Design of an Optimal Unknown Input Observer for Load Compensation in Motion Systems

Abstract

Recently, several model-based control designs have been proposed for motion systems and computerized numerical control (CNC) machines to improve motion accuracy. However, in real applications, their performance is seriously degraded when significant disturbances or cutting forces are applied. In this paper, we derive straightforward design procedures for a generalstructured unknown input observer (UIO) which perfectly decouples the effect of the external disturbance from the state estimation. Furthermore, we derive the optimal UIO by minimizing the estimation errors for both the state and the disturbance via the Riccati equation. Experimental results show that the performance of all advanced motion controllers suffers when external loads are applied. By compensating for the disturbance of a servo motor using the proposed optimal-UIO, the original contouring accuracy, which is degraded by the external loading, can be successfully recovered.

Design and analysis of a modular learning based cross-coupled control algorithm for multi-axis precision positioning systems

“…The peak (allowable) input value (10V) is applied for one time sample (30ms) emulating an impulse input while the response is recorded. By using this experimental data, and correlating the results with (5), system characteristic parameters ζ and ω n are found as 1.1 and 150rad/s respectively. The time delay,τ, is also estimated as 0.015s by observing the closed loop step response for position loop and the controller output.…”

“…In order to find the DC gain, G DC , open loop velocity (i.e., X(s)) step response of the plant can be used since there is no free integrator in the transfer function relating the velocity of slider to the applied voltage. In (5), the time domain response solution, c(t), for an over-damped (ζ > 1) unity gain second order system is given when the input function is the impulse function as reported in many sources such as [24].…”

“…Although almost all systems employ feedback control, considerable improvement in tracking accuracy can be achieved by addition of feedforward control to the algorithm. Several feedforward control approaches are developed in literature to improve tracking accuracy such as zero phase error tracking control (ZPETC) [4][5][6], feedforward friction compensation [7,8] and iterative learning control (ILC) [9][10][11]. Performance of a ZPETC system is sensitive to variations in plant parameters and modeling errors since it is based on pole/zero and phase cancellations [4].…”

A design method for unknown input observer for non-minimum phase systems

“…Initially, the unknown input observer is examined by Kudva, Viswanadham and Ramakrishna 6 . Since then, several papers have been published to design unknown input observers [7][8][9] . According to these papers, the unknown input observer for the plant ( can be designed if and only if following 2 expressions hold true: (1) ) 0 , , , C B A B CB rank rank and (2) the plant has no invariant zero in the closed right half plane, that is, the plant ( is of minimum-phase.…”

Learning Based Cross-Coupled Control for Multi-Axis High Precision Positioning Systems

“…Although almost all systems employ feedback as a part of tracking control, substantial improvement of tracking accuracy is achieved by the addition of feed forward control methods. In literature, several feed forward control schemes have been shown to improve tracking accuracy such as zero phase error tracking control (ZPETC) [4][5][6], feed forward friction compensation [7,8] and iterative learning control (ILC) [9,10]. According to Tomizuka [4], tracking performance of a ZPETC system is sensitive to variations in plant parameters and modeling errors since ZPETC design is based on pole/zero cancellation and phase cancellation.…”