Robust Multi-objective Iterative Learning Control 13553323.ppt
- 1. Robust Multi-objective Iterative Learning Control Tong Duy Son Promoters Prof. Jan Swevers Prof. Goele Pipeleers September 2016
- 2. 1st try
- 3. 2nd try
- 4. 3rd try
- 5. 1. perform 2. analyze 3. do again (and better) 3rd try
- 6. 6 Repetitions 1. perform 2. analyze 3. do again (and better)
- 7. 7 Repetitions in industry vehicle testing batch processes industrial robots semiconductor
- 8. 8 Repetitions in industry vehicle testing batch processes industrial robots semiconductor control system
- 9. 9 Challenges Goal of this thesis • Improve the control system • Exploit ‘repetition’ Improvements in • Robustness • Precision • Fast • Energy efficiency
- 10. 10 Desired output: pick an object from A to B Question: What input should you apply? input desired output Consider a robot arm in a factory Sketch of Control
- 11. 11 Desired output: pick an object from A to B Question: What input should you apply? input desired output knowledge of the system! Sketch of Control 𝑦= 𝑓 (𝑥 ,𝑢)
- 12. 12 Sketch of Control Feedback Controller 𝑦= 𝑓 (𝑥 ,𝑢) Feedforward Controller Feedback Feedforward
- 13. 13 Sketch of Control • Model uncertainty • Disturbance • Transient response, lag • Non-minimum phase • Highly dependent on accuracy of the model • Disturbance has to be known Feedback Feedforward
- 14. 14 Sketch of Control • Model uncertainty • Disturbance • Transient response, lag • Non-minimum phase • Highly dependent on accuracy of the model • Disturbance has to be known Feedback Feedforward feedforward feedback no control feedforward feedback no control
- 15. 15 Sketch of Control • Model uncertainty • Disturbance • Transient response, lag • Non-minimum phase • Highly dependent on accuracy of the model • Disturbance has to be known Feedback Feedforward feedforward feedback no control
- 16. 16 Repetitions in industry same task and same performance hundreds to thousands times a day
- 17. 17 Revisit: Challenges Goal of this thesis • Improve the control system • Exploit ‘repetition’ Iterative learning control (ILC): improves control performance by incorporating information from previous trials
- 18. 18 Iterative Learning Control (ILC) Iterative learning control (ILC): improves control performance by incorporating information from previous trials )
- 19. 19 Main contributions 1. Multi-objective frequency domain ILC 2. Lifted system ILC: analysis and synthesis 3. Robust norm-optimal ILC
- 20. 20 Main contributions 1. Multi-objective frequency domain ILC 2. Lifted system ILC: analysis and synthesis 3. Robust norm-optimal ILC
- 21. 21 Introduction (1) Most ILC designs reply on a two-step sequential problem formulation and the design procedures are usually heuristic: • Design L then design Q: 1. L as model-inversion or phase-lead type 2. Q as a low-pass filter: depends on designed • Design Q then design L: 1. design Q 2. find L that optimize the learning speed • Iterate the previous 2 designs The design is not optimal while costly and time consuming! ) designed controller: (Q,L)
- 22. 22 Introduction (2) Hard to incorporate multi-objective intuitively • Robustness (unmodeled dynamics, uncertain parameter…) 1. Robustness vs tracking performance 2. Unknown: robustness and tracking performance vs learning speed have 1 month training have 4 year training (i.e. for Olympic): more difficult attempts • Input constraints • Trade-offs between the objectives
- 23. 23 Methodology • Design Q, L simultaneously using optimization • Accounts for the trade-off designs Approach: First, specify the desired performance, input constraints, and robustness conditions. Next, design ILC controller (Q, L) to optimize the convergence (learning) speed with the given specifications minimize convergence speed Q,L subject to robust performance robust convergence input constraints
- 24. 24 Methodology • Design Q, L simultaneously using optimization • Accounts for the trade-off designs Approach: First, specify the desired performance, input constraints, and robustness conditions. Next, design ILC controller (Q, L) to optimize the convergence (learning) speed with the given specifications minimize convergence speed Q,L subject to robust performance robust convergence input constraints non-convex, hard to solve!
- 25. 25 Methodology • Design Q, L simultaneously using optimization • Accounts for the trade-off designs Approach: First, specify the desired performance, input constraints, and robustness conditions. Next, design ILC controller (Q, L) to optimize the convergence (learning) speed with the given specifications minimize convergence speed Q,L subject to robust performance robust convergence input constraints non-convex, hard to solve! reformulated as a linear program
- 27. Advantages (1) Multi-objective and their trade-offs: convergence speed input constraints robust convergence robust performance optimality computation flexibility intuition multi-objective
- 28. 28 Advantages (2) Optimality • no 2-step and heuristic design • (Q, L) is simultaneously generated using optimization • noncausal ILC controller optimality computation flexibility intuition multi-objective • reliable as a result of a linear program Computation
- 29. 29 Advantages (4) Flexibility • controller type: FIR, IIR, PID... • different objectives: minimize tracking performance Q,L subject to convergence speed robust convergence input constraints • no parametric model is required, only FRFs • continuous and discrete • selecting interested frequencies: i.e. for noise and disturbance rejection. optimality computation flexibility intuition multi-objective
- 30. 30 Advantages (5) Intuition • Use conventional control system terminologies: sensitivity function, bandwidth optimality computation flexibility intuition multi-objective
- 31. 31 Advantages (5) Intuition • Use conventional control system terminologies: sensitivity function, bandwidth optimality computation flexibility intuition multi-objective • Automated design possible Multi- objective ILC algorithm system model (FRFs) performance specs. (bandwidth) ILC controller (and learning speed)
- 32. 32 Validation • Validate the proposed ILC designs: simulations and experiments • Validate the multi-objective trade-offs • Compare with existing designs Control Development Simulation & Experimental Validation
- 33. 33 Validation Control Development Simulation & Experimental Validation tracking performance function (sensitivity function) convergence (learning) speed function
- 34. 34 Validation Control Development Simulation & Experimental Validation convergence speed vs tracking performance with 2 different designs convergence speed vs input constraints red: no constraint
- 35. Validation: trade-off designs Control Development Simulation & Experimental Validation 35 Select the desired controller: desired tracking performance desired learning speed level of uncertainty
- 36. 36 Main contributions 1. Multi-objective frequency domain ILC 2. Lifted system ILC: analysis and synthesis 3. Robust norm-optimal ILC
- 37. 37 Introduction • Consider multiple objectives as previous, but investigate time domain using lifted system representation of finite trial length. • (Q,L) are matrix variables • Study robust analyses • Proposes ILC syntheses (designs) ) designed controller: (Q,L)
- 38. 38 Robustness • Robust monotonic convergence and robust performance analyses an LMI (or BMI) problem i.e. • Both unstructured and structured uncertainty are considered
- 39. 39 Synthesis (for short/moderate trial lengths) • Synthesis I: Optimize convergence speed • Synthesis II: Optimize tracking error ) designed controller: (Q,L) minimize convergence speed L subject to an LMI problem minimize tracking error Q subject to an LMI problem
- 40. 40 Validation Control Development Simulation & Experimental Validation XY-wafer stage with linear motor
- 41. 41 Main contributions 1. Multi-objective frequency domain ILC 2. Lifted system ILC: analysis and synthesis 3. Robust norm-optimal ILC
- 42. 42 Introduction Norm-optimal ILC is an efficient way to design the optimal ILC input: is the cost function w.r.t the nominal model (no uncertainty model is accounted) analytical solution (noncausal, time-varying controller) × has to sacrifice a lot tracking performance to obtain robustness 𝐽 (𝑢 𝑗 +1 ❑ ) minimize 𝑢𝑗+1 ❑
- 43. 43 Methodology • obtain both robustness and high tracking performance • deal with input constraints • efficient computation Approach: optimize the worst-case cost function: 𝐽 (𝑢 𝑗 +1 ❑ , ∆) minimize sup 𝑢𝑗+1 ❑ ∆∈ ℬ∆ subject to input constraints 𝐽 ( ∆ ) ∆ 𝐽 wc 𝐽 nom
- 44. 44 Methodology • obtain both robustness and high tracking performance • deal with input constraints • efficient computation Approach: optimize the worst-case cost function: 𝐽 (𝑢 𝑗 +1 ❑ , ∆) minimize sup 𝑢𝑗+1 ❑ ∆∈ ℬ∆ subject to input constraints non-convex problem 𝐽 ( ∆ ) ∆ 𝐽 wc 𝐽 nom
- 45. 45 Methodology • obtain both robustness and high tracking performance • deal with input constraints • efficient computation Approach: optimize the worst-case cost function: 𝐽 (𝑢 𝑗 +1 ❑ , ∆) minimize sup 𝑢𝑗+1 ❑ ∆∈ ℬ∆ subject to input constraints 𝐽dual (𝑢 𝑗 +1 ❑ , 𝛾 𝑗 +1 ❑ ) minimize , subject to input constraints non-convex problem reformulated as a convex problem
- 46. 46 Advantages obtain robustness w.r.t. cost function (proved): deal with input constraints efficient computation high tracking performance? 𝐽 ( ∆ ) ∆ 𝐽 wc 𝐽 nom
- 47. 47 Advantages obtain robustness w.r.t. cost function (proved): high tracking performance? Considering the same cost function: • if the classical norm-optimal ILC diverges, the proposed robust ILC still converges. • if the classical norm-optimal ILC converges, the robust ILC also converges to similar tracking performance but with lower convergence speed
- 48. 48 Advantages (cont.) deal with input constraints efficient computation the selection of weight matrices is not critical as other norm- optimal ILC designs. the proof of the equivalence to an adaptive norm-optimal ILC (trial- varying controller) can be used to avoid solving optimization if needed (i.e. when convergence is already obtained).
- 49. 49 Validation Control Development Simulation & Experimental Validation • Validate the proposed ILC designs: simulations and experiments • Compare with classical (robust and non-robust) norm-optimal ILC: accurate model, inaccurate model
- 50. 50 Validation Control Development Simulation & Experimental Validation • Validate the proposed ILC designs: simulations and experiments • Compare with classical (robust and non-robust) norm-optimal ILC: accurate model, inaccurate model accurate model inaccurate model red: classical norm- optimal ILC blue: proposed ILC black: other robust design
- 51. 51 Validation Control Development Simulation & Experimental Validation • Validate the proposed ILC designs: simulations and experiments • Compare with classical (robust and non-robust) norm-optimal ILC: accurate model, inaccurate model accurate model
- 52. 52 Main contributions 1. Multi-objective frequency domain ILC 2. Lifted system ILC: analysis and synthesis 3. Robust norm-optimal ILC
- 53. 53 Summary 1. Robust ILC: robustness and high tracking performance, frequency and time domain 2. Multiple objectives and their trade-offs 3. Efficient computation 4. Extensive simulation and experimental validations: guideline to select the suitable controller
- 54. 54 Future works 1. Multivariable (MIMO) systems 2. Different classes of uncertainty modelling 3. Robust ILC nonlinear optimization 4. ILC for different purposes: energy optimal, time-optimal… 5. Applications (human in the loop, distributed systems…)
- 55. 55 Thank you! More detailed information: https://tongduyson.github.io/publication.html
- 56. 56 Conservative: small Evaluate the original constraints: and using both simulation and experiments for different system models) The differences are small hence small conservative. page 69 (thesis)
- 57. 57 (near) Future works Multivariable (MIMO) systems performance condition:
- 58. 58 (near) Future works 2-order controllers generated from the optimization problem:
Editor's Notes
- #27: aa