Direct Learning for Parameter-Varying Feedforward Control: A Neural-Network Approach

The performance of a feedforward controller is primarily determined by the extent to which it can capture the relevant dynamics of a system. The aim of this paper is to develop an input-output linear parameter-varying (LPV) feedforward parameterization and a corresponding data-driven estimation method in which the dependency of the coefficients on the scheduling signal are learned by a neural network. The use of a neural network enables the parameterization to compensate a wide class of constant relative degree LPV systems. Efficient optimization of the neural-network-based controller is achieved through a Levenberg-Marquardt approach with analytic gradients and a pseudolinear approach generalizing Sanathanan-Koerner to the LPV case. The performance of the developed feedforward learning method is validated in a simulation study of an LPV system showing excellent performance.Comment: Final author version, accepted for publication at 62nd IEEE Conference on Decision and Control, Singapore, 202

Direct Learning for Parameter-Varying Feedforward Control:A Neural-Network Approach

The performance of a feedforward controller is primarily determined by the extent to which it can capture the relevant dynamics of a system. The aim of this paper is to develop an input-output linear parameter-varying (LPV) feedforward parameterization and a corresponding data-driven estimation method in which the dependency of the coefficients on the scheduling signal are learned by a neural network. The use of a neural network enables the parameterization to compensate a wide class of constant relative degree LPV systems. Efficient optimization of the neural-network-based controller is achieved through a Levenberg-Marquardt approach with analytic gradients and a pseudolinear approach generalizing Sanathanan-Koerner to the LPV case. The performance of the developed feedforward learning method is validated in a simulation study of an LPV system showing excellent performance

Technical developments for quantitative and motion resolved MR-guided radiotherapy

Radiotherapy is a non-invasive therapeutic modality that uses highly targeted radiation to treat cancer. The success of the treatment is to a large extent related to the precision of the radiation delivery. The precision of the radiation delivery depends, among others, on the visualization of the cancer before and during the radiation delivery. Traditionally the cancer is visualized with X-ray type of imaging techniques such as computed tomography (CT), which does not always provide sufficient image quality to precisely localize the cancer. To improve the visualization, radiotherapy departments are now more frequently using magnetic resonance imaging (MRI) to localize the cancer before the treatment, on diagnostic MRI systems, and during radiation delivery on hybrid MR-linac systems. The MRI scans provided by these systems are typically derived from radiology and are not necessarily tailored towards radiotherapy. In this thesis I present works on the design and implementation of MR scans that are optimized for specific radiotherapy applications, such as imaging in the presence of system hardware imperfections, high quality MR imaging while the patient is breathing and real-time estimation of the motion of moving tumors during irradiation. These dedicated MRI scans could potentially reduce motion-related imprecision of the radiation delivery while simultaneously providing improved integration and therefore acceleration of the radiotherapy workflow. The further development and integration of these new MRI scans could be important for the long term clinical impact of the use of MRI in radiotherapy

Enhancing feedforward controller tuning via instrumental variables: with application to nanopositioning

\u3cp\u3eFeedforward control enables high performance of a motion system. Recently, algorithms have been proposed that eliminate bias errors in tuning the parameters of a feedforward controller. The aim of this paper is to develop a new algorithm that combines unbiased parameter estimates with optimal accuracy in terms of variance. A simulation study is presented to illustrate the poor accuracy properties of pre-existing algorithms compared to the proposed approach. Experimental results obtained on an industrial nanopositioning system confirm the practical relevance of the proposed method.\u3c/p\u3

Unifying model-based and neural network feedforward: Physics-guided neural networks with linear autoregressive dynamics

Unknown nonlinear dynamics often limit the tracking performance of feedforward control. The aim of this paper is to develop a feedforward control framework that can compensate these unknown nonlinear dynamics using universal function approximators. The feedforward controller is parametrized as a parallel combination of a physics-based model and a neural network, where both share the same linear autoregressive (AR) dynamics. This parametrization allows for efficient output-error optimization through Sanathanan-Koerner (SK) iterations. Within each SK-iteration, the output of the neural network is penalized in the subspace of the physicsbased model through orthogonal projection-based regularization, such that the neural network captures only the unmodelled dynamics, resulting in interpretable models