The Effect of Computational Delay on Performance of Adaptive Control Systems

Author(s):  
Pawel Konrad Orzechowski ◽  
Tsu-Chin Tsao ◽  
James Steve Gibson

In many adaptive control applications, especially where the recursive-least-squares (RLS) algorithms are used, the real-time implementation of high order adaptive filters for estimating the disturbance dynamics is computationally intensive. The delay associated with the computational burden is usually either underestimated as no delay or overestimated as one sample delay in the control system design and analysis. For a stochastic disturbance dynamics, the H2 optimal control performance for the case of one-step delay is worse than that of no delay due to the nonminimum phase plant zero introduced by the delay. The optimal performance for a fractional delay is bounded between these two extremes. The paper investigates the effect of the fractional computational delay on a variable order adaptive controller based on a recursive least-squares adaptive lattice filter. The trade-off between the adaptive filter order and the computational delay is analyzed and evaluated by an example.

2021 ◽  
pp. 107754632110191
Author(s):  
Fereidoun Amini ◽  
Elham Aghabarari

An online parameter estimation is important along with the adaptive control, that is, a time-dependent plant. This study uses both online identification and the simple adaptive control algorithm with velocity feedback. The recursive least squares method was used to identify the stiffness and damping parameters of the structure’s stories. Identification was carried out online without initial estimation and only by measuring the structural responses. The limited information regarding sensor measurements, parameter convergence, and the effects of the covariance matrix is examined. The integration of the applied online identification, the appropriate reference model selection in simple adaptive control, and adopting the proportional integral filter was used to limit the structural control response error. Some numerical examples are simulated to verify the ability of the proposed approach. Despite the limited information, the results show that the simultaneous use of online identification with the recursive least squares method and simple adaptive control algorithm improved the overall structural performance.


Author(s):  
K Warwick ◽  
Y-H Kang

A self-tuning proportional, integral and derivative control scheme based on genetic algorithms (GAs) is proposed and applied to the control of a real industrial plant. This paper explores the improvement in the parameter estimator, which is an essential part of an adaptive controller, through the hybridization of recursive least-squares algorithms by making use of GAs and the possibility of the application of GAs to the control of industrial processes. Both the simulation results and the experiments on a real plant show that the proposed scheme can be applied effectively.


2007 ◽  
Vol 359-360 ◽  
pp. 528-532 ◽  
Author(s):  
Jing Kang ◽  
Chang Jian Feng ◽  
Hong Ying Hu ◽  
Qiang Shao

In order to improve form cutter accuracy in grinding process, a closed-loop control system was designed to accomplish adaptive control of constant force in grinding process. Since it is a complicated dynamic process with severe nonlinear and much stochastic disturbance, fuzzy adaptive controller was used which can adjust parameters online. By measuring grinding force, characteristic information of grinding process was acquired. Regulation factor of feed rate is determined by grinding force ratio, and force deviation and its change rate are used as evaluation indexes. Thus, adaptive control of constant force in grinding process is accomplished. Simulation and tool grinding test indicate that the system has high precision and stability, and reduces cutter error efficiently.


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