PMA Production Engineering, Machine Design and Automation

1. K.U.Leuven Department of Mechanical Engineering Celestijnenlaan 300 B, B-3001 Leuven, BelgiumTel: +32 16 32 24 80 Fax: +32 16 32 29 87 www.mech.kuleuven.ac.be/pma PMA Production Engineering, Machine Design and Automation

2. Overview of presentation • General information • Facts and figures • Research areas • Spin offs and Technology Centre • Scores related research • Mechatronics • Noise and Vibration • Robotics and Intelligent Machines • Research Infrastructure

3. General information : Facts and Figures • Staff : 130 • Research projects funded by • university: 30% • governments (European + federal + regional): 36% • industry: 30 % • foreign fellowships: 4% • Students : • Engineering degree : 80 per year • Ph.D. : 8 per year

4. General information : Facts and Figures • Academic staff (professors) • F. Al-Bender • H. Bruyninkcx • J. De Schutter • W. Desmet • J. Duflou • W. Heylen • J.P. Kruth • B. Lauwers • D. Reynaerts • P. Sas • J. Swevers • H. Van Brussel • D. Vandepitte

5. Spin offs Name Establ. Area LMS International 1979 Dynamic analysis Data Analysis 1988 Monitoring for Products maintenance Krypton Electronic 1989 Measuring & quality Engineering control systems Materialise 1990 Rapid prototyping products and software Metris 1995 Reverse engineering & quality control Optidrive 1997 Optimization of drive systems

6. FMTC: Flanders’ Mechatronics Technology Centre • New research centre, operating since October 2003 • Initiative of Agoria, the Belgian multi-sector federation for the technology industry, and 14 leading mechatronic companies in Flanders • Supported by the Flemisch government via the IWT • Mission: to establish the bridge between the academic and industrial know-how in mechatronics • The centre executes industry driven mid-term and long-term research projects in: • Machine diagnosis • Modular Machines • Super Performance Machines • Close co-operation with PMA in project research and PhD research • Contact: Prof. Hendrik Van Brussel

7. General Information: Research Areas M P machines & products production machines production processes noise & vibration engineering mechatronics intelligent machines & robotics intelligent manufacturing systems A

8. General Information: Research Areas • Production processes • Computer integrated manufacturing • Dimensional metrology and quality control • Design of light weight structures • Micro- and precision engineering • Automotive engineering • Space technology • Design of mechatronic systems • Noise and vibration engineering • Robots and intelligent machines

9. General Information: Research Areas • Types of research: Balance between: • Long-term fundamental research • Short-term or applied research • Linked to industrial needs: • Fundamental research in co-operation with industry • Solving specific problems • Experimental validation of developed techniques

10. Scores (most) related research Topics • Design of mechatronic systems • Noise and vibration engineering • Robots and intelligent machines { { • No strict separation of research topics • Co-operation between different research groups

11. Scores (most) related research (cont’d) • Robots and intelligent machines: task planning, active sensing, sensor based environment modeling and task execution • Dynamic balancing of high speed machines • Modeling, identification and analysis of (non-linear) dynamic systems • Vibro-acoustic modeling and prediction • Active control

12. Robots and intelligent machines • Non-Minimal State Kalman Filter (NMSKF) for nonlinear systems • exact Bayesian estimation: pdf state x, given measurements Zk • and are updated with a KF algorithm • static systems with additive Gaussian measurement uncertainty • limited group of dynamic systems

13. Robots and intelligent machines • Application of the NSMKF for sensor based geometric model building • example: force controlled manipulation • measurements: contact force (6D) and motion (6D) • task: build geometric model of the environment using measurements • starting from scratch: large initial uncertainties on estimated states • use primitive contacts (e.g. vertex/face contact)  nonlinear equations • reduce number of parameters using statistical hypothesis testinge.g.: two vertex/face contacts reduce to one edge/face contact • example: putting a cube in a corner

14. Robots and intelligent machines 1 vertex/face 2 vertex/face

15. Robots and intelligent machines • Task specification as a constrained optimization problem • multiple motion tasks x(t) are defined; task jacobian J • over- or underconstrained task specification • task weighting with e.g. system inertia • implementation using torque control or joint velocity control • example: automatic generation of a ‘natural’ underconstrained human motion

16. Robots and intelligent machines • Task planning for ‘active sensing’: a constrained optimization problem • generate robot trajectory such that sensor information collected between start and goal position yields the most accurate estimates of the parameters of the world model • in force controlled manipulation: ‘hybrid’ optimization • generate sequence of discrete ‘contact formations’ (e.g. edge/vertex) • generate continuous motion within a contact formation

17. Dynamic balancing of reciprocating machinery • Input torque balancing to reduce drive speed variations: • novel mechanism: cam based ‘centrifugal pendulum (CBCP) • optimized, designed, and implemented • functions correctly, yields significant enhancement of dynamic behavior • Counterweight balancing of linkages: • reduce shaking forces and moments exerted on the supporting frame • reformulated as a convex optimisation problem • computationally efficient, global optimum

18. Dynamic balancing of reciprocating machinery

19. Modeling, identification and analysis of dynamic systems • Fundamental • Development of on- and off-line parameter estimation techniques • Modeling and analysis of local non-linear system dynamics, e.g. friction • Applied • Analysis of road noise transmission in vehicles • Non-destructive material identification using mixed numerical-experimental identification techniques, e.g. for laminates • Model based friction compensation • Experimental robot identification

20. Development of dynamic friction models and friction compensation techniques • Physics based friction model • Stochastic model based on asperity interaction scenario combined with phenomenological mechanisms: creep deformation, adhesion • Able to describe all observed types of friction behavior

21. Development of dynamic friction models and friction compensation techniques • Generalized Maxwell-slip model for friction compensation

22. Development of dynamic friction models and friction compensation techniques • Detailed experimental analysis of friction: tribometer • Friction compensation: combination of • Model based feedforward compensation • Disturbance observers

23. Vibro-acoustic modeling and prediction • Fundamental: Mid-frequency modeling: • Wave based models • Fuzzy finite element models • Hybrid tools for aero-acoustic modeling • Applied • Validation of vibro-acoustic modeling techniques • Models for sound propagation in subsonic confined flows, e.g. automotive mufflers • Modeling of the drive train dynamics of a wind turbine • Validation of the FE & BE methods in vibro acoustic modeling and analysis

24. Active control • Fundamental • Development of robust controllers for non-linear systems based on approximate model structures • Optimal decoupling for improved MIMO control design • Modeling and control of Linear Parameter Varying systems • Applied • Active control of exhaust noise of combustion engine • Design of lightweight inertial actuators with integrated velocity sensor for active vibration control of a thin panel • Active noise and vibration control for machining systems • Active and semi-active suspension systems for passenger cars • Anti-sway control for the load of a tower crane • Model-based and robust servo-control for high-performance drive systems

25. Optimal decoupling for improved MIMO control design • MIMO identification and control design: cumbersome • Decentralised control neglecting coupling: limited performance • Combine decentralised control with I/O decoupling: • optimised static decoupling • dynamic transformation filter (inverse based control) • Validation: Time Waveform Replication (TWR):

26. Optimal decoupling for improved MIMO control design Application: Time Waveform Replication GOAL :Replicate loads actingon vehicle (component)

27. Optimal decoupling for improved MIMO control design

28. Optimal decoupling for improved MIMO control design

29. Optimal decoupling for improved MIMO control design

30. Control of active and semi-active suspension systems for passenger cars • IWT-project with Tenneco/Monroe • Development of robust controller for active suspension of quarter car • linear model • uncertainty modeling, • H-infinity design

31. Control of active and semi-active suspension systems for passenger cars

32. Control of active and semi-active suspension systems for passenger cars

33. Control of active and semi-active suspension systems for passenger cars Some measured FRF’s and fitted models

34. Control of active and semi-active suspension systems for passenger cars Estimated uncertainty

35. Control of active and semi-active suspension systems for passenger cars Results: PDF of body acceleration and tire force, for: passive suspension (blue), constant settings of active suspension (red), and robustly controlled active suspension (green)

36. Control of active and semi-active suspension systems for passenger cars Robust Control No Control

37. Anti-sway control of the load of a tower crane • IWT project with ARCOMET NV

38. Anti-sway control of the load of a tower crane • Results

39. Anti-sway control of the load of a tower crane Without control With Control

40. Research Infrastructure • Team Cube : high-performance 6-DOF shaker table • Multi-channel Measurement, data-acquisition and analysis equipment • Multi-channel DSP-based control systems • Krypton K600 : 6-DOF position/orientation measurement system • Robotics laboratory equipped with 5 industrial robots, 2 mobile learning robots and semi-autonomous wheel chairs • Several high-performance drive-systems based on linear motor technology • Semi-anechoic measurement room • Other experimental test setups, e.g. tribometer, weaving machine a blank, etc ...

41. Research Infrastructure: Team Cube

42. Research Infrastructure: Team Cube • 3 actuator pairs • 82 kN/pair • (1+2)/2 = Z / (1-2)/2 = Pitch • (3+4)/2 = X / (3-4)/2 = Yaw • (5+6)/2 = Y / (5-6)/2 = Roll

43. Research Infrastructure: Team Cube Show film

44. Research Infrastructure: Robot lab

45. Research Infrastructure: linear motor based machines

46. Conclusion • Research at PMA most related to Systems, Control and Optimization: • Robots and intelligent machines • Noise and vibration engineering • Design of mechatronic systems • Characteristics: • Many applications of existing methodologies • Development of new approach in view of applications • Validation by experimental work • Valorisation in industrial projects