Enhanced sensitivity shaping by data-based tuning of disturbance observer with non-binomial filter

Data-based tuning of bumpless feedforward for tracking of multi-phase trajectories with application to wire-bonding machine

To improve the settling time for high-speed point-to-point motion, a piecewise-model feedforward controller is introduced which utilizes multiple inverse sub-models with bumpless transfer between them. As the transfer function of this bumpless feedforward controller is non-commutative and non-invertible, a set of special perturbation and reference inputs are designed to extract the signals required for computing the gradient of the cost function. In this way, the optimal parameters within different motion phases are found within an integrated process. It is experimentally demonstrated in a wire-bonding machine that the root-mean-square of tracking error is improved by at least 15.3% in settling phase by the proposed data-based tuning method with bumpless feedforward controller, compared with existing tuning methods with uniform feedforward controller.

Application of optimization algorithms to adaptive motion control for repetitive process

The application of optimization algorithms to adaptive motion control is proposed in this paper. In order to provide optimal system response, optimization algorithm is used as adjustment mechanism of controller coefficients. Most of optimization algorithms are not able to work in continuous optimization mode and with non-constant search space (i.e. dataset). For this reason, the introduction of a novel approach, Adaptive Procedure for Optimization Algorithms (APOA), that allows to apply most of optimization algorithms to adaptation process seems to be necessary. Such an algorithm is innovative, due to its universality in terms of applied optimization algorithm. Its application allows to obtain optimal motion control in modern electric drives. The proposed APOA is presented together with the novel desired-response adaptive system (DRAS) approach for repetitive processes. This solution provides unchanged system response regardless of plant parameters variation or external disturbances. The developed adaptive approach based on optimization algorithm is implemented in permanent magnet synchronous motor (PMSM) drive to adapt state feedback speed controller during moment of inertia variations. Since the proposed DRAS is model-free approach, the adaptation procedure is immune to issues related to plant parameters mismatch. The obtained results prove that proposed adaptive speed controller for PMSM assures desired system response regardless of the moment of inertia variation.