Nonlinear robust observer design using an invariant manifold approach. (October 2016)

• Record Type: Journal Article
• Authors: Khan, Irfan Ullah; Wagg, David; Sims, Neil D.
• Is Part Of: Control engineering practice. Volume 55(2016)
• Page Start: 69
• Page End: 79
• Publication Date: 2016-10
• Subjects: Observer design -- Invariant manifold -- Lipschitz non-linearity -- Error dynamics -- Mapping functions; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2016.06.015
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: This paper presents a method to design a reduced order observer using an invariant manifold approach. The main advantages of this method are that it enables a systematic design approach, and (unlike most nonlinear observer design methods), it can be generalized over a larger class of nonlinear systems. The method uses specific mapping functions in a way that minimizes the error dynamics close to zero. Another important aspect is the robustness property which is due to the manifold attractivity: an important feature when an observer is used in a closed loop control system. A two degree-of-freedom system is used as an example. The observer design is validated using numerical simulation. Then experimental validation is carried out using hardware-in-the-loop testing. The proposed observer is then compared with a very well known nonlinear observer based on the off-line solution of the Riccati equation for systems with Lipschitz type nonlinearity. In all cases, the performance of the proposed observer is shown to be very high. Abstract : Highlights: A method to design a reduced order observer using an invariant manifold is proposed. The method uses specific mapping functions that minimizes the error dynamics close to zero. Another important aspect is the robustness property due to the manifold attractivity. The observer design is validated using both numerical simulation and hardware-in-loop testing. The proposed observer is then compared with a very well-known nonlinear observer.