Chaos Control of Atomic Force Microscope System Using Nonlinear Model Predictive Control

2016 ◽  
Vol 33 (3) ◽  
pp. 405-415 ◽  
Author(s):  
J. Keighobadi ◽  
J. Faraji ◽  
S. Rafatnia
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Chaos Control ◽  
Control Method ◽  
Nonlinear Model Predictive Control ◽  
Stable Range ◽  
Operational Conditions ◽  
Simulation Results ◽  
Micro Cantilever

AbstractOwing to robust and optimal specification, model predictive control method has received wide attentions over recent years. Since in certain operational conditions, an Atomic/scanning Force Microscope (AFM) shows chaos behavior, the chaos feedback control of the AFM system is considered. According to the nonlinear model of forces interacting between the tip of micro cantilever and the substrate of AFM; the nonlinear control methods are proposed. In the paper, the chaos control of a micro cantilever AFM based on the nonlinear model predictive control (NMPC) technique is presented. Through software simulation results, the effectiveness of the designed NMPC of the AFM is assessed. The simulation results together with analytical stability proofs indicate that the proposed method is effective in keeping the system in a stable range.

A Study on Nonlinear Model Predictive Control for Helicopter/Engine with Variable Rotor Speed Based on Linear Kalman Filter

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Yong Wang ◽  
Qiangang Zheng ◽  
Haibo Zhang ◽  
Yuan Gao
Keyword(s):  
Kalman Filter ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Torsional Vibration ◽  
Vibration Suppression ◽  
Control Method ◽  
Notch Filter ◽  
Nonlinear Model Predictive Control ◽  
Rotor Speed

Abstract A novel control method combining nonlinear model predictive control (NMPC) with linear kalman filter (LKF) and adaptive notch filter (ANF) for an integrated helicopter/engine system with variable rotor speed is proposed to enhance the response ability of turboshaft engine. Firstly, based on the integrated helicopter/engine model with variable rotor speed, an ANF combined with frequency estimation is introduced. Then, in order to estimate the significant and unmeasurable performance parameters utilized in model predictive control, such as temperature before turbine, a nonlinear model predictive controller based on LKF is presented. The simulation verifications demonstrate that the fundamental frequency of torsional vibration changes from 1.30 Hz to 2.70 Hz when the rotor speed varies continuously by 50 %. In this case, compared with the notch filter, all torsional vibration amplitudes are damped below 0.1 % by ANF. Meanwhile, in comparison with the PID controller, the NMPC can reduce the overshoot and droop of the power turbine speed to less than 1 % with steady-state error no more than 0.5 % at the off-design point of NMPC based on adaptive torsional vibration suppression. The applications of LKF and similar transformation improve the control accuracy and robustness performance of the NMPC designed at a single operating point.

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