Search

Iterative learning control (ILC) enables high control performance through learning from the measured data, using only limited model knowledge in the form of a nominal parametric model to guarantee convergence. The aim of this brief is to develop a range of approaches for multivariable ILC, where specific attention is given to addressing interaction. The proposed methods either address the interaction in the nominal model or as uncertainty, i.e., through robust stability. The result is a range of techniques, including the use of the structured singular value (SSV) and Gershgorin bounds, which provide a different tradeoff between modeling requirements, i.e., modeling effort and cost and achievable performance. This allows an appropriate choice in view of modeling budget and performance requirements. The tradeoff is demonstrated in a case study on an industrial printer. [ABSTRACT FROM AUTHOR]

Repetitive control (RC) enables high control performance for systems subject to periodic disturbances. Many disturbances in mechatronic applications involve the sum of multiple periodic signals, which is not necessarily periodic over a small time interval. The contribution of this article is a design procedure for multiple repetitive controllers, each addressing a single periodic component that achieves high performance with fast convergence in the presence of multiple periodic disturbances. The developed approach involves sequential design of the controllers, and systematically addresses modeling errors and coupling between controllers. An experimental case study on an industrial roll-to-roll printer confirms the benefits of the proposed approach, including superior performance compared to preexisting RC designs. [ABSTRACT FROM AUTHOR]

The print productivity and medium dimensions of wide-format roll-to-roll printing systems are limited by position errors induced by step-wise medium transportation. The aim of this article is to develop a design framework for multivariable repetitive control (RC), that enables improved productivity and accuracy through continuous media flow printing. The developed technical framework for RC explicitly addresses the tradeoff between performance and modeling requirements through decentralized designs. In particular, systematic design approaches are developed that explicitly address unmodeled interaction as uncertainty, i.e., through robustness. The result is a range of solutions, including 1) independent single-input single-output (SISO) designs, and 2) sequential SISO design. Experimental results confirm that the developed RC framework in conjunction with continuous media flow printing outperforms existing approaches. [ABSTRACT FROM AUTHOR]

Learning control enables significant performance improvement for systems that perform repeating tasks. Achieving high tracking performance by utilizing past error data typically requires noncausal learning that is based on a parametric model of the process. Such model-based approaches impose significant requirements on modeling and filter design. The aim of this paper is to reduce these requirements by developing a learning control framework that enables performance improvement through noncausal learning without relying on a parametric model. This is achieved by explicitly using the discrete Fourier transform to enable learning by using a nonparametric frequency response function model of the process. The effectiveness of the developed method is illustrated by application to a nanopositioning stage. [ABSTRACT FROM AUTHOR]

Trial-varying disturbances are a key concern in Iterative Learning Control (ILC) and may lead to inefficient and expensive implementations and severe performance deterioration. The aim of this paper is to develop a general framework for optimization-based ILC that allows for enforcing additional structure, including sparsity. The proposed method enforces sparsity in a generalized setting through convex relaxations using ℓ 1 norms. The proposed ILC framework is applied to the optimization of sampling sequences for resource efficient implementation, trial-varying disturbance attenuation, and basis function selection. The framework has a large potential in control applications such as mechatronics, as is confirmed through an application on a wafer stage. [ABSTRACT FROM AUTHOR]

Feedforward control enables high performance for industrial motion systems that perform nonrepeating motion tasks. Recently, learning techniques have been proposed that improve both performance and flexibility to nonrepeating tasks in a batch-to-batch fashion by using a rational parameterization in feedforward control. This paper aims to unify these approaches through a single framework that provides transparent connections and clear differences between the alternatives. Experimental results on an industrial motion system confirm the theoretical findings and illustrate benefits of rational feedforward tuning in motion systems, including preactuation and postactuation. [ABSTRACT FROM PUBLISHER]

Performance requirements in precision motion systems, including those used in integrated circuit manufacturing and printing systems, are ever increasing. For instance, internal deformations cannot be neglected anymore. As a result, measured signals at sensor locations cannot be used directly to evaluate performance at the point of interest. The aim of this brief is to develop an inferential motion control framework that explicitly distinguishes between performance variables and measured variables. In the proposed framework, a dynamic model is used to infer the performance variables from the measured variables. As the inferred performance variables depend on the model quality, an identification for robust inferential control approach is pursued that tightly captures the uncertainty. Experimental results on a prototype motion system reveal that ignoring internal deformations using traditional motion control design approaches can lead to disastrous performance at the point of interest. In addition, it is shown that the proposed inferential motion control framework leads to high performance at the unmeasurable point of interest. [ABSTRACT FROM PUBLISHER]

Next-generation precision motion systems are lightweight to meet stringent requirements regarding throughput and accuracy. Such lightweight systems typically exhibit lightly damped flexible dynamics in the controller cross-over region. State-of-the-art modeling and motion control design procedures do not deliver the required model complexity and fidelity to control the flexible dynamical behavior. The aim of this paper is to develop a combined system identification and robust control design procedure for high performance motion control and apply it to a wafer stage. Hereto, new connections between system identification and robust control are employed. The experimental results confirm that the proposed procedure significantly extends existing results and enables next-generation motion control design. [ABSTRACT FROM AUTHOR]

Model-based control design requires a careful specification of performance and robustness requirements. In typical norm-based control designs, performance and robustness requirements are specified in a scalar optimization criterion, even for complex multivariable systems. This paper aims to develop a novel approach for the formulation of this optimization criterion for multivariable motion systems that exhibit spatio-temporal deformations. To achieve this, characteristics of the underlying system are exploited to design multivariable weighting functions. In contrast to pre-existing approaches, which typically lead to diagonal weighting functions, the proposed approach enables the design of non-diagonal weighting functions. Extensive experimental results confirm that the proposed procedure can significantly improve the performance of an industrial motion system compared to earlier approaches. [ABSTRACT FROM AUTHOR]

Next-generation deformable mirrors are envisaged to exhibit low-frequency flexible dynamics and to contain a large number of spatially distributed actuators due to increasingly stringent performance requirements. The increasingly complex system characteristics necessitate identifying the flexible dynamic behavior for design validation and next-generation control. The aim of this paper is to develop a unified approach for the identification of mechanical systems with a large number of spatially distributed actuators and a limited number of sensors. A frequency domain-based approach using local modeling techniques is developed. The modal modeling framework is employed to analyze the design and create outputs that were not measured. The proposed approach is applied to an experimental deformable mirror case study that illustrates the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Actuators that require commutation algorithms, such as the switched reluctance motor (SRM) considered in this paper and employed in the coarse pointing assembly (CPA) for free-space optical communication, often have torque-ripple disturbances that are periodic in the commutation-angle domain that deteriorate the positioning performance. The aim of this paper is to model the torque ripple as a Gaussian Process (GP) in the commutation-angle domain and consequently compensate for it at arbitrary velocity. The approach employs repetitive control (RC) at a constant velocity. A spatial GP with a periodic kernel is trained using data that is obtained from the RC step resulting in a static non-linear function for compensation at arbitrary velocity. Stability conditions are provided for both steps. The approach is successfully applied to a CPA prototype to improve the tracking performance for laser communication, where the torque ripple is compensated at arbitrary velocity. [ABSTRACT FROM AUTHOR]