Topic
System identification
About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.
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TL;DR: In this article, a non-certainty-equivalence-based adaptive attitude control algorithm is proposed to overcome the adverse effect of uncertain parameter effects on closed-loop performance.
Abstract: Virtually every existing adaptive attitude control solution is based on the certainty-equivalence principle, which permits the adaptive controller structure to be based upon the deterministic feedback control algorithm (controller design based on nominal system information without any inertia-parameter uncertainty) and to be used in conjunction with a suitable adaptive parameter-estimation algorithm. However, one of the main drawbacks of the certainty-equivalence-based adaptive control methodology is the arbitrary degradation in closed-loop performance due to the adaptation (parameter-estimation) process, which acts like a forcing disturbance (uncertain parameter effect) imposed onto the deterministic closed-loop control dynamics. In this paper, we significantly deviate from the classical certainty-equivalence-based adaptive control framework and develop, for the first time (to our best knowledge), a noncertainty-equivalent adaptive attitude control algorithm. This novel control design process eliminates the deleterious performance-degradation effects of the certainty-equivalence controller through the introduction of a stable attracting manifold into the adaptation process, such that the resulting closed-loop adaptive attitude control dynamics recover the deterministic (ideal) case of closed-loop attitude controller performance (i.e., no uncertain parameter effects). In addition to detailed derivations of the new controller design and a rigorous sketch of all the associated stability and attitude error convergence proofs, we present numerical simulation results that not only illustrate the various features of the new noncertainty-equivalent controller design methodology but also highlight the ensuing closed-loop-performance benefits when compared with the conventional certainty-equivalence-based adaptive control schemes.
141 citations
TL;DR: It is presented necessary and sufficient conditions which guarantee the existence of a coordinate change and output-dependent time scaling, such that in the new coordinates and with respect to the new time the system has linear error dynamics.
141 citations
TL;DR: In this tutorial very simple methods will be presented by which models for dynamic processes can be obtained that employ step responses of the process or responses to other nonperiodic testsignals or process responses to periodic signals.
141 citations
TL;DR: Results indicate that the proposed method can successfully be used for the estimation of the useful energy extracted from the system and the temperature rise in the stored water of solar domestic water heating (SDHW) systems with the minimum of input data.
140 citations
TL;DR: This paper studies the parameter estimation problem for the sine combination signals and periodic signals and presents the multi-innovation stochastic gradient parameter estimation method, derived by means of the trigonometric function expansion.
Abstract: The sine signals are widely used in signal processing, communication technology, system performance analysis and system identification. Many periodic signals can be transformed into the sum of different harmonic sine signals by using the Fourier expansion. This paper studies the parameter estimation problem for the sine combination signals and periodic signals. In order to perform the online parameter estimation, the stochastic gradient algorithm is derived according to the gradient optimization principle. On this basis, the multi-innovation stochastic gradient parameter estimation method is presented by expanding the scalar innovation into the innovation vector for the aim of improving the estimation accuracy. Moreover, in order to enhance the stabilization of the parameter estimation method, the recursive least squares algorithm is derived by means of the trigonometric function expansion. Finally, some simulation examples are provided to show and compare the performance of the proposed approaches.
140 citations