Advanced Motion Control for Precision Mechatronics: Control, Identification, and Learning of Complex Systems

Abstract

Manufacturing equipment and scientific instruments, including wafer scanners, printers, microscopes, and medical imaging scanners, require accurate and fast motions. An increase in such requirements necessitates enhanced control performance. The aim of this paper is to identify several challenges for advanced motion control originating from these increasing accuracy, speed, and cost requirements. For instance, flexible mechanics must be explicitly addressed through overactuation, oversensing, inferential control, and position-dependent control. This in turn requires suitable models of appropriate complexity. One of the main advantages of such motion systems is the fact that experimenting and collecting large amounts of accurate data is inexpensive, paving the way for identifying and learning of models and controllers from experimental data. Several ongoing developments are outlined that constitute part of an overall framework for control, identification, and learning of complex motion systems. In turn, this may pave the way for new mechatronic design principles, leading to fast lightweight machines where spatio-temporal flexible mechanics are explicitly compensated through advanced motion control.

Parameterizing robust manipulator controllers under approximate inverse dynamics: A double‐Youla approach

“…Proof. According to Theorem 1, the uncertain nonlinear dynamics (6)-(8) are conservatively covered by the uncertainty structure  Δ from (28). As by Theorem 2,  Δ corresponds to  Δ when the nominal controller K 0 is applied.…”

“…27 In addition, parameterizations of the dual form play a key role to reduce conservatism in the robust design of advanced motion control systems; see the work of Oomen. 28 The dual parameterization characterizes 8,22 in terms of a stable parameter system S all systems that are stabilized by one particular given controller.…”

“…By adopting the uncertainty structure (28), the separation of the two uncertainty sources is preserved because the summation of u, w M Δ , and w ψ Δ in (27) is captured by the signal interconnection in the generalized plant G, as depicted in Figure 4. Therefore, this structure is carried into the dual-Youla operator in the sequel.…”

“…Consider the system (1) with the AID controller (3) such that Assumptions 1 and 2 are fulfilled. According to Section 3.2, the resulting system dynamics is conservatively covered by the linear, stabilizable, and detectable plant (25) with a state-space realization (C1) and the uncertainty structure (28). Let a nominal linear controller be applied to the outer loop, with the pairs (A K , B K ) stabilizable and (A K , C K ) detectable.…”

“…To quantify the uncertainty in the closed loop bŷS, an expression for the corresponding dual-Youla parameter is derived as well. (25), (28) controlled by (37) is given as where A S,11 = B 13 D 0 C 31 + A 11 , Proof. The derivation is analogous to the full case of Theorem 2, replacing the coprime factors with those for a static controller.…”