Parametric and Nonparametric Identification and Robust Control of a Rotational/Translational Actuator

Abstract

Abstract-RTAC1 benchmark problem considers a nonlinear fourth-order dynamical system involving the nonlinear interaction of a translational oscillator and an eccentric rotational proof mass. This problem has been posed to investigate the utility of a rotational proof mass actuator for stabilizing translational motion. In order to implement any of the model-based controllers proposed in the literature, the values of model parameters are required which are generally difficult to determine rigorously. In this paper, an approach to the leastsquares estimation of system parameters is discussed and practically applied to the benchmark problem. Next, in order to design an H ∞ controller, the nonlinear system is modelled as a perturbed linear system using an effective identification scheme. Experimental results confirm that this approach can effectively condense the whole nonlinearities, uncertainties, and disturbances within the system into a favorable perturbation block. Finally, an effective mixed-sensitivity problem is developed for the system to satisfy all performance requirements as well as robust stability despite actuator saturation.

A Review on the Control of Second Order Underactuated Mechanical Systems

“…TORA. The TORA (Translational Oscillator with Rotational Actuator) inFigure 6consists of a translational oscillating platform with mass 1 , that is controlled via a rotational eccentric mass 2[50][51][52][53][54].…”

RobustH∞feedback compensator design for an electromechanical actuator with time delay

“…Here, W s ð Þ is the uncertainty weighting function and Δ is an operator with infinity-norm less than unity (Tavakoli, Taghirad, and Abrishamchian 2005), that is ||Δ(s) ∞ || < 1, so…”

Identification and robust controller design for an electromechanical actuator with time delay

“…By plotting the frequency response of GMHD jv ð ÞÀGnom jv ð Þ Gnom jv ð Þ , the multiplicative uncertainty weighting function may be estimated (Tavakoli et al, 2005). Uncertainty and weighting function models of the studied system are illustrated in Figure 4.…”

“…where W s ð Þ is the uncertainty weighting function, and D is an operator with infinity-norm less than unity (Tavakoli et al, 2005)…”