Bibliografie

(eng): The paper presents an output-feedback model predictive control applied to the motion control of a dynamic model of a parallel kinematic machine. The controlled system is described by a stochastic linear discrete-time model with bounded disturbances. An approximate uniform Bayesian filter provides set state estimates. The choice of the specific point estimate from this set is a part of the optimization. The cost function includes penalties on the tracking error and the actuation effort respecting increments. Illustrative examples show the effectiveness of the proposed approach and provide a comparison with previous results.