Improving trajectory tracking by feedforward evolutionary multivariable constrained optimization

Improvements in positioning accuracy and reduction of trajectory tracking error in robotic systems require advanced control laws; these should take into account also multivariable concurrent specifications and be able to handle inputs and state constraints. In this work these requirements are considered exploiting a feedforward model-based predictive controller in which the control law is planned online on the basis of a multiobjective cost function, the minimization of which is executed by means of an evolutionary algorithm. The proposed scheme has been applied to an existing robust feedback control scheme, to achieve a more accurate trajectory tracking of the tip of a flexible link. Real time execution is possible; moreover, notwithstanding the stochastic inference engine of the evolutionary algorithms, the proposed scheme is sufficiently reliable, since it reveals a high degree of repeatability of the control signals.