Acceleration feedback via an algebraic state estimation method

In many mechanical systems, only accelerations are available for feedback purposes. For example, certain aerospace, positioning systems and force-position controllers in robotic systems, use accelerometers as the only sensing device. This paper presents initial steps towards an algebraic approach for the state estimation based feedback control problem in systems where the highest order derivative of the controlled variable is available. An illustrative case is presented dealing with the trajectory tracking problem for a second order position system on which only the acceleration is available for measurement. Based on an algebraic approach, an on-line algebraic estimator is developed for the unmeasured position and velocity variables. The obtained expressions depend solely on iterated integrals of the measured acceleration output and of the control input. The approach is robust to noisy measurement and it has the advantage to provide fast, on-line, non-asymptotic state estimations in the form of formulae requiring only the input and the output of the system. Based on these estimations, a linear feedback control law including estimated position error integrals is designed illustrating the possibilities of acceleration feedback via algebraic state estimation.

[1]  A. Levant Robust exact differentiation via sliding mode technique , 1998 .

[2]  Petr Mandl,et al.  Numerical differentiation and parameter estimation in higher-order linear stochastic systems , 1996, IEEE Trans. Autom. Control..

[3]  Cédric Join,et al.  Numerical differentiation with annihilators in noisy environment , 2009, Numerical Algorithms.

[4]  S. Diop,et al.  A numerical procedure for filtering and efficient high-order signal differentiation , 2004 .

[5]  M. Fliess,et al.  Correcteurs proportionnels-intégraux généralisés , 2002 .

[6]  Tzyh Jong Tarn,et al.  On robust impact control via positive acceleration feedback for robot manipulators , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[7]  Cédric Join,et al.  Algebraic change-point detection , 2009, Applicable Algebra in Engineering, Communication and Computing.

[8]  Vicente Feliú Batlle,et al.  Nonlinear Control for Magnetic Levitation Systems Based on Fast Online Algebraic Identification of the Input Gain , 2011, IEEE Transactions on Control Systems Technology.

[9]  Michel Fliess,et al.  Parameters estimation of systems with delayed and structured entries , 2009, Autom..

[10]  Wilfrid Perruquetti,et al.  An algebraic approach for human posture estimation in the sagittal plane using accelerometer noisy signal , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[11]  Faryar Jabbari,et al.  H∞ Control for Seismic-Excited Buildings with Acceleration Feedback , 1995 .

[12]  Daniel J. Inman,et al.  Vibration with Control: Inman/Vibration with Control , 2006 .

[13]  Hebertt Sira-Ramírez,et al.  Closed-loop parametric identification for continuous-time linear systems via new algebraic techniques , 2007 .

[14]  D. Inman Vibration control , 2018, Advanced Applications in Acoustics, Noise and Vibration.

[15]  Mamadou Mboup,et al.  Différenciation numérique multivariable I : estimateurs algébriques et structure , 2010 .

[16]  Romain Delpoux,et al.  On-line parameter estimation of a magnetic bearing , 2011, 2011 19th Mediterranean Conference on Control & Automation (MED).

[17]  Salim Ibrir,et al.  Linear time-derivative trackers , 2004, Autom..

[18]  M. Fliess,et al.  Flatness and defect of non-linear systems: introductory theory and examples , 1995 .

[19]  Hebertt Sira-Ramírez,et al.  A comparison between the algebraic and the reduced order observer approaches for on-line load torque estimation in a unit power factor rectifier-DC motor system , 2012 .

[20]  H. Sira-Ram A COMPARISON BETWEEN THE ALGEBRAIC AND THE REDUCED ORDER OBSERVER APPROACHES FOR ON-LINE LOAD TORQUE ESTIMATION IN A UNIT POWER FACTOR RECTIFIER-DC MOTOR SYSTEM , 2012 .

[21]  M. Fliess,et al.  An algebraic framework for linear identification , 2003 .

[22]  M. Fliess,et al.  Nonlinear observability, identifiability, and persistent trajectories , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[23]  M. Mboup Parameter estimation for signals described by differential equations , 2009 .

[24]  M. Fliess,et al.  CORRECTEURS PROPORTIONNELS-INT EGRAUX G EN ERALIS ES , 2002 .

[25]  Christophe Pouzat,et al.  An Algebraic Method for Eye Blink Artifacts Detection in Single Channel EEG Recordings , 2010 .