Adaptive online parameter estimation algorithm of PEM fuel cells

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.Postprint (published version

Adaptive Input and Parameter Estimation with Application to Engine Torque Estimation

This paper presents two estimation methods for systems with unknown time-varying input dynamics. By defining auxiliary filtered variables, an invariant manifold is derived and used to drive the input estimator with only one tuning parameter. Exponential error convergence to a small compact set around theorigin can be proved. Robustness against noise is studied and compared with two well-known schemes. Moreover, when the input dynamics to be estimated are parameterized in a quasilinear form with unknown parameters, the proposed idea is further investigated to estimate the associated unknowntime-varying parameters. The algorithms are tested by considering the torque estimation of internal combustion engines (ICEs). Comparative simulation results based on a benchmark engine simulation model show satisfactory transient androbustness performance

Real-time adaptive parameter estimation for a polymer electrolyte membrane fuel cell

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIn 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 well-recognized 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 scenariosPeer ReviewedPostprint (author's final draft

Adaptive estimation of time-varying parameters with application to roto-magnet plant

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis 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 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 estimation performance than several other estimation algorithms. Finally, the proposed parameter estimation methods are verified by conducting experiments based on a roto-magnet plant.Peer ReviewedPostprint (author's final draft

Least Squares Based Adaptive Control and Extremum Seeking with Active Vehicle Safety System Applications

On-line parameter estimation is one of the two key components of a typical adaptive control scheme, beside the particular control law to be used. Gradient and recursive least squares (RLS) based parameter estimation algorithms are the most widely used ones among others. Adaptive control studies in the literature mostly utilize gradient based parameter estimators for convenience in nonlinear analysis and Lyapunov analysis based constructive design. However, simulations and real-time experiments reveal that, compared to gradient based parameter estimators, RLS based parameter estimators, with proper selection of design parameters, exhibit better transient performance from the aspects of speed of convergence and robustness to measurement noise. One reason for the control theory researchers' preference of gradient algorithms to RLS ones is that there does not exist a well-established stability and convergence analysis framework for adaptive control schemes involving RLS based parameter estimation. Having this fact as one of the motivators, this thesis is on systematic design, formal stability and convergence analysis, and comparative numerical analysis of RLS parameter estimation based adaptive control schemes and extension of the same framework to adaptive extremum seeking, viz. adaptive search for (local) extremum points of a certain field. Extremum seeking designs apply to (i) finding locations of physical signal sources, (ii) minimum or maximum points of (vector) cost or potential functions for optimization, (iii) calculating optimal control parameters within a feedback control design. In this thesis, firstly, gradient and RLS based on-line parameter estimation schemes are comparatively analysed and a literature review on RLS estimation based adaptive control is provided. The comparative analysis is supported with a set of simulation examples exhibiting transient performance characteristics of RLS based parameter estimators, noting absence of such a detailed comparison study in the literature. The existing literature on RLS based adaptive control mostly follows the indirect adaptive control approach as opposed to the direct one, because of the difficulty in integrating an RLS based adaptive law within the direct approaches starting with a certain Lyapunov-like cost function to be driven to (a neighborhood) of zero. A formal constructive analysis framework for integration of RLS based estimation to direct adaptive control is proposed following the typical steps for gradient adaptive law based direct model reference adaptive control, but constructing a new Lyapunov-like function for the analysis. After illustration of the improved performance with RLS adaptive law via some simple numerical examples, the proposed RLS parameter estimation based direct adaptive control scheme is successfully applied to vehicle antilock braking system control and adaptive cruise control. The performance of the proposed scheme is numerically analysed and verified via Matlab/Simulink and CarSim based simulation tests. Similar to the direct adaptive control works, the extremum seeking approaches proposed in the literature commonly use gradient/Newton based search algorithms. As an alternative to these search algorithms, this thesis studies RLS based on-line estimation in extremum seeking aiming to enhance the transient performance compared to the existing gradient based extremum seeking. The proposed RLS estimation based extremum seeking approach is applied to active vehicle safety system control problems, including antilock braking system control and traction control, supported by Matlab/Simulink and CarSim based simulation results demonstrating the effectiveness of the proposed approach

Adaptive Output Feedback Control of Nonlinear Systems

Adaptive output feedback control of classes of nonlinear systems and related problems are investigated. The classes of systems that are studied include Lipschitz nonlinear systems, large-scale interconnected nonlinear systems with quadratically bounded interconnections, nonlinear systems containing product terms of unmeasured states and unknown parameters, and mechanical systems with unknown time-varying parameters and disturbances. Solutions and their bounds of relevant algebraic and differential matrix equations in systems and control theory are also studied. For analysis and synthesis of controllers, methods from Lyapunov theory, Algebraic Riccati Equations (AREs), Linear Matrix Inequalities (LMIs), and local polynomial approximations are extensively used. Findings and Conclusions: A stable output feedback controller can be designed for Lipschitz nonlinear systems if sufficient conditions related to distances to uncontrollability and unobservability of pairs of system matrices are satisfied. Stable linear decentralized output feedback controllers can be designed for large-scale systems if certain sufficient conditions are satisfied; these conditions can be formulated either as existence of positive definite solutions to AREs or as a feasibility problem of an LMI. By casting the dynamics of a nonlinear system, which contains products of unmeasurable states and unknown parameters, into a modified form, a stable adaptive output feedback controller can be constructed using a parameter dependent Lyapunov function; the procedure for casting the system dynamics into a modified form is constructive and is always possible. A stable adaptive controller for mechanical systems with unknown time-varying parameters and disturbances can be designed using local polynomial approximation; the time-varying parameters and disturbances are estimated by a modified least-squares algorithm using a new resetting strategy, which is a consequence of keeping the estimates continuous at the beginning of each time interval of local polynomial approximation. For all the problems that are investigated, simulation and experimental results are given to verify and validate the proposed methods.Department of Biochemistry and Molecular Biolog