Adaptive Dual Control of Discrete‐Time Lqg Problems with Unknown‐But‐Bounded Parameter

Qian, Fucai; Huang, Jiaoru; Liu, Ding; Hu, Shaolin

doi:10.1002/asjc.942

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…The main idea of the dual control is to make a correct balance between the control action and the probing action, that is, on the one hand, it drives the system to the desired output, on the other hand, it learns the unknown parameters actively and quickly to reduce the uncertainty for future control improvement. [14][15][16][17][18][19][20] However, the essence of dual control is an optimization of an overall stage. Due to the existence of unknown parameters in the system, the dual controller cannot be solved analytically, and it requires extensive numerical computations even for only very simple examples.…”

Section: Introductionmentioning

confidence: 99%

Dual fault‐tolerant control for a class of stochastic systems with partial loss‐of‐control effectiveness

Liu

Xie

Qian

et al. 2021

Intl J Robust & Nonlinear

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Loss-of-control effectiveness is a common fault in the engineering field. In the existing adaptive fault-tolerant control algorithms, observers and Lyapunov functions are often used to estimate fault parameters. However, they can only ensure the convergence of fault parameters as time goes to infinity. This leads to the fact that the controller can only play a better fault-tolerant role in the steady-state process, and cannot guarantee the system performance in the transient state. Therefore, a dual fault-tolerant control (DFTC) algorithm is proposed for a class of stochastic systems with partial loss-of-control effectiveness. The algorithm introduces a fault-tolerant objective on the basis of the control objective. By establishing a new objective function, the controller is forced to have the characteristics of active learning to improve the transient performance of the fault system. An example simulation verifies the validity of DFTC. The simulation results show that DFTC can drive the system to the desired output, especially when partial loss-of-control efficiency occurs in the system, it can learn the unknown parameters actively, quickly, and accurately, which is significantly better than the conventional control based on the certainty equivalence principle and improves the reliability of the system.

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Section: Introductionmentioning

confidence: 99%

Dual fault‐tolerant control for a class of stochastic systems with partial loss‐of‐control effectiveness

Liu

Xie

Qian

et al. 2021

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this paper, a soft switching among multiple models is realized by means of the dual adaptive control method. Since Feldbaum first proposed the dual control method for the autoregressive moving average (ARMA) model with unknown parameters, after more than half a century of research, it has been proved that dual control can optimize the system toward the desired target on one hand, and on the other hand, it can actively collect information and guarantee the unknown parameter estimation process [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Multi-Model Reliable Control for Variable Fault Systems under LQG Framework

et al. 2019

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The problem of reliable control for variable fault systems under linear quadratic Gaussian (LQG) framework is studied in this paper. Firstly, a cluster of models is used to cover the dynamic behaviors of different fault modes of a system and, for each model, LQG control is implemented. By using the a posteriori probability of model innovation as the weight information, a multi-model reliable control (MMRC) is proposed. Secondly, it is proved that MMRC can enable the controller to learn the real operating mode of the system. When the controller is in a deadlock state, a deadlock avoidance strategy is given and its convergence of the a posteriori probability is proved. Finally, the validity of MMRC is verified by an example simulation of the lateral-directional control system of an aircraft. Simulation results show that MMRC guarantees an acceptable performance of the closed-loop system. In addition, since the controller fuses the control law of each model according to the weight information, when the system model is switched, the controller implements a soft switching, which avoids the jitter caused by frequent hard switching to the system.

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“…Besides uncertainties of external interference, there are uncertainties in internal parameters in industrial systems . For the latter uncertainties, there are different cases in some specific systems.…”

Section: Introductionmentioning

confidence: 99%

A Suboptimal Dual Control Method for the Stochastic Systems with Parameters Drifting

Yang

Gao

Qian

et al. 2018

Asian Journal of Control

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This paper proposed a suboptimal dual control method for the stochastic systems with parameters drifting. Based on the consideration of the performance index control and the parameter estimation, the minimum variance of the system output and the estimated covariance matrix of the parameters estimation are both put into the performance index to evaluate the control quality. Furthermore, a dual control strategy is designed which is of the property of learning and control. Simulation results illustrate the effectiveness of the algorithm.

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Adaptive Dual Control of Discrete‐Time Lqg Problems with Unknown‐But‐Bounded Parameter

Cited by 7 publications

References 17 publications

Dual fault‐tolerant control for a class of stochastic systems with partial loss‐of‐control effectiveness

Dual fault‐tolerant control for a class of stochastic systems with partial loss‐of‐control effectiveness

Multi-Model Reliable Control for Variable Fault Systems under LQG Framework

A Suboptimal Dual Control Method for the Stochastic Systems with Parameters Drifting

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