inverse systems, for intended use in feedforward control, where non-causality can be exploited to compensate ‘unstable’ poles. The developed method employs kernel-based regularization to improve the bias/variance trade-off, where the non-causal kernel is constructed using rational basis functions that include poles outside the usual stability region. The benefits of the developed method are demonstrated on an example, including non-causality.\u3c/p\u3

Data-driven feedforward learning using non-causal rational basis functions : application to an industrial flatbed printer:application to an industrial flatbed printer

\u3cp\u3eData-driven feedforward learning enables high performance for industrial motion systems based on measured data from previous motion tasks. The key aspect herein is the chosen feedforward parametrization, which should parsimoniously model the inverse system. At present, high performance comes at the cost of parametrizations that are nonlinear in the parameters and consequences thereof. A linear parametrization is proposed that enables parsimonious modeling of inverse systems for feedforward through the use of non-causal rational orthonormal basis functions. The benefits of the proposed parametrization are experimentally demonstrated on an industrial printer, including pre-actuation and cyclic pole repetition.\u3c/p\u3

Flexible ILC : towards a convex approach for non-causal rational basis functions

Iterative learning control (ILC) is subject to a trade-off between effective compensation of repeating disturbances, and amplification of non-repeating disturbances. Although important progress has been made in enhancing the flexibility of ILC to non-repeating tasks by means of basis functions, at present high performance comes at the cost of non-convex optimization. The aim of this paper is to develop a convex approach to ILC with rational basis functions. A key aspect of the proposed approach is the use of orthonormal basis functions in L2, such that non-causal control actions can be utilized. The benefits of using non-causal rational basis functions in ILC are demonstrated by means of a relevant example

Multivariable repetitive control design framework applied to flatbed printing with continuous media flow

\u3cp\u3eThe production speed and medium versatility in traditional wide-format printers are limited by medium positioning errors caused by step-wise transportation. The aim of this paper is to develop a repetitive control (RC) framework, that enables continuous media flow printing with enhanced positioning accuracy and increased productivity. The developed framework explicitly addresses the trade-off between performance and model knowledge. Specific solutions that avoid the need for a full multivariable model include i) independent SISO design, and ii) sequential SISO design. The benefits of the pursued RC approach for continuous media flow printing are experimentally validated on an industrial flatbed printer.\u3c/p\u3