Adaptive quantile control for stochastic system
Journal article, 2021

Adaptive control has been successfully developed in deriving control law for stochastic systems with unknown parameters. The generation of reasonable control law depends on accurate parameter estimation. Recursive least square is widely used to estimate unknown parameters for stochastic systems; however, this approach only fits systems with Gaussian noises. In this paper, the adaptive quantile control is first proposed to cover the case where stochastic system noise follows sharp and thick tail distribution rather than Gaussian distribution. In the proposed approach, the system noise is modeled by the Asymmetric Laplace Distribution, and the unknown parameter is online estimated by our developed Bayesian quantile sum estimator, which combines recursive quantile estimations weighted by Bayesian posterior probabilities. With the real-time estimated parameter, the adaptive quantile control law is constructed based on the certainty equivalence principle. Our proposed estimator and controller are not computationally consuming and can be easily conducted in the Micro Controller Unit to fit practical applications. The comparison with some dominant controllers for the unknown stochastic system is conducted to verify the effectiveness of the adaptive quantile control.

Asymmetric Laplace Distribution

adaptive quantile control

certainty equivalence principle

Bayesian quantile sum estimator


Xuehui Ma

Xi'an University of Technology

Fucai Qian

Xi'an University of Technology

Shiliang Zhang

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Li Wu

Xi'an University of Technology

ISA Transactions

0019-0578 (ISSN)

Vol. In Press

Areas of Advance

Information and Communication Technology

Subject Categories

Control Engineering



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