This paper proposes a variable forgetting factor QRD-based recursive least squares algorithm with bias compensation (VFF-QR-RLS-BC) for system identification with input noise. The new algorithm is based on the least s...
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ISBN:
(纸本)9781479953424
This paper proposes a variable forgetting factor QRD-based recursive least squares algorithm with bias compensation (VFF-QR-RLS-BC) for system identification with input noise. The new algorithm is based on the least square estimation with bias compensation framework and it employs a variable forgetting factor to improve the tracking speed and a QRD-based implementation for recursively solving the LS problem with bias compensation. Simulation results show that the proposed method can obtain improved convergence rate in sudden system change environment and satisfactory performance under stationary environment.
The traditional brushless DC motor PID Control of double closed loop has a long adjusting time, large overshoot and especially when the motor model parameters have changed, the motor's control system is unstable. ...
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The traditional brushless DC motor PID Control of double closed loop has a long adjusting time, large overshoot and especially when the motor model parameters have changed, the motor's control system is unstable. To solve these problems, we propose a speed regulation method based on self-tuning PID control. In the parameter estimator, we use input and output data of the system to identify the corresponding mathematical model according to the recursive least squares algorithm. The design mechanism mainly carries out the solution of the closed-loop feature polynomial, and adjusts the controller parameters adaptively according to the model parameters. Simulation and experimental results show that the proposed control algorithm can effectively overcome the effects of speed and load change, and has the characteristics of fast dynamic response, small overshoot and strong robustness.
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