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MESA (Mechatronics, Embedded Systems and Automation) LAB School of Engineering,

ME280, Fractional Order Mechanics FISP (Focused Independent Study Project). December 17, 2013. Fractional Order Model Nonlinear Predictive Control Using RIOTS_95 Tiebiao Zhao Instructor: Prof. YangQuan Chen. MESA (Mechatronics, Embedded Systems and Automation) LAB School of Engineering,

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MESA (Mechatronics, Embedded Systems and Automation) LAB School of Engineering,

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  1. ME280, Fractional Order Mechanics FISP (Focused Independent Study Project) December 17, 2013 Fractional Order Model Nonlinear Predictive Control Using RIOTS_95 Tiebiao Zhao Instructor: Prof. YangQuan Chen MESA (Mechatronics, Embedded Systems and Automation) LAB School of Engineering, University of California, Merced http://mechatronics.ucmerced.edu/

  2. Introduction • Background • Model Predictive Control • RIOTS_95 • Integer Order MPC using RIOTS_95 • Oustaloup Recursive Approximation • Luenberger Observer • Fractional Order MPC using RIOTS_95

  3. Model Predictive Control Philosophy • At time t, solve an optimal control problem over a finite horizon of N steps: • Only apply the first control output • At time t+1, Get new measurements, repeat the optimization. And so on… Advantage of on-line repeated optimization-Feedback

  4. Lee and Markus stated: One technique for obtaining a feedback controller from knowledge of open-loop controllers is to measure the current control process state and then compute very rapidly for the open-loop control function. After which a new measurement of the process state is made and a new open-loop control function is computed for this new measurement, the procedure is then repeated.

  5. RIOTS_95

  6. Control Scheme of MPC using RIOTS_95

  7. Comparison with MPC in Matlab Toolbox

  8. Oustaloup Recursive Approximation

  9. Example

  10. Luenberger Observer

  11. b0 = 1;a0 = 0.5;a1 = 1;alpha = 0.9;w_L=1e-4;w_H=alpha*1e4;N=1;[A,b,c,d] = ssdata(ora_foc(-alpha,N,w_L,w_H));Transfer = tf(b0/(a1/ss(A,b,c,0)+a0));[num den]=tfdata(Transfer,'v');sstf=tf(num,den);[a b c d]=ssdata(sstf);op=-3*ones(1,3);l=acker(a',c',op)';a1=a-l*c;b1=[b l];tsim=20;SIM_OPTIONS = simset('Solver', 'ode45');sim('ob_ex', tsim, SIM_OPTIONS) Example

  12. Plant of Fractional Order Plant of Integer Order Get System State MPC based on RIOTS_95 Controller Design

  13. RIOTS_95 MPC with Observer The order of state observer is not the same as the order of real system.

  14. Examples Order of state observer ≠ Order of real system Order of state observer＝ Order of real system SISO With step disturbance on output Order of state observer ≠ Order of real system MIMO With step disturbance on output Constraints on control output

  15. The orders of state observer and real system are the same System Output Variable of real system Variable of system observer Control Output

  16. The orders of state observer and real system are different

  17. MIMO with disturbances Setpoint:

  18. With control output constraints

  19. Large time delay (large dead time) State estimators under stochastic noise (Kalman Filter ) Application on the test bench TITO platform Future Work

  20. Credits Prof. YangQuan Chen Zhuo Li

  21. Thank you!

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