Tracking of linear time-varying systems using state-space least mean square

Author(s):  
M.B. Malik ◽  
R.A. Bhatti
1994 ◽  
Vol 116 (3) ◽  
pp. 456-473 ◽  
Author(s):  
Sunil K. Singh ◽  
Lin Shi

We investigate robust adaptive controller designs for interconnected systems when no exact knowledge about the structure of the nonlinear interconnections between various subsystems is available. In this study, we concentrate on several different types of systems. We deal with both linear time-invariant (LTI) and linear time-varying (LTV) systems with nonlinear interconnections. For LTI systems, we examine the following types of interconnections: • interconnections that are bounded by first order polynomials in state space; • slowly time varying interconnections; • interconnections bounded by higher-order polynomials in state-space together with input channel interconnections. For LTV systems we deal with interconnections bounded by first-order polynomials in state space. We show that the nature of the nonlinear interactions influences the adaptation laws. We use the direct method of Lyapunov for the design of adaptive controllers for tracking in such systems. We investigate issues such as stability, transient performance and steady-state errors, and derive quantitative estimates and analytical bounds for various different adaptive controllers. For time-varying systems, we analyze the effect of the time variations of parameters and interactions and propose a modified adaptive control scheme with better performance. Simulation results are presented to validate our conclusions. We also investigate these results experimentally on a two-link robot manipulator. Experimental results validate theoretical conclusions and demonstrate the usefulness of such robust adaptive controllers for high-speed motions in uncertain systems.


2019 ◽  
Vol 26 (3-4) ◽  
pp. 200-213 ◽  
Author(s):  
Hongbo Zheng ◽  
Hui Qin ◽  
Mingke Ren ◽  
Zhiyi Zhang

This paper proposes a new adaptive algorithm for the active vibration control of time-varying systems in the presence of broadband or narrowband disturbances. The new algorithm combines the conventional filtered-x least mean square algorithm with the recursive prediction error (RPE) algorithm after the gradient modification of the RPE algorithm. The modified RPE algorithm is used to estimate the model of the control path online. The well-known filtered-x least mean square (FxLMS) algorithm is effective for the uncertain or time-varying systems, and adopts an auxiliary white noise approach to estimate the model of the control path online. However, the auxiliary excitation will degrade the control performance to some extent. In the new algorithm, the auxiliary excitation is eliminated at the expense of a larger computational burden. The influence of the estimated finite impulse response series on the convergence is also discussed. A propulsion shafting model with the time-varying dynamics is established by frequency response function synthesis. Numerical simulation for the established model is presented to demonstrate the superior performance of the proposed algorithm as compared with the FxLMS algorithm.


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