High Gain Disturbance Observer-Based Control for Nonlinear Affine Systems

Abstract

This paper proposes a high gain disturbance observer based control approach for nonlinear systems. Compared with previous works, it can admit a much larger class of disturbances produced by nonlinear exo-systems. Our approach will follow the framework of the two-stage design procedure, which separates the disturbance observer design from the controller design. Under certain conditions, the observer error for the disturbances generated by nonlinear exo-systems globally and asymptotically converges on zero and the stability of the closed-loop system is guaranteed. An illustrative example of a two-link robot manipulator with limit cycles disturbance is presented to show the effectiveness of the proposed method.

Identifying the set of all admissible disturbances: discrete-time systems with perturbed gain matrix

“…In most control systems, the existence of disturbances has a remarkable probability. The influence of the physical environment on the systems leads to the emergence of these undesirable parameters [1][2][3][4][5], and references therein. In this direction, and in the control literature, there are many works and techniques to avoid the effects of disturbances, [6][7][8][9][10][11][12][13][14][15] and references therein.…”

Observer based robust neuro-adaptive control of non-square MIMO nonlinear systems with unknown dynamics

“…In Ref. 30, a high gain disturbance observer-based control was suggested for nonlinear affine systems with known dynamics. In Ref.…”

Neural-network-based composite disturbance rejection control for a distillation column

“…One efficient way of improving system performance in such cases is to introduce an effective disturbance estimation technique, which serves as a feed-forward compensation part for the disturbances into the controller besides the conventional feedback part (Li et al, 2014a,b). Disturbance observer (DOB)-based control method is a kind of robust control method and has been successfully and widely employed in industry because of its simplicity and powerful ability to compensate the disturbances, e.g., robotic systems (Su et al, 2014), hard disk drive systems (Lee et al, 2012), permanent-magnet synchronous motor (PMSM) systems (Li et al, 2013, 2012; Li and Liu, 2009), flight control systems (Chen, 2003; Wei et al, 2009; Guo and Wen, 2010), missiles systems (Li and Yang, 2013; Zhang et al, 2013), ball mill grinding circuits (Chen et al, 2009), vibration control systems (Li and Qiu et al, 2011), piezoelectric actuators (Sofla et al, 2012; Ghafarirad et al, 2014) and other systems (Back and Shim, 2008; Chen and Chen, 2010; Kim et al, 2010; Cheng et al, 2012; Yang et al, 2013), etc. It is well known that the inverse model of system is used in the design of conventional DOB, which is only available for systems whose inverse models exist (Shim and Joo, 2007).…”