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Filtering-Based Concurrent Learning Adaptive Control: Exponential Convergence of System Parameters and Control Coefficient | IEEE Conference Publication | IEEE Xplore

Filtering-Based Concurrent Learning Adaptive Control: Exponential Convergence of System Parameters and Control Coefficient


Abstract:

This paper presents a filtering-based concurrent learning (FCL) adaptive control technique for a class of nonlinear systems with parameter uncertainties and unknown contr...Show More

Abstract:

This paper presents a filtering-based concurrent learning (FCL) adaptive control technique for a class of nonlinear systems with parameter uncertainties and unknown control coefficient. The proposed FCL builds on the baseline CL, which was created to achieve exponential convergence of either system parameters or control coefficient while relying on the estimation of state derivatives using numerical methods. To extend CL, the key point is to define an adaptation mechanism that records the filtered basis of the system instead of the original basis while there is no need for numerical methods to estimate the state derivatives. For this purpose, we first suggest the control law that uses the estimates of unknown parameters and state derivatives, and then show how to derive the FCL adaptation law. The main contribution of this paper is that system parameters, control coefficient, and tracking errors are all exponentially convergent which is ensured by using a Lyapunov argument. Simulations on four illustrative examples—an underwater vehicle, an inverted pendulum, the wing rock model, and a Segway—are finally carried out to validate the soundness of the proposed technique.
Date of Conference: 08-10 June 2022
Date Added to IEEE Xplore: 05 September 2022
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ISSN Information:

Conference Location: Atlanta, GA, USA

I. Introduction

Over the years, considerable research attention has been paid to adaptive control due to its ability to identify the un-known model parameters [1]–[7]. Slotine’s adaptive control methods [2], [3] are among the most common approaches, although they require persistent excitation (PE) condition for the system states due to the use of only instantaneous data for adaptation. To relax this condition and provide exponential convergence of both tracking and parameter errors, the concurrent learning (CL) approach was recently introduced in which recorded data is utilized alongside real-time data to derive the parameter adaptation [1].

References

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