Faj Frank Boeren

Accuracy aspects in motion feedforward tuning

Feedforward control can significantly improve the performance of a motion system through compensation of known disturbances. Recently, new feedforward algorithms have been proposed that exploit measured data from previous tasks and a suitable feedforward parametrization to attain high performance. The aim of this paper is to analyze the accuracy of these approaches. To achieve this, related results from closed-loop identification are exploited in feedforward control. Furthermore, a new algorithm is proposed that leads to optimal accuracy. The results are confirmed in a simulation study of a motion system.

Iterative feedforward control: a closed-loop identification problem and a solution

Feedforward control can significantly improve the performance of a system through compensation of disturbances. By exploiting measured data from previous tasks and a suitable feedforward parametrization, iterative feedforward control simultaneously attains high performance and good extrapolability of tasks. This paper aims to show that earlier contributions in this area suffer from a closed-loop identification problem. A novel solution is presented based on closed-loop identification techniques, which shows that existing feedforward control algorithms can be significantly enhanced. A simulation example confirms the existence of a closed-loop identification problem in earlier approaches and shows that the proposed solution is superior compared to pre-existing results.

Optimal estimation of rational feedforward controllers: An instrumental variable approach

Iterative control enables a significant control performance enhancement by learning feedforward command signals from previous tasks in a batch-to-batch fashion. The aim of this paper is to develop an approach to estimate the parameters of rational feedforward controllers that provide high performance and extrapolation capabilities towards varying tasks. An instrumental variable-based algorithm is developed that leads to unbiased parameter estimates and optimal accuracy in terms of variance. The approach also enables optimal estimation of a feedforward controller using a polynomial basis. Simulation results confirm that optimal accuracy is obtained with the proposed approach.

Rational iterative feedforward tuning: Approaches, stable inversion, and experimental comparison

Feedforward control plays a key role in achieving high performance for industrial motion systems that perform non-repeating motion tasks. Recently, learning techniques have been proposed to further improve both performance and robustness to non-repeating tasks by using a rational feedforward basis. The aim of this paper is to propose a unifying framework which connects these approaches. Experimental results on an industrial motion system validate the approaches and illustrate benefits of rational feedforward tuning in motion systems, including pre- and post-actuation through stable inversion.

Enhancing performance through multivariable weighting function design in ℋ − loop-shaping: with application to a motion system

The quality of model-based controllers hinges on a careful specification of performance and robustness requirements. In typical norm-based control designs, these performance and robustness requirements are specified in a scalar optimization criterion, even for complex multivariable systems. This paper aims to develop a novel and systematic approach for the formulation of this optimization criterion for complex multivariable systems. Hereto, characteristics of the underlying system are exploited. In contrast to pre-existing approaches that typically lead to multiloop SISO weighting functions, the proposed approach enables the design of multivariable weighting functions. Experimental results confirm that the proposed procedure significantly improves the performance of an industrial motion system compared to earlier approaches.