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Journal ArticleDOI

Adaptive Control Based on Explicit Criterion Minimization

Lennart Ljung, +1 more
- 01 Aug 1981 - 
- Vol. 14, Iss: 2, pp 881-886
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TLDR
An approach to adaptive control based on direct minimization of a posed performance criterion with respect to the regulator parameters is described using a stochastic approximation technique and it is shown how the instrumental variable technique can provide such information.
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This article is published in IFAC Proceedings Volumes.The article was published on 1981-08-01. It has received 30 citations till now. The article focuses on the topics: Adaptive control & Stochastic approximation.

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Citations
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Minimax approaches to robust model predictive control

TL;DR: It is shown how tools from modern nonlinear control theory can be used to synthesize finite horizon MPC controllers with guaranteed stability, and more importantly, how some of the tech- nical assumptions in the literature can be dispensed with by using a slightly more complex controller.
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Paper: Self-tuning control of nonminimum-phase systems

TL;DR: The paper describes the practical features that a self-tuning controller must possess and discusses how plant dead-time and excess continuous-time poles can lead to discrete-time nonminimum-phase zeros.
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Adaptive Forgetting in Recursive Identification through Multiple Models

TL;DR: A new recursive identification method, Adaptive forgetting through Multiple Models — AFMM, is presented and evaluated using computer simulations, indicating that AFMM is able to track rapidly changing parameters well, and that the method is robust in several respects.
Journal ArticleDOI

Paper: Adaptive control based on explicit criterion minimization

TL;DR: This work discusses how objective functions can be minimized on-line, thus providing adaptive control, and shows that the self-tuning regulator is obtained as a special case, corresponding to a particular quadratic criterion and a particular way of estimating the system dynamics.
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Brief Direct iterative tuning via spectral analysis

TL;DR: A nonparametric frequency-domain scheme for tuning closed-loop systems that guarantees stability of the loop during the tuning procedure and uses the generalised stability margin and a metric on transfer functions to impose safe limits on the variation of the control law.
References
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Journal ArticleDOI

A Stochastic Approximation Method

TL;DR: In this article, a method for making successive experiments at levels x1, x2, ··· in such a way that xn will tend to θ in probability is presented.
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On self tuning regulators

TL;DR: In this paper, the problem of controlling a system with constant but unknown parameters is considered and an algorithm obtained by combining a least squares estimator with a minimum variance regulator computed from the estimated model is analyzed.
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Analysis of recursive stochastic algorithms

TL;DR: It is shown how a deterministic differential equation can be associated with the algorithm and examples of applications of the results to problems in identification and adaptive control.
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Self-tuning controller

TL;DR: In this paper, a cost function which incorporates system input, output and set-point variations is selected, and a control law for a known system is derived, and the control input is chosen to make the prediction zero.
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Paper: Theory and applications of self-tuning regulators

TL;DR: The regulator algorithms, their theory and industrial applications are reviewed and the major ideas are covered but detailed analysis is given elsewhere.