Abstract
This article presents an output feedback controller and observer design approach for linear time-invariant systems with unknown dynamics. The presented method uses an open-loop reference model to generate the desired trajectory and a closed-loop reference model as an observer. The controller only uses the observer states. Lyapunov-based stability proofs show that the error states converge asymptotically to zero and that all other signals are uniformly stable. Furthermore, bounds are proven on the transient behavior.
Highlights
In this paper, we present a novel control method for linear timeinvariant (LTI) systems with unknown dynamics
In this paper we introduce a new method for adaptive control of LTI systems with unknown dynamics
While the proof of Theorem 1 only holds for LTI systems, we tested the robustness of the system by performing two changes in system parameters
Summary
We present a novel control method for linear timeinvariant (LTI) systems with unknown dynamics. One modification to MRAC known as closed-loop reference model adaptive control (CRM) (see, e.g., [10], [13], [14]) introduces a feedback structure in the reference model. This feedback introduces a new degree of freedom for tuning, and allows the reference model dynamics to change if the system is incapable of tracking the original dynamics [14, Ch. 3.2.2]. Another recent modification to MRAC is presented in [11], [12] In those works, the authors introduce a modification scheme through filtering for the reference model and the control action in order to achieve improved convergence of the estimation error. A simulation comparison with the CRM method is performed, where our proposed method achieves a lower integrated absolute error between the system output and the reference signal
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