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دانشگاه صنعتي اميركبير دانشكده مهندسي پزشكي استاد درس دكتر فرزاد توحيدخواه بهمن 1389

دانشگاه صنعتي اميركبير دانشكده مهندسي پزشكي استاد درس دكتر فرزاد توحيدخواه بهمن 1389. MPC Stability-2. کنترل پيش بين-دکتر توحيدخواه. Example 4.4 . Consider the system:. Δ t = 1, Q = C T C, and R = I; a = 0, N = 5 and Np = 140

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دانشگاه صنعتي اميركبير دانشكده مهندسي پزشكي استاد درس دكتر فرزاد توحيدخواه بهمن 1389

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  1. دانشگاه صنعتي اميركبير دانشكده مهندسي پزشكي استاد درس دكتر فرزاد توحيدخواه بهمن 1389 MPC Stability-2 کنترل پيشبين-دکتر توحيدخواه

  2. Example 4.4. Consider the system: Δt = 1, Q = CTC, and R = I; a = 0, N = 5 and Np = 140 Integrators are used in the design for disturbance rejection. Compute the solution using the long prediction horizon with exponential data weight α = 1.2, and modified Qα and Rα. Compare the eigen values and the gain matrices with DLQR solution. کنترل پيشبين-دکتر توحيدخواه

  3. Solution 1- DLQR solution Riccatimatrix P Then, the P is used to obtain matrices Qα and Rα, where γ = 1/α = 0.8333 کنترل پيشبين-دکتر توحيدخواه

  4. The new Qα and Rα matrices are used in the design of discrete-time model predictive control with exponential data weighting. Closed-loop eigen values (α = 1.2). Key: (1) from DLQR; (2) from DMPC کنترل پيشبين-دکتر توحيدخواه

  5. Parameters in the first row of the control gain matrix (α = 1.2). Key: (1) from DLQR; (2) from DMPC کنترل پيشبين-دکتر توحيدخواه

  6. Results are seen to be identical to each other; the condition number of the Hessian matrix is 475. In contrast, without exponential weighting, the condition number of the Hessian matrix is 44607, and the numerical solution is ill-conditioned. کنترل پيشبين-دکتر توحيدخواه

  7. Example 4.5. کنترل پيشبين-دکتر توحيدخواه

  8. Without constraints, illustrate the equivalence within one optimization window between LQR (scaled) and the exponentially weighted predictive control. In addition, show that when receding horizon control is applied, the closed-loop control systems are identical. کنترل پيشبين-دکتر توحيدخواه

  9. Solution. The feedback control gain vector K using DLQR program is: which is identical to the predictive control: کنترل پيشبين-دکتر توحيدخواه

  10. closed-loop state trajectory LQR control کنترل پيشبين-دکتر توحيدخواه

  11. Numerical results show identical results between the transformed variables in DLQR system and the predictive control system within one optimization window. Because predictive control uses the principle of receding horizon control, at j = 0 the first sample of the optimization window, the weight factor α0 is unity. Thus, the unconstrained control should be completely identical to the optimal LQR solution when receding horizon control is applied. کنترل پيشبين-دکتر توحيدخواه

  12. کنترل پيشبين-دکتر توحيدخواه

  13. کنترل پيشبين-دکتر توحيدخواه

  14. Discrete-time MPC with Prescribed Degree of Stability کنترل پيشبين-دکتر توحيدخواه

  15. For a SISO system: The closed-loop performance of the MPC is specified by Q and R matrices when a sufficiently large prediction horizon and a large N are used in the design. So often, we select Q = CTC to minimize the output errors, and R is used to tune the closed-loop response speed. For a MIMO system: time consuming to tune the closed-loop performance using the Q and R it may be desirable to have the closed-loop eigen values within a prescribed circle on the complex plane. کنترل پيشبين-دکتر توحيدخواه

  16. Design Objective کنترل پيشبين-دکتر توحيدخواه

  17. کنترل پيشبين-دکتر توحيدخواه

  18. کنترل پيشبين-دکتر توحيدخواه

  19. Computational Procedure کنترل پيشبين-دکتر توحيدخواه

  20. Computational Procedure (cont.) کنترل پيشبين-دکتر توحيدخواه

  21. Example 4.6. In Example 4.4, with identical design specifications except that the degree of stability λ is chosen to be 0.9, namely all closed-loop eigen values are specified to be within the circle of radius 0.9. Solution With the prescribed degree of stability λ = 0.9, and the weight matrices Q and R, together with the augmented state model (A,B) we solve the following steady-state algebraic Riccati equation to find P∞ کنترل پيشبين-دکتر توحيدخواه

  22. predictive control gain matrices. Matab کنترل پيشبين-دکتر توحيدخواه

  23. کنترل پيشبين-دکتر توحيدخواه

  24. The Relationship Between P∞and Jmin کنترل پيشبين-دکتر توحيدخواه

  25. optimal solution: minimum of the cost function: کنترل پيشبين-دکتر توحيدخواه

  26. Due to the numerical error, Jmin increases as Npincreases and is numerically unstable. Case A. Sufficiently large N is used the control trajectory will converge to the underlying optimal control trajectory defined by the discrete-time LQR cost function: کنترل پيشبين-دکتر توحيدخواه

  27. Minimum of this cost function, with optimal control: With exponential data weighting, in the predictive control, the cost function is: کنترل پيشبين-دکتر توحيدخواه

  28. کنترل پيشبين-دکتر توحيدخواه

  29. Example 4.7. Consider the system: compute the solution using the long prediction horizon with exponential data weight Compare the relative errors of the diagonal elements between Riccati solution P∞ and the matrix Pdmpcwhen N1 = N2 = 6 and N1 = N2 = 8 کنترل پيشبين-دکتر توحيدخواه

  30. Solution P MATLAB dlqr کنترل پيشبين-دکتر توحيدخواه

  31. کنترل پيشبين-دکتر توحيدخواه

  32. Therefore, with an increasing N, the relative errors between the diagonal elements of these two matrices are reduced. Furthermore, by choosing x(ki) being a vector containing unity elements, the minimum of the cost function is evaluated. For LQR, Jmin = 11.0811, and for the discrete-time MPC when a1 = a2 = 0.0, N1 = N2 = 8, Jmin = 11.0812. کنترل پيشبين-دکتر توحيدخواه

  33. Case B. Relatively small N is used 1- Smaller N not converge to the optimal control trajectory defined by (Q,R). 2- N sufficiently large as the global optimum. Smaller N could be termed a truncated approximation to the global optimum. There is only one global optimal solution once Q and R are selected. However, there are many approximations to the optimal solutions depending on the selection of the parameters a and N in the Laguerre functions. کنترل پيشبين-دکتر توحيدخواه

  34. 3- They provide the user with the means to select the closed-loop performance that might be desirable in a specific application. More explicitly, once Q and R are selected, the parameters a and N are used as fine-tuning parameters for the closed-loop performance. This is particularly useful when dealing with a complex system, where the variations of a and N are selected for each input independently to find the desired closed-loop performance. کنترل پيشبين-دکتر توحيدخواه

  35. An approximation to the global optimal solution could also be interpreted as a global optimal solution on its own for a pair of weight matrices ˜Q and ˜R which are unknown, also different from the original Q and R. For known a and N, cost function is: کنترل پيشبين-دکتر توحيدخواه

  36. With restricted a and N, Jmin is different from the global minimum, and the optimal control is different from the LQR optimal control defined by the pair (Q,R). Therefore, for a restricted pair of a and N parameters, there is a pair of unknown weight matrices Q and R (˜Q and ˜R) defining a different cost function. We find out what they are: کنترل پيشبين-دکتر توحيدخواه

  37. With the original choice of Q and R, and a, N parameters, the Riccati solution Pdmpc is calculated as: کنترل پيشبين-دکتر توحيدخواه

  38. This means that by choosing a restricted pair of a and N parameters, the predictive control system is equivalent to a discrete-time LQR system with a pair of weight matrices ˜Q and ˜R. For a small N, the closed-loop predictive control system is stable if Pdmpcis positive definite and ˜Q is non-negative definite. کنترل پيشبين-دکتر توحيدخواه

  39. If Pdmpc is equal to P∞ from the original cost function using LQR design, then ˜Q = Q, thus there is no change in the cost function. However, if Pdmpc differs from P∞, then equivalently a different LQR problem is solved using the predictive control framework, with the pair ˜Q and ˜R . Additionally, the prescribed degree of stability λ can be effectively enforced with an arbitrary pair of a and N, without ˜Q entering the computation and its existence is for theoretical justification and for understanding the essence of the problem in relation to the existing discrete-time LQR design. کنترل پيشبين-دکتر توحيدخواه

  40. Example 4.8. Choosing Q = CTC, and R = 0.1, α = 1.2 as the design parameters, show the variation of closed-loop performance by varying the Laguerre pole a for 0 ≤ a ≤ 0.9 where the parameter N = 1 is fixed. The prediction horizon Np = 46 is selected for the computation کنترل پيشبين-دکتر توحيدخواه

  41. Solution A unit step response test is used with zero initial condition of the state variables α = 0, 0.3, 0.6, 0.9 N = 1, Compare the closed-loop control results with the results obtained using DLQR کنترل پيشبين-دکتر توحيدخواه

  42. Tuning of predictive control system (N = 1, varying a). Key: line (1) DLQR control; line (2) α = 0; line (3) α = 0.3; line (4) α = 0.6; line (5) α = 0.9 کنترل پيشبين-دکتر توحيدخواه

  43. Closed loop predictive control system is stable for the range of a used in the design. Furthermore, the optimal DLQR system offers the fastest rise time and slight over-shoot. For this particular system, as α increases, the closed-loop response speed of the predictive control system reduces. There is a performance trend dependent on the variation of α. کنترل پيشبين-دکتر توحيدخواه

  44. Tuning Procedure Once More کنترل پيشبين-دکتر توحيدخواه

  45. With exponential data weighting, the closed-loop performance parameters are very similar to the DLQR performance parameters. Basically, the choice of weight matrices Q and R determines the closed-loop performance. The prediction horizon Np no longer plays a role in the design, because α large Np is used to approximate an infinite prediction horizon. For a choice of large N, with any 0 ≤ a < 1, the trajectory of the future control trajectory converges to the underlying optimal control trajectory defined by the corresponding LQR control law. کنترل پيشبين-دکتر توحيدخواه

  46. The prescribed degree of stability, λ, is a very important parameter in the specification of closed-loop performance. Tuning procedure: First, the weight matrix Q is always important for the closed-loop performance and often selected as: Q = CTC With this choice, the closed-loop eigenvalues are determined by the weight matrix R. کنترل پيشبين-دکتر توحيدخواه

  47. For SISO and R = rw, the closed-loop eigen values are the inside-the-unit-circle zeros of the equation: For MIMO and Q = CTC, and R = rwI, the closed-loop eigen values are the inside-the-unit-circle zeros of the equation: کنترل پيشبين-دکتر توحيدخواه

  48. Varying the scalar rw set closed-loop eigenvalues A more general case: Q = CTQyC, where Qy > 0, R > 0 are the diagonal weight matrices. The smaller elements in R corresponds to faster closed-loop response speed. کنترل پيشبين-دکتر توحيدخواه

  49. Next, the exponential weight factor α needs to be specified. Use of the exponential weight will avoid the numerical ill-conditioning problem for the class of MPC systems that have embeddedintegrators. If the plant is stable, any α > 1 will serve this purpose. A modest α is recommended when dealing with constraints (e.g., α = 1.1 is sufficient for the class of stable plants or plants with integrators). کنترل پيشبين-دکتر توحيدخواه

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