T lOutput torque of the gearbox.

This paper presents an alternative adaptive parameter estimation framework for nonlinear systems with time-varying parameters. Unlike existing techniques that rely on the polynomial approximation of time-varying parameters, the proposed method can directly estimate the unknown time-varying parameters. Moreover, this paper proposes several new adaptive laws driven by the derived information of parameter estimation errors, which achieve faster convergence rate than conventional gradient descent algorithms. In particular, the exponential error convergence can be rigorously proved under the well-recognized persistent excitation (PE) condition. The robustness of the developed adaptive estimation schemes against bounded disturbances is also studied. Comparative simulation results reveal that the proposed approaches can achieve better performance than several conventional estimation algorithms. Finally, the proposed parameter estimation methods are verified by conducting experiments based on a roto-magnet plant.

In this paper we propose real-time adaptive parameter estimation methods for a polymer electrolyte membrane fuel cell (PEMFC) to facilitate the modeling and the subsequent control synthesis. Specifically, the electrochemical model of this fuel cell is in a nonlinearly parametric formulation. Hence, most of existing parameter estimation techniques for PEMFC mainly rely on the optimization approaches, requiring heavy computational costs or even offline implementation. In comparison to those methods, real-time adaptive parameter estimation methods for nonlinearly parametric system are developed in this paper. First, the nonlinearly parametric function is linearized by using the Taylor series expansion. Then, adaptive parameter estimation methods are proposed for estimating the constant or time-varying parameters of PEMFC. Different from the wellrecognized adaptive parameter estimation methods, the proposed adaptive laws are driven by the extracted estimation errors, so that exponential convergence of the parameter estimation error can be guaranteed, without using any predictors or observers. Finally, practical experiments in a H-100 PEMFC system are conducted, which illustrate satisfactory performances of the presented parameter estimation methods under different operation scenarios.

Since most of fuel cell models are generally nonlinearly parameterized functions, existing modeling techniques rely on the optimization approaches and impose heavy computational costs. In this paper, an adaptive online parameter estimation approach for PEM fuel cells is developed in order to directly estimate unknown parameters. The general framework of this approach is that the electrochemical model is first reformulated using Taylor series expansion. Then, one recently proposed adaptive parameter estimation method is further tailored to estimate the unknown parameters. In this method, the adaptive law is directly driven by the parameter estimation errors without using any predictors or observers. Moreover, parameter estimation errors can be guaranteed to achieve exponential convergence. Besides, the online validation of regressor matrix invertibility are avoided such that computation costs can be effectively reduced. Finally, comparative simulation results demonstrate that the proposed approach can achieve better performance than least square algorithm for estimating unknown parameters of fuel cells.