Nonlinear state estimation for suspension control applications: a Takagi-Sugeno Kalman filtering approach

• Published in: Control engineering practice Vol. 61; pp. 292 - 306
• Main Authors: Pletschen, Nils, Diepold, Klaus J.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2017
• Subjects: Active suspensions; Kalman filter; Semi-active dampers; State estimation; Takagi-Sugeno model; Vehicle dynamics; Takagi-Sugeno model; Semi-active dampers; State estimation; Vehicle dynamics; Kalman filter; Active suspensions
• ISSN: 0967-0661; 1873-6939
• DOI: 10.1016/j.conengprac.2016.05.013

A new nonlinear state estimation approach, which combines classical Kalman filter theory and Takagi-Sugeno (TS) modeling, is proposed in this paper. To ensure convergence of the TS observer, conditions are derived that explicitly account for the TS model's confined region of validity. Thereby, the secured domain of attraction (DA) of the TS error dynamics is maximized within given bounds. The TS Kalman filtering concept is then applied to a hybrid vehicle suspension configuration, whose nonlinear dynamics are exactly represented by a continuous-time TS system. The benefit of the novel estimation technique is analyzed in comparison with the well-known EKF and UKF variants in simulations and experiments of a passive and an actively controlled suspension configuration in a quarter-car set-up. Employing a real road profile as disturbance input, the TS Kalman filter shows the highest estimation quality of the concepts studied. Moreover, as its computational complexity adds up to only one third of the one involved with the classical methods, the new approach operates remarkably efficient. •A new state estimation approach applicable for nonlinear systems is proposed.•The concept combines classical Kalman filter theory and Takagi-Sugeno (TS) modeling.•LMI conditions that consider the local validity of the TS error dynamics are derived.•The approach is applied to a controllable mechatronic vehicle suspension system.•The novel method shows superior estimation performance to EKF and UKF.